Search results for: gas distribution network

Authors: Dibakar Chakrabarty, Mebada Suiting

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Rapid urbanization coupled with changes in land use pattern results in increasing peak discharge and shortening of catchment time of concentration. The consequence is floods, which often inundate roads and inhabited areas of cities and towns. Management of storm water resulting from rainfall has, therefore, become an important issue for the municipal bodies. Proper management of storm water obviously includes adequate design of storm water drainage networks. The design of storm water network is a costly exercise. Least cost design of storm water networks assumes significance, particularly when the fund available is limited. Optimal design of a storm water system is a difficult task as it involves the design of various components, like, open or closed conduits, storage units, pumps etc. In this paper, a methodology for least cost design of storm water drainage systems is proposed. The methodology proposed in this study consists of coupling a storm water simulator with an optimization method. The simulator used in this study is EPA’s storm water management model (SWMM), which is linked with Genetic Algorithm (GA) optimization method. The model proposed here is a mixed integer nonlinear optimization formulation, which takes care of minimizing the sectional areas of the open conduits of storm water networks, while satisfactorily conveying the runoff resulting from rainfall to the network outlet. Performance evaluations of the developed model show that the proposed method can be used for cost effective design of open conduit based storm water networks.

Keywords: genetic algorithm (GA), optimal design, simulation-optimization, storm water network, SWMM

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Authors: Kamakshaiah Kolli, R. Seshadri

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Water is a key resource in all economic activities ranging from agriculture to industry. Only a tiny fraction of the planet's abundant water is available to us as fresh water. Assessment of water quality has always been paramount in the field of environmental quality management. It is the foundation for health, hygiene, progress and prosperity. With ever increasing pressure of human population, there is severe stress on water resources. Therefore efficient water management is essential to civil society for betterment of quality of life. The present study emphasizes on the groundwater quality, sources of ground water contamination, variation of groundwater quality and its spatial distribution. The bases for groundwater quality assessment are groundwater bodies and representative monitoring network enabling determination of chemical status of groundwater body. For this study, water samples were collected from various areas of the entire corporation area of Guntur. Water is required for all living organisms of which 1.7% is available as ground water. Water has no calories or any nutrients, but essential for various metabolic activities in our body. Chemical and physical parameters can be tested for identifying the portability of ground water. Electrical conductivity, pH, alkalinity, Total Alkalinity, TDS, Calcium, Magnesium, Sodium, Potassium, Chloride, and Sulphate of the ground water from Guntur district: Different areas of the District were analyzed. Our aim is to check, if the ground water from the above areas are potable or not. As multivariate are present, Data mining technique using JRIP rules was employed for classifying the ground water.

Keywords: groundwater, water quality standards, potability, data mining, JRIP, PCA, classification

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Authors: Yiqiong Yuan, Jun Sun, Dongmei Zhou, Jianan Sun

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In this paper, we presented a Multi-Objective Random Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based on RDPSO and crowding distance sorting to improve the convergence and distribution with less computation cost. MORDPSO-CD makes the most of RDPSO to approach the true Pareto optimal solutions fast. We adopt the crowding distance sorting technique to update and maintain the archived optimal solutions. Introducing the crowding distance technique into MORDPSO can make the leader particles find the true Pareto solution ultimately. The simulation results reveal that the proposed algorithm has better convergence and distribution

Keywords: multi-objective optimization, random drift particle swarm optimization, crowding distance sorting, pareto optimal solution

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Authors: Alaka Padhye, S. P. Kane

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The following study considers some variations made to the secretary problem (SP). In a multiple criteria secretary problem (MCSP), the selection of a unit is based on two independent characteristics. The units that appear before an observer are known say N, the best rank of a unit being N. A unit is selected, if it is better with respect to either first or second or both the characteristics. When the number of units is large and due to constraints like time and cost, the observer might want to stop earlier instead of inspecting all the available units. Let the process terminate at r2th unit where r1Keywords: joint distribution, marginal distribution, real ranks, secretary problem, selection criterion, two tailed secretary problem

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Authors: Radhia Toujani, Jalel Akaichi

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In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: social network, community detection, agglomerative hierarchical clustering, divisive hierarchical clustering, similarity, modularity, metaheuristic, bee colony

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Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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Authors: Mesut BALIBEY, Serpil TÜRKYILMAZ

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A popular topic in the econometrics and time series area is the cointegrating relationships among the components of a nonstationary time series. Engle and Granger’s least squares method and Johansen’s conditional maximum likelihood method are the most widely-used methods to determine the relationships among variables. Furthermore, a method proposed to test a unit root based on the periodogram ordinates has certain advantages over conventional tests. Periodograms can be calculated without any model specification and the exact distribution under the assumption of a unit root is obtained. For higher order processes the distribution remains the same asymptotically. In this study, in order to indicate advantages over conventional test of periodograms, we are going to examine a possible relationship between tourism and economic growth during the period 1999:01-2010:12 for Turkey by using periodogram method, Johansen’s conditional maximum likelihood method, Engle and Granger’s ordinary least square method.

Keywords: cointegration, economic growth, periodogram ordinate, tourism

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Authors: Najiba Ouled Omar, Azza Harbaoui, Henda Ben Ghezala

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Current research practices sentiment analysis with a focus on social networks, DEfi Fouille de Texte (DEFT) (Text Mining Challenge) evaluation campaign focuses on opinion mining and sentiment analysis on social networks, especially social network Twitter. It aims to confront the systems produced by several teams from public and private research laboratories. DEFT offers participants the opportunity to work on regularly renewed themes and proposes to work on opinion mining in several editions. The purpose of this article is to scrutinize and analyze the works relating to opinions mining and sentiment analysis in the Twitter social network realized by DEFT. It examines the tasks proposed by the organizers of the challenge and the methods used by the participants.

Keywords: opinion mining, sentiment analysis, emotion, polarity, annotation, OSEE, figurative language, DEFT, Twitter, Tweet

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Authors: Andrew Laghos

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The Internet is a big part of our everyday lives. People can now access the internet from a variety of places including home, college, and work. Many airports, hotels, restaurants and cafeterias, provide free wireless internet to their visitors. Using technologies like computers, tablets, and mobile phones, we spend a lot of our time online getting entertained, getting informed, and communicating with each other. This study deals with the latter part, namely, human communication through the Internet. People can communicate with each other using social media, social network sites (SNS), e-mail, messengers, chatrooms, and so on. By connecting with each other they form virtual communities. Regarding SNS, types of connections that can be studied include friendships and cliques. Analyzing these connections is important to help us understand online user behavior. The method of Social Network Analysis (SNA) was used on a case study, and results revealed the existence of some useful patterns of interactivity between the participants. The study ends with implications of the results and ideas for future research.

Keywords: human communication, internet communities, online user behavior, psychology

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Authors: Mujahid Muhammad, Paul Kearney, Adel Aneiba

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The ability for vehicles to communicate with other vehicles (V2V), the physical (V2I) and network (V2N) infrastructures, pedestrians (V2P), etc. – collectively known as V2X (Vehicle to Everything) – will enable a broad and growing set of applications and services within the intelligent transport domain for improving road safety, alleviate traffic congestion and support autonomous driving. The telecommunication research and industry communities and standardization bodies (notably 3GPP) has finally approved in Release 14, cellular communications connectivity to support V2X communication (known as LTE – V2X). LTE – V2X system will combine simultaneous connectivity across existing LTE network infrastructures via LTE-Uu interface and direct device-to-device (D2D) communications. In order for V2X services to function effectively, a robust security mechanism is needed to ensure legal and safe interaction among authenticated V2X entities in the LTE-based V2X architecture. The characteristics of vehicular networks, and the nature of most V2X applications, which involve human safety makes it significant to protect V2X messages from attacks that can result in catastrophically wrong decisions/actions include ones affecting road safety. Attack vectors include impersonation attacks, modification, masquerading, replay, MiM attacks, and Sybil attacks. In this paper, we focus our attention on LTE-based V2X security and access control mechanisms. The current LTE-A security framework provides its own access authentication scheme, the AKA protocol for mutual authentication and other essential cryptographic operations between UEs and the network. V2N systems can leverage this protocol to achieve mutual authentication between vehicles and the mobile core network. However, this protocol experiences technical challenges, such as high signaling overhead, lack of synchronization, handover delay and potential control plane signaling overloads, as well as privacy preservation issues, which cannot satisfy the adequate security requirements for majority of LTE-based V2X services. This paper examines these challenges and points to possible ways by which they can be addressed. One possible solution, is the implementation of the distributed peer-to-peer LTE security mechanism based on the Bitcoin/Namecoin framework, to allow for security operations with minimal overhead cost, which is desirable for V2X services. The proposed architecture can ensure fast, secure and robust V2X services under LTE network while meeting V2X security requirements.

Keywords: authentication, long term evolution, security, vehicle-to-everything

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Authors: K. AlJimaz, A. Abdullah, A. Abdulsalam, K. Ebdah, A. Abdalrasheed

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Understanding flow distribution and head losses is essential to design and calculate Thermo fluid parameters in order to reduce the pressure to a certain required pressure. This paper discusses the ways acquired in design and simulation to create and design an impedance box that reduces pressure. It's controlled by specific scientific principles such as Bernoulli’s principle and conservation of mass. In this paper, the design is made using SOLIDWORKS, and the simulation is done using ANSYS software to solve differential equations and study the parameters in the 3D model, also to understand how the design of this box reduced the pressure. The design was made so that fluid enters at a certain pressure of 3000 Pa in a single inlet; then, it exits from six outlets at a pressure of 300 Pa with respect to the conservation of mass principle. The effect of the distribution of flow and the head losses has been noticed that it has an impact on reducing the pressure since other factors, such as friction, were neglected and also the temperature, which was constant. The design showed that the increase in length and diameter of the pipe helped to reduce the pressure, and the head losses contributed significantly to reduce the pressure to 10% of the original pressure (from 3000 Pa to 300 Pa) at the outlets.

Keywords: box, pressure, thermodynamics, 3D

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Authors: Malika Kharouf, Aly Chkeir, Khac Tuan Huynh

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Detecting change points in time series data is a challenging problem, especially in scenarios where there is limited prior knowledge regarding the data’s distribution and the nature of the transitions. We present a method designed for detecting changes in the covariance structure of high-dimensional time series data, where the number of variables closely matches the data length. Our objective is to achieve unbiased test statistic estimation under the null hypothesis. We delve into the utilization of Random Matrix Theory to analyze the behavior of our test statistic within a high-dimensional context. Specifically, we illustrate that our test statistic converges pointwise to a normal distribution under the null hypothesis. To assess the effectiveness of our proposed approach, we conduct evaluations on a simulated dataset. Furthermore, we employ our method to examine changes aimed at detecting frailty in the elderly.

Keywords: change point detection, hypothesis tests, random matrix theory, frailty in elderly

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Authors: Chi Wu, Dean Wu, Wen-Te Liu, Cheng-Yu Tsai, Shin-Mei Hsu, Yin-Tzu Lin, Ru-Yin Yang

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Background: The results of previous studies compared the benefits and risks of receiving insomnia medication. However, the effects between hypnotic drugs used and enhancement of sleep quality were still unclear. Objective: The aim of this study is to establish a prediction model for hypnotic drugs' dosage used for insomnia subjects and associated the relationship between sleep stage ratio change and drug types. Methodologies: According to American Academy of Sleep Medicine (AASM) guideline, sleep stages were classified and transformed to hypnogram via the polysomnography (PSG) in a hospital in New Taipei City (Taiwan). The subjects with diagnosis for insomnia without receiving hypnotic drugs treatment were be set as the comparison group. Conversely, hypnotic drugs dosage within the past three months was obtained from the clinical registration for each subject. Furthermore, the collecting subjects were divided into two groups for training and testing. After training convolution neuron network (CNN) to predict types of hypnotics used and dosages are taken, the test group was used to evaluate the accuracy of classification. Results: We recruited 76 subjects in this study, who had been done PSG for transforming hypnogram from their sleep stages. The accuracy of dosages obtained from confusion matrix on the test group by CNN is 81.94%, and accuracy of hypnotic drug types used is 74.22%. Moreover, the subjects with high ratio of wake stage were correctly classified as requiring medical treatment. Conclusion: CNN with hypnogram was potentially used for adjusting the dosage of hypnotic drugs and providing subjects to pre-screening the types of hypnotic drugs taken.

Keywords: convolution neuron network, hypnotic drugs, insomnia, polysomnography

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Authors: Wei Zhang

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With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

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Authors: Dongwon Lee, Sunmyeng Kim

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Underwater acoustic networks (UANs) have become a very active research area in recent years. Compared with wireless networks, UANs are characterized by the limited bandwidth, long propagation delay and high channel dynamic in acoustic modems, which pose challenges to the design of medium access control (MAC) protocol. The characteristics severely affect network performance. In this paper, we study a MS-MAC (Multi-Sender MAC) protocol in order to improve network performance. The proposed protocol exploits temporal reuse by learning the propagation delays to neighboring nodes. A source node locally calculates the transmission schedules of its neighboring nodes and itself based on the propagation delays to avoid collisions. Performance evaluation is conducted using simulation, and confirms that the proposed protocol significantly outperforms the previous protocol in terms of throughput.

Keywords: acoustic channel, MAC, temporal reuse, UAN

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Authors: Ye Xue, Zhenhua Deng

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Objectives: Facial soft tissue thickness (FSTT) data could be obtained from CT scans by measuring the face-to-skull distances at sparsely distributed anatomical landmarks by manually located on face and skull. However, automated measurement using 3D facial and skull models by dense points using open-source software has become a viable option due to the development of computed assisted imaging technologies. By utilizing dense FSTT information, it becomes feasible to generate plausible automated facial approximations. Therefore, establishing a comprehensive and detailed, densely calculated FSTT database is crucial in enhancing the accuracy of facial approximation. Materials and methods: This study utilized head CT scans from 250 Chinese adults of Han ethnicity, with 170 participants originally born and residing in northern China and 80 participants in southern China. The age of the participants ranged from 14 to 82 years, and all samples were divided into five non-overlapping age groups. Additionally, samples were also divided into three categories based on BMI information. The 3D Slicer software was utilized to segment bone and soft tissue based on different Hounsfield Unit (HU) thresholds, and surface models of the face and skull were reconstructed for all samples from CT data. Following procedures were performed unsing MeshLab, including converting the face models into hollowed cropped surface models amd automatically measuring the Hausdorff Distance (referred to as FSTT) between the skull and face models. Hausdorff point clouds were colorized based on depth value and exported as PLY files. A histogram of the depth distributions could be view and subdivided into smaller increments. All PLY files were visualized of Hausdorff distance value of each vertex. Basic descriptive statistics (i.e., mean, maximum, minimum and standard deviation etc.) and distribution of FSTT were analysis considering the sex, age, BMI and birthplace. Statistical methods employed included Multiple Regression Analysis, ANOVA, principal component analysis (PCA). Results: The distribution of FSTT is mainly influenced by BMI and sex, as further supported by the results of the PCA analysis. Additionally, FSTT values exceeding 30mm were found to be more sensitive to sex. Birthplace-related differences were observed in regions such as the forehead, orbital, mandibular, and zygoma. Specifically, there are distribution variances in the depth range of 20-30mm, particularly in the mandibular region. Northern males exhibit thinner FSTT in the frontal region of the forehead compared to southern males, while females shows fewer distribution differences between the northern and southern, except for the zygoma region. The observed distribution variance in the orbital region could be attributed to differences in orbital size and shape. Discussion: This study provides a database of Chinese individuals distribution of FSTT and suggested opening source tool shows fine function for FSTT measurement. By incorporating birthplace as an influential factor in the distribution of FSTT, a greater level of detail can be achieved in facial approximation.

Keywords: forensic anthropology, forensic imaging, cranial facial reconstruction, facial soft tissue thickness, CT, open-source tool

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Suzannah Sharif, Aznie Aida Ahmad, Maznah Ismail

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Cocoa bean is the raw material for products such as cocoa powder and chocolate. Cocoa bean contains polyphenol which has been shown in several clinical studies to confer beneficial health effects. However studies showed that cocoa polyphenol absorption in the human intestinal tracts are very low. Therefore nanoemulsion may be one way to increase the bioavailability of cocoa polyphenol. This study aim to characterize nanoemulsion incorporating crude cocoa polyphenol produced using high energy technique. Cocoa polyphenol was extracted from fresh freeze-dried cocoa beans from Malaysia. The particle distribution, particle size, and zeta potential were determined. The emulsion was also analysed using transmission electron microscope to visualize the particles. Solubilization study was conducted by titrating the nanoemulsion into distilled water or 1% surfactant solution. Result showed that the nanoemulsion contains particle which have narrow size distribution. The particles size average at 112nm with zeta potential of -45mV. The nanoemulsions behave differently in distilled water and surfactant solution.

Keywords: cocoa, nanoemulsion, cocoa polyphenol, solubilisation study

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Authors: Sunil Sangwan, Narayan Chandra Nayak

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Over the last few decades, Indian banking system has achieved remarkable growth in its credit volume. However, one of the most disturbing facts about this growth is the uneven distribution of financial services across regions. Having witnessed limited success from all the earlier efforts towards financial inclusion targeting the rural poor and the underprivileged, provision of microfinance, of late, has emerged as a supplementary mechanism. There are two prominent modes of microfinance distribution in India namely Bank-SHG linkage (SBLP) and private Microfinance Institutions (MFIs). Ironically, such efforts also seem to have failed to achieve the desired targets as the microfinance services have witnessed skewed distribution across the states of the country. This study attempts to make a comparative analysis of the geographical skew of the SBLP and MFI in India and examine the factors influencing their regional distribution. The results indicate that microfinance services are largely concentrated in the southern region, accounting for about 50% of all microfinance clients and 49% of all microfinance loan portfolios. This is distantly followed by an eastern region where client outreach is close to 25% only. The north-eastern, northern, central, and western regions lag far behind in microfinance sectors, accounting for only 4%, 4%, 10%, and 7 % client outreach respectively. The penetration of SHGs is equally skewed, with the southern region accounting for 46% of client outreach and 70% of loan portfolios followed by an eastern region with 21% of client outreach and 13% of the loan portfolio. Contrarily, north-eastern, northern, central, western and eastern regions account for 5%, 5%, 10%, and 13% of client outreach and 3%, 3%, 7%, and 4% of loan portfolios respectively. The study examines the impact of literacy rate, rural poverty, population density, primary sector share, non-farm activities, loan default behavior and bank penetration on the microfinance penetration. The study is limited to 17 major states of the country over the period 2008-2014. The results of the GMM estimation indicate the significant positive impact of literacy rate, non-farm activities and population density on microfinance penetration across the states, while the rise in loan default seems to deter it. Rural poverty shows the significant negative impact on the spread of SBLP, while it has a positive impact on MFI penetration, hence indicating the policy of exclusion being adhered to by the formal financial system especially towards the poor. However, MFIs seem to be working as substitute mechanisms to banks to fill the gap. The findings of the study are a pointer towards enhancing financial literacy, non-farm activities, rural bank penetration and containing loan default for achieving greater microfinance prevalence.

Keywords: bank penetration, literacy rate, microfinance, primary sector share, rural non-farm activities, rural poverty

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Authors: W. Intagun, T. Khamdaeng, P. Prom-ngarm, N. Panyoyai

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Biochar has been used as a soil amendment since it has high porous structure and has proper nutrients and chemical properties for plants. Product yields produced from biochar kiln are dependent on process parameters and kiln types used. The objective of this research is to investigate the effect of core puncture diameter on biochar kiln efficiency, i.e., yields of biochar and produced gas. Corncobs were used as raw material to produce biochar. Briquettes from agricultural wastes were used as fuel. Each treatment was performed by changing the core puncture diameter. From the experiment, it is revealed that the yield of biochar at the core puncture diameter of 3.18 mm, 4.76 mm, and 6.35 mm was 10.62 wt. %, 24.12 wt. %, and 12.24 wt. %, of total solid yields, respectively. The yield of produced gas increased with increasing the core puncture diameter. The maximum percentage by weight of the yield of produced gas was 81.53 wt. % which was found at the core puncture diameter of 6.35 mm. The core puncture diameter was furthermore found to affect the temperature distribution inside the kiln and its thermal efficiency. In conclusion, the high efficient biochar kiln can be designed and constructed by using the proper core puncture diameter.

Keywords: anila stove, bio-char, soil conditioning materials, temperature distribution

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Authors: M. Bortolini, E. Ferrari, F. G. Galizia, C. Mora

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In the last decades, an increasing set of companies adopted lean philosophy to improve their productivity and efficiency promoting the so-called continuous improvement concept, reducing waste of time and cutting off no-value added activities. In parallel, increasing attention rises toward green practice and management through the spread of the green supply chain pattern, to minimise landfilled waste, drained wastewater and pollutant emissions. Starting from a review on contributions deepening lean and green principles applied to supply chain management, the most relevant drivers to measure the performance of industrial processes are pointed out. Specific attention is paid on the role of cost because it is of key importance and it crosses both lean and green principles. This analysis leads to figure out an original reference framework for integrating lean and green principles in designing and managing supply chains. The proposed framework supports the application, to the whole value chain or to parts of it, e.g. distribution network, assembly system, job-shop, storage system etc., of the lean-green integrated perspective. Evidences show that the combination of the lean and green practices lead to great results, higher than the sum of the performances from their separate application. Lean thinking has beneficial effects on green practices and, at the same time, methods allowing environmental savings generate positive effects on time reduction and process quality increase.

Keywords: environmental sustainability, green supply chain, integrated framework, lean thinking, supply chain management

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Authors: Oscar Javier Herrera Ochoa, Ivan Dario Romero Fonseca

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This paper presents a design of a model for planning the distribution logistics operation. The significance of this work relies on the applicability of this fact to the analysis of small and medium enterprises (SMEs) of dry freight in Bogotá. Two stages constitute this implementation: the first one is the place where optimal planning is achieved through a hybrid model developed with mixed integer programming, which considers the transhipment operation based on a combined load allocation model as a classic transshipment model; the second one is the specific routing of that operation through the heuristics of Clark and Wright. As a result, an integral model is obtained to carry out the step by step planning of the distribution of dry freight for SMEs in Bogotá. In this manner, optimum assignments are established by utilizing transshipment centers with that purpose of determining the specific routing based on the shortest distance traveled.

Keywords: transshipment model, mixed integer programming, saving algorithm, dry freight transportation

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Authors: Helen K. Buteme, Rebecca Axelsson-Robertson, Moses L. Joloba, Henry W. Boom, Gunilla Kallenius, Markus Maeurer

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Background: The Ugandan population is heavily affected by infectious diseases and Human leukocyte antigen (HLA) diversity plays a crucial role in the host-pathogen interaction and affects the rates of disease acquisition and outcome. The identification of HLA class 1 alleles and determining which alleles are associated with tuberculosis (TB) outcomes would help in screening individuals in TB endemic areas for susceptibility to TB and to predict resistance or progression to TB which would inevitably lead to better clinical management of TB. Aims: To be able to determine the HLA class 1 phenotype distribution in a Ugandan TB cohort and to establish the relationship between these phenotypes and active and latent TB. Methods: Blood samples were drawn from 32 HIV negative individuals with active TB and 45 HIV negative individuals with latent MTB infection. DNA was extracted from the blood samples and the DNA samples HLA typed by the polymerase chain reaction-sequence specific primer method. The allelic frequencies were determined by direct count. Results: HLA-A*02, A*01, A*74, A*30, B*15, B*58, C*07, C*03 and C*04 were the dominant phenotypes in this Ugandan cohort. There were differences in the distribution of HLA types between the individuals with active TB and the individuals with LTBI with only HLA-A*03 allele showing a statistically significant difference (p=0.0136). However, after FDR computation the corresponding q-value is above the expected proportion of false discoveries (q-value 0.2176). Key findings: We identified a number of HLA class I alleles in a population from Central Uganda which will enable us to carry out a functional characterization of CD8+ T-cell mediated immune responses to MTB. Our results also suggest that there may be a positive association between the HLA-A*03 allele and TB implying that individuals with the HLA-A*03 allele are at a higher risk of developing active TB.

Keywords: HLA, phenotype, tuberculosis, Uganda

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Authors: Elham Sharif, Nasser Rizk, Sirin Abu Aqel, Ofelia Masoud

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Background: Previous studies have investigated the association of rs1544410, rs7975232 and rs731236 polymorphisms in vitamin D receptor gene and its impact on diseases such as cancer, diabetes and hypertension in different ethnic backgrounds. Aim: The aim of this study is to investigate the association between VDR polymorphisms using three SNP’s (rs1544410, rs7975232 and rs731236) and the severity of the significant lesion in coronary arteries among angiographically diagnosed CAD. Methods: A prospective-retrospective study was conducted on 192 CAD patients enrolled from the cardiology department-Heart Hospital HMC, grouped in 96 subjects with significant stenosis and 96 with non-significant stenosis with a mean age between 30 and 75 years old. Genotyping was performed for the following SNPs rs1544410, rs7975232 and rs731236 using TaqMan assay by the Real Time PCR, ABI 7500 in Health Sciences Labs at Qatar University Biomedical Research Center. Results: The results showed that both groups have matched age and gender distribution but patients with the significant stenosis have significantly higher; BMI (p=0.047); smoking status (p=0.039); FBS (p= 0.031); CK-MB (p=0.025) and Troponin (p=0.002) than the patients with non–significant lesion. Among the traditional risk factors, smoking increases the odds of the severe stenotic lesion in CAD patients by 1.984, with 95% CI between 1.024 – 7.063, with p= 0.042.HWE showed deviations of the rs1544410 and rs731236 among the study subjects. The most frequent genotype in distribution of rs7975232 is the AA among the significant stenosis patients, while the heterozygous AC was the frequent genotype in distribution among the non-significant stenosis group. The carriers of CC genotype in rs7975232 increased the risk of having significant coronary arteries stenotic lesion by 1.83 with 95% CI (1.020 – 3.280), p=0.043. No association was found between the rs7975232 with vitamin D and VDBP. Conclusion: There is a significant association between rs7975232 and the severity of CAD lesion. The carrier of CC genotype in rs7975232 increased the risk of having significant coronary arteries atherosclerotic lesion especially in patients with smoking history independent of vitamin D.

Keywords: vitamin D, vitamin D receptor, polymorphism, coronary harat disease

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Authors: E. O. E. Nnadi

Abstract:

Transportation is a critical factor in the economic growth and development of any nation, region or state. Good transportation network supports every sector of the economy like the manufacturing, transportation and encourages investors thereby affect the overall economic prosperity. The paper evaluates the impact of transportation on the economic growth of Nigeria using south eastern states as a case study. The choice of the case study is its importance as the commercial and industrial nerve of the country. About 200 respondents who are of different professions such as dealers in goods, transporters, contractors, consultants, bankers were selected and a set of questionnaire were administered to using the systematic sampling technique in the five states of the region. Descriptive statistics and relative importance index (RII) technique was employed for the analysis of the data gathered. The findings of the analysis reveal that Nigeria has the least effective ratio per population in Africa of 949.91 km/Person. Conclusion was drawn to improve road network in the area and the country as a whole to enhance the economic activities of the people.

Keywords: economic growth, south-east, transportation, transportation cost, Nigeria

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Authors: Jan-Tore H. Horn, Jørgen Juncher Jensen

Abstract:

Uncertainties related to fatigue damage estimation of non-linear systems are highly dependent on the tail behaviour and extreme values of the stress range distribution. By using a combination of the First Order Reliability Method (FORM) and Monte Carlo simulations (MCS), the accuracy of the fatigue estimations may be improved for the same computational efforts. The method is applied to a bottom-fixed, monopile-supported large offshore wind turbine, which is a non-linear and dynamically sensitive system. Different curve fitting techniques to the fatigue damage distribution have been used depending on the sea-state dependent response characteristics, and the effect of a bi-linear S-N curve is discussed. Finally, analyses are performed on several environmental conditions to investigate the long-term applicability of this multistep method. Wave loads are calculated using state-of-the-art theory, while wind loads are applied with a simplified model based on rotor thrust coefficients.

Keywords: fatigue damage, FORM, monopile, Monte Carlo, simulation, wind turbine

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Authors: Somdatta Saha

Abstract:

The main purpose was to provide an implementable design scenario for underground coal mines using wireless sensor networks (WSNs). The main reason being that given the intricacies in the physical structure of a coal mine, only low power WSN nodes can produce accurate surveillance and accident detection data. The work mainly concentrated on designing and simulating various alternate scenarios for a typical mine and comparing them based on the obtained results to arrive at a final design. In the Era of embedded technology, the Zigbee protocols are used in more and more applications. Because of the rapid development of sensors, microcontrollers, and network technology, a reliable technological condition has been provided for our automatic real-time monitoring of coal mine. The underground system collects temperature, humidity and methane values of coal mine through sensor nodes in the mine; it also collects the number of personnel inside the mine with the help of an IR sensor, and then transmits the data to information processing terminal based on ARM.

Keywords: ARM, embedded board, wireless sensor network (Zigbee)

Procedia PDF Downloads 334

Authors: Emmanuelle Maria, Pierre Crançon, Gaëtane Lespes

Abstract:

Colloids are ubiquitous in the environment and are known to play a major role in enhancing the transport of trace elements, thus being an important vector for contaminants dispersion. Colloids study and characterization are necessary to improve our understanding of the fate of pollutants in the environment. However, in stream water and groundwater, colloids are often very poorly concentrated. It is therefore necessary to pre-concentrate colloids in order to get enough material for analysis, while preserving their initial structure. Many techniques are used to extract and/or pre-concentrate the colloidal phase from bulk aqueous phase, but yet there is neither reference method nor estimation of the impact of these different techniques on the colloids structure, as well as the bias introduced by the separation method. In the present work, we have tested and compared several methods of colloidal phase extraction/pre-concentration, and their impact on colloids properties, particularly their size distribution and their elementary composition. Ultrafiltration methods (frontal, tangential and centrifugal) have been considered since they are widely used for the extraction of colloids in natural waters. To compare these methods, a ‘synthetic groundwater’ was used as a reference. The size distribution (obtained by Field-Flow Fractionation (FFF)) and the chemical composition of the colloidal phase (obtained by Inductively Coupled Plasma Mass Spectrometry (ICPMS) and Total Organic Carbon analysis (TOC)) were chosen as comparison factors. In this way, it is possible to estimate the pre-concentration impact on the colloidal phase preservation. It appears that some of these methods preserve in a more efficient manner the colloidal phase composition while others are easier/faster to use. The choice of the extraction/pre-concentration method is therefore a compromise between efficiency (including speed and ease of use) and impact on the structural and chemical composition of the colloidal phase. In perspective, the use of these methods should enhance the consideration of colloidal phase in the transport of pollutants in environmental assessment studies and forensics.

Keywords: chemical composition, colloids, extraction, preconcentration methods, size distribution

Procedia PDF Downloads 205

Authors: Wan Jen Chang

Abstract:

Back ground: Taiwan's current adult lifelong learning services have expanded from vocational training to universal lifelong learning. However, both the professional knowledge training of learning guidance and consulting services and the provision of adult online learning consulting service systems still need to be established. Purpose: The purposes of this study are as follows: 1. Analyze the professional training mechanism for cultivating adult lifelong learning consultation and coaching; 2. Explore the feasibility of constructing a system that uses network technology to provide adult learning consultation services. Research design: This study conducts a literature analysis of counseling and coaching policy reports on lifelong learning in European countries and the United States. There are two focus discussions were conducted with 15 lifelong learning scholars, experts and practitioners as research subjects. The following two topics were discussed and suggested: 1. The current situation, needs and professional ability training mechanism of "Adult Lifelong Learning Consulting and Services"; 2. Strategies for establishing an "Adult Lifelong Learning Consulting and Service internet System". Conclusion: 1.Based on adult lifelong learning consulting and service needs, plan a professional knowledge training and certification system.2.Adult lifelong learning consulting and service professional knowledge and skills training should include the use of network technology to provide consulting service skills.3.To establish an adult lifelong learning consultation and service system, the Ministry of Education should promulgate policies and measures at the central level and entrust local governments or private organizations to implement them.4.The adult lifelong learning consulting and service system can combine the national qualifications framework, private sector and NPO to expand learning consulting service partners.

Keywords: adult lifelong learning, profesional knowledge, consulting and service, network system

Procedia PDF Downloads 59

Authors: Roshanak Khodabakhsh Jolfaei, Javad Akbari Torkestani

Abstract:

As a cooperative environment for problem-solving, it is necessary that grids develop efficient job scheduling patterns with regard to their goals, domains and structure. Since the Grid environments facilitate distributed calculations, job scheduling appears in the form of a critical problem for the management of Grid sources that influences severely on the efficiency for the whole Grid environment. Due to the existence of some specifications such as sources dynamicity and conditions of the network in Grid, some algorithm should be presented to be adjustable and scalable with increasing the network growth. For this purpose, in this paper a job scheduling algorithm has been presented on the basis of learning automata in computational Grid which the performance of its results were compared with FPSO algorithm (Fuzzy Particle Swarm Optimization algorithm) and GJS algorithm (Grid Job Scheduling algorithm). The obtained numerical results indicated the superiority of suggested algorithm in comparison with FPSO and GJS. In addition, the obtained results classified FPSO and GJS in the second and third position respectively after the mentioned algorithm.

Keywords: computational grid, job scheduling, learning automata, dynamic scheduling

Procedia PDF Downloads 327