Search results for: dynamic network analysis

Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Ali M. Babalghaith, Hamad A. Alsoliman, Abdulrahman S. Al-Suhaibani

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Flexible pavement made with neat asphalt binder is not enough to resist heavy traffic loads as well as harsh environmental condition found in Riyadh region. Therefore, there is a need to modify asphalt binder with polymers to satisfy such conditions. There are several types of polymers that are used to modify asphalt binder. The objective of this paper is to compare the rheological properties of six polymer modified asphalt binders (Lucolast7010, Anglomak2144, Paveflex140, SBS KTR401, EE-2 and Crumb rubber) obtained from asphalt manufacturer plants. The rheological properties of polymer modified asphalt binders were tested using conventional tests such as penetration, softening point and viscosity; and SHRP tests such as dynamic shear rheometer and bending beam rheometer. The results have indicated that the polymer modified asphalt binders have lower penetration and higher softening point than neat asphalt indicating an improvement in stiffness of asphalt binder, and as a result, more resistant to rutting. Moreover, the dynamic shear rheometer results have shown that all modifiers used in this study improved the binder properties and satisfied the Superpave specifications except SBS KTR401 which failed to satisfy the rutting parameter (G*/sinδ).

Keywords: polymer modified asphalt, rheological properties, SBS, crumb rubber, EE-2

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Bandreddy Anand Babu, Srinivasa Rao Mandadi, C. Pradeep Reddy, N. Ramesh Babu

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In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming.

Keywords: ARIMA, ANFIS, fuzzy surmising tool stash, weather forecasting, MATLAB

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Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

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Authors: Ghobad Gorji, Hasan Golabi

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The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is directly generated into the lower band of the UWB spectrum, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying (DCSK), were studied before, and their performance was evaluated. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: UWB, DCC, IEEE 802.15.4a, COOK, DCSK

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Authors: Po-Ting Chen, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

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Classical molecular dynamics (CMD) simulations are highly efficient for material simulations but have limited accuracy. In contrast, ab initio molecular dynamics (AIMD) provides high precision by solving the Kohn–Sham equations yet requires significant computational resources, restricting the size of systems and time scales that can be simulated. To address these challenges, we employed NequIP, a machine learning model based on an E(3)-equivariant graph neural network, to accelerate molecular dynamics simulations of a 1M LiPF6 in EC/EMC (v/v 3:7) for Li battery applications. AIMD calculations were initially conducted using the Vienna Ab initio Simulation Package (VASP) to generate highly accurate atomic positions, forces, and energies. This data was then used to train the NequIP model, which efficiently learns from the provided data. NequIP achieved AIMD-level accuracy with significantly less training data. After training, NequIP was integrated into the LAMMPS software to enable molecular dynamics simulations of larger systems over longer time scales. This method overcomes the computational limitations of AIMD while improving the accuracy limitations of CMD, providing an efficient and precise computational framework. This study showcases NequIP’s applicability to electrolyte systems, particularly for simulating the dynamics of LiPF6 ionic mixtures. The results demonstrate substantial improvements in both computational efficiency and simulation accuracy, highlighting the potential of machine learning models to enhance molecular dynamics simulations.

Keywords: lithium-ion batteries, electrolyte simulation, molecular dynamics, neural network

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Authors: Yasemin Gülşen Yılmaz, Süleyman Hakan Yılmaz, Muhammet Erbay

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Social media is among the most important means of communication. Social media offers individuals and groups with an opportunity for participatory socialization over the internet, which is free of any time and place restrictions. Social media is a kind of interactive communication and bilateral social network. Various communication contents can be shared and put into mass circulation easily and quickly through social media. These sharings are not only limited to individuals but also happen to groups, institutions, and different constitutions. Their contents consist of any type of written message, audio and video files. We are living in the social media era now. It is not surprising that social media which has extensive communication facilities and massive prevalence is used in politics. Therefore, the use of social media (Facebook) by political parties during the Turkish general elections held on June 7, 2015, has been chosen as our research subject. Four parties namely, AKP, CHP, MHP and HDP who have the majority of votes in Turkey and participate in elections in Konya have been selected for our study. Their provincial centers’ and parliamentary candidates` use of social media (Facebook) on the last three days prior to the election have been examined and subjected to a qualitative analysis by means of content analysis.

Keywords: social media, June 7 general elections, politics, Facebook

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Dilusha Rajapakse

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The main reason for printing coloured imageries, pattern or motif onto textiles is to enhance the visual appearance of the surface so that the final textile product would get the required attention from potential customers. Such colours and patterns are permanently applied onto the textiles using conventional static colourants, and we expect such decorations to be last for the entire lifecycle of the textile product. The focus of this research presentation is to discuss the ability to integrate multicolour and pattern changing aesthetics onto textiles with the application of water based photochromic colourants. By adopting a research through design approach, a number of iterative flatbed screen printing experiments were conducted to explore the process of printing water based photochromic colours on textile surfaces. The research resulted in several technical parameters that have to be considered during the process of screen printing. Moreover, a modified printing technique that could be used to apply decorative photographic imagery onto textile with multicolour changing effects was also identified. A number of product applications for such dynamic printed textiles were revealed, and appropriate visual evidence was referred to justify the finding.

Keywords: dynamic aesthetics, multicolour changing textiles, non-emissive colours, printed textile design

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Authors: Harshadkumar Rajgor, Jeff Butterworth

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Background: Barretts oesophagus increases the risk of developing oesophageal adenocarcinoma. Over the last 40 years, there has been a 6 fold increase in the incidence of oesophageal adenocarcinoma in the western world and the incidence rates are increasing at a greater rate than cancers of the colon, breast and lung. Endoscopic mucosal resection (EMR) is a relatively new technique being used by 2 centres in the greater midlands cancer network. EMR can be used for curative or staging purposes, for high-grade dysplasia’s and T1 tumours of the oesophagus. EMR is also suitable for those who are deemed high risk for oesophagectomy. EMR has a recurrence rate of 21% according to the Wiesbaden data. Method: A retrospective study of prospectively collected data was carried out involving 24 patients who had EMR for curative or staging purposes. Complications of residual or recurrent disease following EMR that required further treatment were investigated. Results: In 54% of cases residual or recurrent disease was suspected. 96% of patients were given clear and concise information regarding their diagnosis of high-grade dysplasia or T1 tumours. All 24 patients consulted the same specialist healthcare team. Conclusion: EMR is a safe and effective treatment for patients who have high-grade dysplasia and T1NO tumours. In 54% of cases residual or recurrent disease was suspected. Initially, only single resections were undertaken. Multiple resections are now being carried out to reduce the risk of recurrence. Complications from EMR remain low in this series and consisted of a single episode of post procedural bleeding.

Keywords: endoscopic mucosal resection, oesophageal dysplasia, T1 tumours, cancer network

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Authors: Mohammadsadegh Zanganehfar

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Since the modernism, movement, and appearance of modern architecture, an aggressive desire for a general design theory in the theoretical works of architects in the form of books and essays emerges. Since Robert Venturi and Denise Scott Brown’s published complexity and contradiction in architecture in 1966, the discourse of complexity and volumetric composition has been an important and controversial issue in the discipline. Ever since various theories and essays were involved in this discourse, this paper attempt to identify chaos theory as a scientific model of complexity and its relation to architecture design theory by conducting a qualitative analysis and multidisciplinary critical approach through architecture and basic sciences resources. As a result, we identify chaotic architecture as the correlation of chaos theory and architecture as an independent nonlinear design theory with specific characteristics and properties.

Keywords: architecture complexity, chaos theory, fractals, nonlinear dynamic systems, nonlinear ontology

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Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Siddharth Ahuja, T. M. Muruganandam

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An analytical investigation is performed to study the unsteady response of a one-dimensional, non-linear dynamic shock system to external downstream pressure perturbations in a supersonic flow in a varying area duct. For a given pressure ratio across a wind tunnel, the normal shock's location can be computed as per one-dimensional steady gas dynamics. Similarly, for some other pressure ratio, the location of the normal shock will change accordingly, again computed using one-dimensional gas dynamics. This investigation focuses on the small-time interval between the first steady shock location and the new steady shock location (corresponding to different pressure ratios). In essence, this study aims to shed light on the motion of the shock from one steady location to another steady location. Further, this study aims to create the foundation of the Unsteady Gas Dynamics field enabling further insight in future research work. According to the new pressure ratio, a pressure pulse, generated at the exit of the tunnel which travels and perturbs the shock from its original position, setting it into motion. During such activity, other numerous physical phenomena also happen at the same time. However, three broad phenomena have been focused on, in this study - Traversal of a Wave, Fluid Element Interactions and Wave Interactions. The above mentioned three phenomena create, alter and kill numerous waves for different conditions. The waves which are created by the above-mentioned phenomena eventually interact with the shock and set it into motion. Numerous such interactions with the shock will slowly make it settle into its final position owing to the new pressure ratio across the duct, as estimated by one-dimensional gas dynamics. This analysis will be extremely helpful in the prediction of inlet 'unstart' of the flow in a supersonic engine intake and its prominence with the incoming flow Mach number, incoming flow pressure and the external perturbation pressure is also studied to help design more efficient supersonic intakes for engines like ramjets and scramjets.

Keywords: analytical investigation, compression and expansion waves, fluid element interactions, shock trajectory, supersonic flow, unsteady gas dynamics, varying area duct, wave interactions

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Authors: Rana I. Abdulghani, Amera I. Melhum

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With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

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Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, online user reviews, long tail theory, product categorization, social network analysis

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Authors: A. P. Mikhailovich, V. V. Fomin, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Woody vegetation among the upper limit of its habitat is a sensitive indicator of biota reaction to regional climate changes. Quantitative assessment of temporal and spatial changes in the distribution of trees and plant biocenoses calls for the development of new modeling approaches based upon selected data from measurements on the ground level and ultra-resolution aerial photography. Statistical models were developed for the study area located in the Polar Urals. These models allow obtaining probabilistic estimates for placing Siberian Larch trees into one of the three age intervals, namely 1-10, 11-40 and over 40 years, based on the Weilbull distribution of the maximum horizontal crown projection. Authors developed the distribution map for larch trees with crown diameters exceeding twenty centimeters by deciphering aerial photographs made by a UAV from an altitude equal to fifty meters. The total number of larches was equal to 88608, forming the following distribution row across the abovementioned intervals: 16980, 51740, and 19889 trees. The results demonstrate that two processes can be observed in the course of recent decades: first is the intensive forestation of previously barren or lightly wooded fragments of the study area located within the patches of wood, woodlands, and sparse stand, and second, expansion into mountain tundra. The current expansion of the Siberian Larch in the region replaced the depopulation process that occurred in the course of the Little Ice Age from the late 13ᵗʰ to the end of the 20ᵗʰ century. Using data from field measurements of Siberian larch specimen biometric parameters (including height, diameter at root collar and at 1.3 meters, and maximum projection of the crown in two orthogonal directions) and data on tree ages obtained at nine circular test sites, authors developed a model for artificial neural network including two layers with three and two neurons, respectively. The model allows quantitative assessment of a specimen's age based on height and maximum crone projection values. Tree height and crown diameters can be quantitatively assessed using data from aerial photographs and lidar scans. The resulting model can be used to assess the age of all Siberian larch trees. The proposed approach, after validation, can be applied to assessing the age of other tree species growing near the upper tree boundaries in other mountainous regions. This research was collaboratively funded by the Russian Ministry for Science and Education (project No. FEUG-2023-0002) and Russian Science Foundation (project No. 24-24-00235) in the field of data modeling on the basis of artificial neural network.

Keywords: treeline, dynamic, climate, modeling

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Diabetic nephropathy (DN) is a major complication of diabetes, with fibrosis and propionate metabolism playing critical roles in its progression. Identifying biomarkers linked to these pathways may provide novel insights into DN diagnosis and treatment. This study aims to identify biomarkers associated with fibrosis and propionate metabolism in DN. Analyze the biological pathways and regulatory mechanisms of these biomarkers. Develop a machine learning model to predict DN-related biomarkers and validate their functional roles. Publicly available transcriptome datasets related to DN (GSE96804 and GSE104948) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds), and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were identified. The analysis began with the extraction of DN-differentially expressed genes (DN-DEGs) and propionate metabolism-related DEGs (PM-DEGs), followed by the intersection of these with fibrosis-related genes to identify key intersected genes. Instead of relying on traditional models, we employed a combination of deep neural networks (DNNs) and ensemble methods such as Gradient Boosting Machines (GBM) and XGBoost to enhance feature selection and biomarker discovery. Recursive feature elimination (RFE) was coupled with these advanced algorithms to refine the selection of the most critical biomarkers. Functional validation was conducted using convolutional neural networks (CNN) for gene set enrichment and immunoinfiltration analysis, revealing seven significant biomarkers—SLC37A4, ACOX2, GPD1, ACE2, SLC9A3, AGT, and PLG. These biomarkers are involved in critical biological processes such as fatty acid metabolism and glomerular development, providing a mechanistic link to DN progression. Furthermore, a TF–miRNA–mRNA regulatory network was constructed using natural language processing models to identify 8 transcription factors and 60 miRNAs that regulate these biomarkers, while a drug–gene interaction network revealed potential therapeutic targets such as UROKINASE–PLG and ATENOLOL–AGT. This integrative approach, leveraging deep learning and ensemble models, not only enhances the accuracy of biomarker discovery but also offers new perspectives on DN diagnosis and treatment, specifically targeting fibrosis and propionate metabolism pathways.

Keywords: diabetic nephropathy, deep neural networks, gradient boosting machines (GBM), XGBoost

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Authors: Nastaran Cheshmehkaboodi, Lotfi Guizani, Noureddine Ghlamallah

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Seismic isolation proves to be a powerful technology in reducing seismic hazards and enhancing overall structural resilience. However, the performance of the technology can be influenced by various factors, including seismic inputs and soil conditions. This research aims to investigate the effects of moderate and strong earthquakes associated with different distances of the source on the seismic responses of conventional and isolated bridges, considering the soil-structure interaction effects. Two groups of moderate and strong near-fault records are applied to the conventional and isolated bridges, with and without considering the underlying soil. For this purpose, using the direct method, three soil properties representing rock, dense, and stiff soils are modeled in Abaqus software. Nonlinear time history analysis is carried out, and structural responses in terms of maximum deck acceleration, deck displacement, and isolation system displacement are studied. The comparison of dynamic responses between both earthquake groups demonstrates a consistent pattern, indicating that the bridge performance and the effects of soil-structure interaction are primarily influenced by the ground motions and their frequency contents. Low ratios of PGA/PGV are found to significantly impact all dynamic responses, resulting in higher force and displacement responses, regardless of the distance associated with the ruptured fault. In addition, displacement responses increase drastically on softer soils. Thus, meticulous consideration is crucial in designing isolation systems to avoid underestimating displacement demands and to ensure sufficient displacement capacity. Despite a lower PGA value in high seismicity areas in this study, the acceleration demand during strong earthquakes is up to 1.3 times higher in conventional bridges and up to 3 times higher in isolated bridges than in moderate earthquakes. Additionally, the displacement demand in strong earthquakes is up to 2 times higher in conventional bridges and up to 5 times higher in isolated bridges compared to moderate earthquakes, highlighting the increased force and displacement demand in strong earthquakes.

Keywords: bridges, seismic isolation, near-fault, earthquake characteristics, soil-structure interaction

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Authors: Mohammed Abbas Al-Jumaili

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There are some studies which have been conducted in resent years to investigate the possibility of producing high performance polymer concrete. However, despite the great important of this subject, very limited amount of literature is available about the strength and performance of this type of concrete in case using in rigid pavement highway. In this study, the possibility of producing high performance polymer concrete by using Styrene Butadiene Rubber (SBR) emulsion with various (SBR) percents of 5,10 ,15, and 20 % by weight of cement has been investigated. The compressive, splitting tensile and flexural strengths and dynamic modulus of elasticity tests were conducted after age of 7 and 28 days for control without polymer and SBR concretes. A total of (30) cubes, (30) cylinders and (30) prisms were prepared using different types of concrete mixes. The AASHTO guide-1993 method was used to determine slab concrete thickness of rigid pavement highway in case of using various SBR polymer concrete mixture types. The research results indicate that the use of 10% SBR by weight of cement leads to produce high performance concrete especially with regard to mechanical properties and structural relative to corresponding control concrete.

Keywords: rigid pavement highway, styrene–butadiene rubber (SBR) latex, compressive test, splitting tensile test, flexural test and dynamic modulus of elasticity test

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Authors: Chenlu Zhang, Wanni Zhang, Florian Schaule

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Indoor environment in office buildings directly affects comfort, productivity, health, and well-being of building occupants. Wireless environmental sensor networks have been deployed in many modern offices to monitor and control the indoor environments. However, indoor environmental variables are not strong enough predictors of comfort and productivity levels of every occupant due to personal differences, both physiologically and psychologically. This study proposes human-centric sensor networks that integrate wireless environmental sensors, body area network sensors and participatory sensing technologies to collect data from both environment and human and support building operations. The sensor networks have been tested in one small-size and one medium-size office rooms with 22 participants for five months. Indoor environmental data (e.g., air temperature and relative humidity), physiological data (e.g., skin temperature and Galvani skin response), and physiological responses (e.g., comfort and self-reported productivity levels) were obtained from each participant and his/her workplace. The data results show that: (1) participants have different physiological and physiological responses in the same environmental conditions; (2) physiological variables are more effective predictors of comfort and productivity levels than environmental variables. These results indicate that the human-centric sensor networks can support human-centric building control and improve comfort and productivity in offices.

Keywords: body area network, comfort and productivity, human-centric sensors, internet of things, participatory sensing

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Authors: Mai Abdul Latif, Yuntian Feng

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Applied Element Method (AEM) is a method that was developed to aid in the analysis of the collapse of structures. Current available methods cannot deal with structural collapse accurately; however, AEM can simulate the behavior of a structure from an initial state of no loading until collapse of the structure. The elements in AEM are connected with sets of normal and shear springs along the edges of the elements, that represent the stresses and strains of the element in that region. The elements are rigid, and the material properties are introduced through the spring stiffness. Nonlinear dynamic analysis has been widely modelled using the finite element method for analysis of progressive collapse of structures; however, difficulties in the analysis were found at the presence of excessively deformed elements with cracking or crushing, as well as having a high computational cost, and difficulties on choosing the appropriate material models for analysis. The Applied Element method is developed and coded to significantly improve the accuracy and also reduce the computational costs of the method. The scheme works for both linear elastic, and nonlinear cases, including elasto-plastic materials. This paper will focus on elastic and elasto-plastic material behaviour, where the number of springs required for an accurate analysis is tested. A steel cantilever beam is used as the structural element for the analysis. The first modification of the method is based on the Gaussian Quadrature to distribute the springs. Usually, the springs are equally distributed along the face of the element, but it was found that using Gaussian springs, only up to 2 springs were required for perfectly elastic cases, while with equal springs at least 5 springs were required. The method runs on a Newton-Raphson iteration scheme, and quadratic convergence was obtained. The second modification is based on adapting the number of springs required depending on the elasticity of the material. After the first Newton Raphson iteration, Von Mises stress conditions were used to calculate the stresses in the springs, and the springs are classified as elastic or plastic. Then transition springs, springs located exactly between the elastic and plastic region, are interpolated between regions to strictly identify the elastic and plastic regions in the cross section. Since a rectangular cross-section was analyzed, there were two plastic regions (top and bottom), and one elastic region (middle). The results of the present study show that elasto-plastic cases require only 2 springs for the elastic region, and 2 springs for the plastic region. This showed to improve the computational cost, reducing the minimum number of springs in elasto-plastic cases to only 6 springs. All the work is done using MATLAB and the results will be compared to models of structural elements using the finite element method in ANSYS.

Keywords: applied element method, elasto-plastic, Gaussian springs, nonlinear

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Authors: Chingwen Yeh, Chang Ning Hung

Abstract:

This paper aims to analysis the aesthetic qualities of Dancing Ink (2017) performed by Miao-Hsuan Dance Group in Taiwan. The premiere was performed at Taichung Seaport Art Center Exhibition Hall Room A, in Taichung Taiwan on March 11th, 2017. What calligraphic elements were applied into the contemporary dance choreography was examed by these authors. Although calligraphy and dance are two different art forms, one can see in Dancing Ink that the beauty of Yin Yang force circling; the flow of Chi from inner meditation to the outer physical action are resonated in both art forms. The aesthetic experience was documented through qualitative research methods. In-depth interviews were conducted within a focus group including the calligrapher, the choreographer, the participated professional dancers and the appreciators. Subject matter, movement qualities, characteristic and meaning are the four main areas of interpretation originated from Janet Adshead’s dance analytical theory. Based on all the research data collection and analysis, this research found: 1) Making invisible spirituality visible in relations to creative process, 2) An artistic symbol that transforms from two-dimensional into three-dimensional generates a new aesthetic value, 3) Choreography and calligraphy require different artistic skills and disciplines but expressed the similar dynamic and rhythm in their movement.

Keywords: Chi, Dancing Ink, Miao Hsuan Dance Group, Yin Yang force

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Authors: M. Wajahat, M. Shoaib, A. Waheed

Abstract:

As a result of increase in world energy demand as well as the demand for heating, refrigeration and air conditioning, energy engineers are now more inclined towards the renewable energy especially solar based thermal driven refrigeration and air conditioning systems. This research is emphasized on solar assisted adsorption refrigeration system to provide comfort conditions for a building in Islamabad. The adsorption chiller can be driven by low grade heat at low temperature range (50 -80 °C) which is lower than that required for generator in absorption refrigeration system which may be furnished with the help of common flat plate solar collectors (FPC). The aim is to offset the total energy required for building’s heating and cooling demand by using FPC’s thus reducing dependency on primary energy source hence saving energy. TRNSYS is a dynamic modeling and simulation tool which can be utilized to simulate the working of a complete solar based adsorption chiller to meet the desired cooling and heating demand during summer and winter seasons, respectively. Modeling and detailed parametric analysis of the whole system is to be carried out to determine the optimal system configuration keeping in view various design constraints. Main focus of the study is on solar thermal loop of the adsorption chiller to reduce the contribution from the auxiliary devices.

Keywords: flat plate collector, energy saving, solar assisted adsorption chiller, TRNSYS

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Authors: Jing Ma, Xindong An

Abstract:

Green infrastructure, the most important aspect of improving the quality of life, has been a crucial element of the liveability measurement. With demanding of more liveable urban environment from increasing population in city area, access to green infrastructure in walking distance should be taken into consideration. This article exemplifies the study on accessibility measurement of green infrastructure in central Auckland (New Zealand), using network analysis tool on the basis of GIS, to verify the accessibility levels of green infrastructure. It analyses the overall situation of green infrastructure and draws some conclusions on the city’s different levels of accessibility according to the categories and facilities distribution, which provides valuable references and guidance for the future facility improvement in planning strategies.

Keywords: quality of life, green infrastructure, GIS, accessibility

Procedia PDF Downloads 281

Authors: Hassan Saghi, Gholam Reza Askarzadeh Garmroud, Seyyed Ali Reza Emamian

Abstract:

Sloshing phenomenon is a complicated free surface flow problem that increases the dynamic pressure on the sidewalls and the bottom of the storage tanks. When the storage tanks are partially filled, it is essential to be able to evaluate the fluid dynamic loads on the tank’s perimeter. In this paper, a numerical code was developed to determine the pressure distribution on the rectangular and trapezoidal storage tanks’ perimeters due to liquid sloshing impact. Assuming the fluid to be inviscid, the Laplace equation and the nonlinear free surface boundary conditions are solved using coupled BEM-FEM. The code performance for sloshing modeling is validated against available data. Finally, this code is used for partially filled rectangular and trapezoidal storage tanks and the pressure distribution on the tanks’ perimeters due to liquid sloshing impact is estimated. The results show that the maximum pressure on the perimeter of the rectangular and trapezoidal storage tanks was decreased along the sidewalls from the top to the bottom. Furthermore, the period of the pressure distribution is different for different points on the tank’s perimeter and it is bigger in the trapezoidal tanks compared to the rectangular ones.

Keywords: pressure distribution, liquid sloshing impact, sway motion, trapezoidal storage tank, coupled BEM-FEM

Procedia PDF Downloads 550

Authors: Ilef Ketata, Mohamed Khalil Baazaoui, Robert Fromm, Ahmad Fakhfakh, Faouzi Derbel

Abstract:

The integration of wireless communication, e. g. in real-or quasi-real-time applications, is related to many challenges such as energy consumption, communication range, latency, quality of service, and reliability. To minimize the latency without increasing energy consumption, wake-up receiver (WuRx) nodes have been introduced in recent works. Low-noise amplifiers (LNAs) are introduced to improve the WuRx sensitivity but increase the supply current severely. Different WuRx approaches exist with always-on, power-gated, or duty-cycled receiver designs. This paper presents a comparative study for improving communication range and decreasing the energy consumption of wireless sensor nodes.

Keywords: wireless sensor network, wake-up receiver, duty-cycled, low-noise amplifier, envelope detector, range study

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Authors: Pejman Hosseiniun, Rose Shayeghi, Iman Rahbari, Mohamad Reza Kalhor

Abstract:

CNFET has emerged as an alternative material to silicon for high performance, high stability and low power SRAM design in recent years. SRAM functions as cache memory in computers and many portable devices. In this paper, a new SRAM cell design based on CNFET technology is proposed. The proposed SRAM cell design for CNFET is compared with SRAM cell designs implemented with the conventional CMOS and FinFET in terms of speed, power consumption, stability, and leakage current. The HSPICE simulation and analysis show that the dynamic power consumption of the proposed 8T CNFET SRAM cell’s is reduced about 48% and the SNM is widened up to 56% compared to the conventional CMOS SRAM structure at the expense of 2% leakage power and 3% write delay increase.

Keywords: SRAM cell, CNFET, low power, HSPICE

Procedia PDF Downloads 413