Search results for: network formation

Authors: Elabed Saud

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This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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Authors: Jorge Fernandes Teixeira Filho, André Oliveira Alcantara Fontenele, Érick Aragão Ribeiro

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The constant emergence of new technologies used in automated processes makes it necessary for teachers and traders to apply new technologies in their classes. This paper presents an application of a new technology that will be employed in a didactic plant, which represents an effluent treatment process located in a laboratory of a federal educational institution. At work were studied in the first place, all components to be placed on automation laboratory in order to determine ways to program, parameterize and organize the plant. New technologies that have been implemented to the process are basically an AS-i network and a Profinet network, a SCADA system, which represented a major innovation in the laboratory. The project makes it possible to carry out in the laboratory various practices of industrial networks and SCADA systems.

Keywords: automation, industrial networks, SCADA systems, lab automation

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Authors: Mehdi Montakhabrazlighi, Ercan Balikci

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The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

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Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

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The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: bibliometrics, data analysis, extraction and data integration, scientometrics

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Authors: Desmond Okorie, Emmanuel Prince

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Availability of portable water has been a global challenge especially to the developing continents/nations such as Africa/Nigeria. The World Health Organization WHO has produced the guideline for drinking water quality GDWQ which aims at ensuring water safety from source to consumer. Portable water parameters test include physical (colour, odour, temperature, turbidity), chemical (PH, dissolved solids) biological (algae, plytoplankton). This paper discusses the use of wireless sensor networks to monitor water quality using efficient and effective sensors that have the ability to sense, process and transmit sensed data. The integration of wireless sensor network to a portable sensing device offers the feasibility of sensing distribution capability, on site data measurements and remote sensing abilities. The current water quality tests that are performed in government water quality institutions in Benue State Nigeria are carried out in problematic locations that require taking manual water samples to the institution laboratory for examination, to automate the entire process based on wireless sensor network, a system was designed. The system consists of sensor node containing one PH sensor, one temperature sensor, a microcontroller, a zigbee radio and a base station composed by a zigbee radio and a PC. Due to the advancement of wireless sensor network technology, unexpected contamination events in water environments can be observed continuously. local area network (LAN) wireless local area network (WLAN) and internet web-based also commonly used as a gateway unit for data communication via local base computer using standard global system for mobile communication (GSM). The improvement made on this development show a water quality monitoring system and prospect for more robust and reliable system in the future.

Keywords: local area network, Ph measurement, wireless sensor network, zigbee

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

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This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

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Authors: Latif Adnane, Benaatou Wafa, Pla Vicent

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Handover is of great significance to achieve seamless connectivity in wireless networks. This paper gives an impression of the main factors which are being affected by the soft and the hard handovers techniques. To know and understand the handover process in The Universal Mobile Telecommunications System (UMTS) network, different statistics are calculated. This paper focuses on the quality of service (QoS) of soft and hard handover in UMTS network, which includes the analysis of received power, signal to noise radio, throughput, delay traffic, traffic received, delay, total transmit load, end to end delay and upload response time using OPNET simulator.

Keywords: handover, UMTS, mobility, simulation, OPNET modeler

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Authors: Kowit Pimpuang

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Through analysis of the Pali-Sanskrit (PL-SKT) terms and their uses in reflecting political society of Thailand, the objectives of this study were to explore PL-SKT word formation and its semantic changes employed in the political society of Thailand and to explore the political reflection of Thai society through their uses. Conceptual framework of this study consists of (1) use of PL-SKT word formation namely, primary derivative (Kitaka), secondary derivative (Tathita), compound (Samasa) and prefix (Upasagga), (2) semantic changes namely; widening, narrowing and transferring of meaning, and (3) political reflection of Thai society. Qualitative method was employed in this study and data were collected from Thai Newspapers. It was found that there were uses of the four kinds of word formation in formatting the new political terms concerned namely, primary derivative, secondary derivative, compound and prefix leading by compound through the following three semantic changes; widening, narrowing and transferring, in order to make clear in understanding. Furthermore, PL-SKT terms were employed in reflecting Thai politics caused by democratic conflicts through the bureaucracy, plutocracy, businessocracy and juristocracy respectively. Later, there have been political business groups and their corruption problems in political society of Thailand.

Keywords: Pali, Sanskrit, reflection, politics, Thailand

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Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway networks play a vital role in post-disaster recovery for disaster-damaged areas. Damaged bridges in such networks can disrupt the recovery activities by impeding the transportation of people, cargo, and reconstruction resources. Therefore, rapid restoration of damaged bridges is of paramount importance to long-term disaster recovery. In the post-disaster recovery phase, the key to restoration scheduling for a highway network is prioritization of bridge-repair tasks. Resilience is widely used as a measure of the ability to recover with which a network can return to its pre-disaster level of functionality. In practice, highways will be temporarily blocked during the downtime of bridge restoration, leading to the decrease of highway-network functionality. The failure to take downtime effects into account can lead to overestimation of network resilience. Additionally, post-disaster recovery of highway networks is generally divided into emergency bridge repair (EBR) in the response phase and long-term bridge repair (LBR) in the recovery phase, and both of EBR and LBR are different in terms of restoration objectives, restoration duration, budget, etc. Distinguish these two phases are important to precisely quantify highway network resilience and generate suitable restoration schedules for highway networks in the recovery phase. To address the above issues, this study proposes a novel resilience quantification method for the optimization of long-term bridge repair schedules (LBRS) taking into account the impact of EBR activities and restoration downtime on a highway network’s functionality. A time-dependent integer program with recursive functions is formulated for optimally scheduling LBR activities. Moreover, since uncertainty always exists in the LBRS problem, this paper extends the optimization model from the deterministic case to the stochastic case. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. The proposed methods are tested using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that, in this case, neglecting the bridge restoration downtime can lead to approximately 15% overestimation of highway network resilience. Moreover, accounting for the impact of EBR on network functionality can help to generate a more specific and reasonable LBRS. The theoretical and practical values are as follows. First, the proposed network recovery curve contributes to comprehensive quantification of highway network resilience by accounting for the impact of both restoration downtime and EBR activities on the recovery curves. Moreover, this study can improve the highway network resilience from the organizational dimension by providing bridge managers with optimal LBR strategies.

Keywords: disaster management, highway network, long-term bridge repair schedule, resilience, restoration downtime

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Pankhuri Dagaonkar, Charumani Singh, Poornima Krothapalli, Krishna Karthik

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The backbone of any retail e-commerce success story is a unique design of supply chain network, providing the business an unparalleled speed and scalability. Primary goal of the supply chain strategy is to meet customer expectation by offering fastest deliveries while keeping the cost minimal. Meeting this objective at the large market that India provides is the problem statement that we have targeted here. There are many models and optimization techniques focused on network design to identify the ideal facility location and size, optimizing cost and speed. In this paper we are presenting a tactical approach to optimize cost of an existing network for a predefined speed. We have considered both forward and reverse logistics of a retail e-commerce supply chain consisting of multiple fulfillment (warehouse) and delivery centers, which are connected via sortation nodes. The mathematical model presented here determines if the shipment from a node should get sorted directly for the last mile delivery center or it should travel as consolidated package to another node for further sortation (resort). The objective function minimizes the total cost by varying the resort percentages between nodes and provides the optimal resource allocation and number of sorts at each node.

Keywords: distribution strategy, mathematical model, network design, supply chain management

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Kious Mecheri, Hadjadj Abdechafik, Ameur Aissa

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The wear of cutting tool degrades the quality of the product in the manufacturing processes. The online monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear online. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions.

Keywords: flank wear, cutting forces, high speed milling, signal processing, neural network

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Authors: Mohadese Motahari

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In general, we consider the phenomenon of "fandism" or extreme fandom to be an aspect of fandom for a person, a group, or a collection, which leads to extreme support for them. So, for example, we consider a fan or a "fanatic" (which literally means a "fanatical person") to be a person who is extremely interested in a certain topic or topics and has a special passion and fascination for that issue. It may also be beyond the scope of logic and normal behavior of the society. With the expansion of the media and the advancement of technology, the phenomenon of fandom also underwent many changes and not only became more intense, but a large economy was also formed alongside it, and it is becoming more and more important every day. This economy, which emerged from the past with the formation of the first media, has now taken a different form with the development of media and social networks, as well as the change in the interaction between celebrities and audiences. Earning huge amounts of money with special methods in every social network and every media is achieved through fans and fandoms. In this article, we have studied the relationship between fans and famous people with reference to the economic debates surrounding it.

Keywords: fandism, famous people, social media, new media

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Authors: Manal Bouasria, Mohammed-Hichem Benzaama, Valérie Pralong, Yassine El Mendili

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Reducing the quantity of cement in cementitious composites can help to reduce the environmental effect of construction materials. By-products such as ferronickel slags (FNS), fly ash (FA), and Crepidula fornicata (CR) are promising options for cement replacement. In this work, we investigated the relevance of substituting cement with FNS-CR and FA-CR on the mechanical properties of mortar and on the thermal properties of concrete. Foraging intervals ranging from 2 to 28 days, the mechanical properties are obtained by 3-point bending and compression tests. The chosen mix is used to construct a prototype in order to study the material’s hygrothermal performance. The data collected by the sensors placed on the prototype was utilized to build an artificial neural network.

Keywords: artificial neural network, cement, circular economy, concrete, by products

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Authors: Mohammad Arif Khan

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This paper analyzes and presents the development of Artificial Neural Network based controller of space vector modulation (ANN-SVPWM) for a seven-phase voltage source inverter. At first, the conventional method of producing sinusoidal output voltage by utilizing six active and one zero space vectors are used to synthesize the input reference, is elaborated and then new PWM scheme called Artificial Neural Network Based PWM is presented. The ANN based controller has the advantage of the very fast implementation and analyzing the algorithms and avoids the direct computation of trigonometric and non-linear functions. The ANN controller uses the individual training strategy with the fixed weight and supervised models. A computer simulation program has been developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller. A comparison of the proposed scheme with the conventional scheme is presented based on various performance indices. Extensive Simulation results are provided to validate the findings.

Keywords: space vector PWM, total harmonic distortion, seven-phase, voltage source inverter, multi-phase, artificial neural network

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Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

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Proxy signature scheme permits an original signer to delegate his/her signing capability to a proxy signer, and then the proxy signer generates a signing message on behalf of the original signer. The two parties must be able to authenticate one another and agree on a secret encryption key, in order to communicate securely over an unreliable public network. Authenticated key agreement protocols have an important role in building secure communications network between the two parties. In this paper, we present a secure proxy signature scheme over an efficient and secure authenticated key agreement protocol based on the discrete logarithm problem.

Keywords: proxy signature, warrant partial delegation, key agreement, discrete logarithm

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Authors: Serap Durkut, A. Eser Elcin, Y. Murat Elcin

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Stimuli-sensitive polymeric hydrogels have received extensive attention in the biomedical field due to their sensitivity to physical and chemical stimuli (temperature, pH, ionic strength, light, etc.). This study describes the rheological properties of a novel thermoresponsive poly(N-vinylcaprolactam)-g-collagen hydrogel. In the study, we first synthesized a facile and novel synthetic carboxyl group-terminated thermo-responsive poly(N-vinylcaprolactam)-COOH (PNVCL-COOH) via free radical polymerization. Further, this compound was effectively grafted with native collagen, by utilizing the covalent bond between the carboxylic acid groups at the end of the chains and amine groups of the collagen using cross-linking agent (EDC/NHS), forming PNVCL-g-Col. Newly-formed hybrid hydrogel displayed novel properties, such as increased mechanical strength and thermoresponsive characteristics. PNVCL-g-Col showed low critical solution temperature (LCST) at 38ºC, which is very close to the body temperature. Rheological studies determine structural–mechanical properties of the materials and serve as a valuable tool for characterizing. The rheological properties of hydrogels are described in terms of two dynamic mechanical properties: the elastic modulus G′ (also known as dynamic rigidity) representing the reversible stored energy of the system, and the viscous modulus G″, representing the irreversible energy loss. In order to characterize the PNVCL-g-Col, the rheological properties were measured in terms of the function of temperature and time during phase transition. Below the LCST, favorable interactions allowed the dissolution of the polymer in water via hydrogen bonding. At temperatures above the LCST, PNVCL molecules within PNVCL-g-Col aggregated due to dehydration, causing the hydrogel structure to become dense. When the temperature reached ~36ºC, both the G′ and G″ values crossed over. This indicates that PNVCL-g-Col underwent a sol-gel transition, forming an elastic network. Following temperature plateau at 38ºC, near human body temperature the sample displayed stable elastic network characteristics. The G′ and G″ values of the PNVCL-g-Col solutions sharply increased at 6-9 minute interval, due to rapid transformation into gel-like state and formation of elastic networks. Copolymerization with collagen leads to an increase in G′, as collagen structure contains a flexible polymer chain, which bestows its elastic properties. Elasticity of the proposed structure correlates with the number of intermolecular cross-links in the hydrogel network, increasing viscosity. However, at 8 minutes, G′ and G″ values sharply decreased for pure collagen solutions due to the decomposition of the elastic and viscose network. Complex viscosity is related to the mechanical performance and resistance opposing deformation of the hydrogel. Complex viscosity of PNVCL-g-Col hydrogel was drastically changed with temperature and the mechanical performance of PNVCL-g-Col hydrogel network increased, exhibiting lesser deformation. Rheological assessment of the novel thermo-responsive PNVCL-g-Col hydrogel, exhibited that the network has stronger mechanical properties due to both permanent stable covalent bonds and physical interactions, such as hydrogen- and hydrophobic bonds depending on temperature.

Keywords: poly(N-vinylcaprolactam)-g-collagen, thermoresponsive polymer, rheology, elastic modulus, stimuli-sensitive

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Authors: Kara Terki Ibtissem, Hassaine Hafida, Bellifa Samia

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Introduction: Staphylococcus aureus is one of the species that are most frequently isolated from urinary catheters. The ability to produce a biofilm is an important step in the pathogenesis of these staphylococci; biofilm formation is strongly dependent on the environmental conditions it also depends on the different parameters these biofilms are subjected to. Antiseptics, including ethanol and betadine, are used in clinical practice for disinfection and infection prevention. Recent studies, however, demonstrate that disinfectants may enhance biofilm production in Staphylococci. Methods: In this study, 48 staphylococcus aureus isolated from urinary catheters at the University Hospital Center of Sidi Bel Abbes (in Northwestern Algeria) were analyzed to detect the formation of biofilm by culture on Red Congo Agar (RCA), the Tube Method (TM) and tissue Culture Plate (TCP) techniques, this last was also used to investigate the effect of ethanol and Betadine on the preformed biofilm In a second time to know which environment is most favorable to the formation of the biofilm we perform a statistical test based on the student test by the software R. Results: It has been found that 23 strains produced a bacterial slime on the Congo red medium, 5 strains produced a biofilm by the tube method, 2 of which are highly productive. In addition, 7 strains produced a biofilm on polystyrene micro-plates; this number was higher in the presence of ethanol 70% and ethanol 90% with 19 and 11 biofilm-producing strains, respectively. On the other hand, no biofilm was formed in the presence of Betadine. Conclusion: It is important to examine the response of biofilms following an imposed external constraint, such as disinfectants, in order to develop new strategies to combat bacterial biofilms but also to better control their formation.

Keywords: staphylococcus aureus, biofilm, urinary catheter, ethanol

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Authors: Sitong. Chen, Bing Wei

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As a sociocultural concept, identity has increasingly gained attention in educational research, and the notion of students’ science identity has been widely discussed in the field of science education. Science identity was proved to be a key indicator of students’ learning engagement, persistence, and career intentions in science-related and STEM fields. Thus, a great deal of educational effort has been made to promote students’ science identity in former studies. However, most of this research was focused on students’ identity development during undergraduate and graduate periods, except for a few studies exploring high school students’ identity formation. High school has been argued as a crucial period for promoting science identity. This study applied a qualitative method to explore how high school students have come to form their science identities in previous learning and living experiences. Semi-structured interviews were conducted with 8 newly enrolled undergraduate students majoring in science-related fields. As suggested by the narrative data from interviews, students’ formation of science identities was driven by their five interrelated experiences: growing self-recognition as a science person, achieving success in learning science, getting recognized by influential others, being interested in science subjects, and informal science experiences in various contexts. Specifically, students’ success and achievement in science learning could facilitate their interest in science subjects and others’ recognition. And their informal experiences could enhance their interest and performance in formal science learning. Furthermore, students’ success and interest in science, as well as recognition from others together, contribute to their self-recognition. Based on the results of this study, some practical implications were provided for science teachers and researchers in enhancing high school students’ science identities.

Keywords: high school students, identity formation, learning experiences, living experiences, science identity

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Authors: Mohammad F. Fauzi, Nurul H. Shahba M. Shahrun, Nurul W. Hamzah, Mohd Noah A. Rahman, Afzaal H. Seyal

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In this paper, we proposed a simulation system using Wireless Sensor Network (WSN) that will be distributed around the forest for early forest fire detection and to locate the areas affected. In Brunei Darussalam, approximately 78% of the nation is covered by forest. Since the forest is Brunei’s most precious natural assets, it is very important to protect and conserve our forest. The hot climate in Brunei Darussalam can lead to forest fires which can be a fatal threat to the preservation of our forest. The process consists of getting data from the sensors, analyzing the data and producing an alert. The key factors that we are going to analyze are the surrounding temperature, wind speed and wind direction, humidity of the air and soil.

Keywords: forest fire monitor, humidity, wind direction, wireless sensor network

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Authors: Gon Park

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Seoul in South Korea established the 2030 Seoul City Master Plan that contains green-link projects to connect critical green areas within the city. However, the plan does not have detailed analyses for green infrastructure to incorporate land-cover information to many structural classes. This study maps green infrastructure networks of Seoul for complementing their green plans with identifying and raking green areas. Hubs and links of main elements of green infrastructure have been identified from incorporating cadastral data of 967,502 parcels to 135 of land use maps using geographic information system. Network analyses were used to rank hubs and links of a green infrastructure map with applying a force-directed algorithm, weighted values, and binary relationships that has metrics of density, distance, and centrality. The results indicate that network analyses using cadastral parcel data can be used as the framework to identify and rank hubs, links, and networks for the green infrastructure planning under a variable scenarios of green areas in cities.

Keywords: cadastral data, green Infrastructure, network analysis, parcel data

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Hugo Badia

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This research answers which is the most suitable transit network structure to serve specific demand requirements in an increasing urban dispersion process. Two main approaches of network design are found in the literature. On the one hand, a traditional answer, widespread in our cities, that develops a high number of lines to connect most of origin-destination pairs by direct trips; an approach based on the idea that users averse to transfers. On the other hand, some authors advocate an alternative design characterized by simple networks where transfer is essential to complete most of trips. To answer which of them is the best option, we use a two-step methodology. First, by means of an analytical model, three basic network structures are compared: a radial scheme, starting point for the other two structures, a direct trip-based network, and a transfer-based one, which represent the two alternative transit network designs. The model optimizes the network configuration with regard to the total cost for each structure. For a scenario of dispersion, the best alternative is the structure with the minimum cost. This dispersion degree is defined in a simple way considering that only a central area attracts all trips. If this area is small, we have a high concentrated mobility pattern; if this area is too large, the city is highly decentralized. In this first step, we can determine the area of applicability for each structure in function to that urban dispersion degree. The analytical results show that a radial structure is suitable when the demand is so centralized, however, when this demand starts to scatter, new transit lines should be implemented to avoid transfers. If the urban dispersion advances, the introduction of more lines is no longer a good alternative, in this case, the best solution is a change of structure, from direct trips to a network based on transfers. The area of applicability of each network strategy is not constant, it depends on the characteristics of demand, city and transport technology. In the second step, we translate analytical results to a real case study by the relationship between the parameters of dispersion of the model and direct measures of dispersion in a real city. Two dimensions of the urban sprawl process are considered: concentration, defined by Gini coefficient, and centralization by area based centralization index. Once it is estimated the real dispersion degree, we are able to identify in which area of applicability the city is located. In summary, from a strategic point of view, we can obtain with this methodology which is the best network design approach for a city, comparing the theoretical results with the real dispersion degree.

Keywords: analytical network design model, network structure, public transport, urban dispersion

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Authors: Mir Jalil Razavi, Tianming Liu, Xianqiao Wang

Abstract:

Cortical folding, characterized by convex gyri and concave sulci, has an intrinsic relationship to the brain’s functional organization. Understanding the mechanism of the brain’s convoluted patterns can provide useful clues into normal and pathological brain function. During the development, the cerebral cortex experiences a noticeable expansion in volume and surface area accompanied by tremendous tissue folding which may be attributed to many possible factors. Despite decades of endeavors, the fundamental mechanism and key regulators of this crucial process remain incompletely understood. Therefore, to taking even a small role in unraveling of brain folding mystery, we present a mechanical model to find mechanism of 3-hinges formation in a growing brain that it has not been addressed before. A 3-hinge is defined as a gyral region where three gyral crests (hinge-lines) join. The reasons that how and why brain prefers to develop 3-hinges have not been answered very well. Therefore, we offer a theoretical and computational explanation to mechanism of 3-hinges formation in a growing brain and validate it by experimental observations. In theoretical approach, the dynamic behavior of brain tissue is examined and described with the aid of a large strain and nonlinear constitutive model. Derived constitute model is used in the computational model to define material behavior. Since the theoretical approach cannot predict the evolution of cortical complex convolution after instability, non-linear finite element models are employed to study the 3-hinges formation and secondary morphological folds of the developing brain. Three-dimensional (3D) finite element analyses on a multi-layer soft tissue model which mimics a small piece of the brain are performed to investigate the fundamental mechanism of consistent hinge formation in the cortical folding. Results show that after certain amount growth of cortex, mechanical model starts to be unstable and then by formation of creases enters to a new configuration with lower strain energy. By further growth of the model, formed shallow creases start to form convoluted patterns and then develop 3-hinge patterns. Simulation results related to 3-hinges in models show good agreement with experimental observations from macaque, chimpanzee and human brain images. These results have great potential to reveal fundamental principles of brain architecture and to produce a unified theoretical framework that convincingly explains the intrinsic relationship between cortical folding and 3-hinges formation. This achieved fundamental understanding of the intrinsic relationship between cortical folding and 3-hinges formation would potentially shed new insights into the diagnosis of many brain disorders such as schizophrenia, autism, lissencephaly and polymicrogyria.

Keywords: brain, cortical folding, finite element, three hinge

Procedia PDF Downloads 234

Authors: Troy A. Tyre

Abstract:

Organizational behavior and organizational change are elements of the ever-changing global business environment. Leadership and organizational behavior are 21st century disciplines. Network marketing organizations need to understand the ever-changing nature of global business and be ready and willing to adapt to the environment. Network marketing organizations have a challenge keeping up with a rapid escalation in global growth. Network marketing growth has been steady and global. Network marketing organizations have been slow to develop a 21st century global strategy to manage the rapid escalation of growth degrading organizational behavior, job satisfaction, increasing attrition, and degrading customer service. Development of an organizational behavior and leadership theory for the 21st century to help network marketing develops a global business strategy to manage the rapid escalation in growth that affects organizational behavior. Managing growth means organizational leadership must develop and adapt to the organizational environment. Growth comes with an open mind and one’s departure from the comfort zone. Leadership growth operates in the tacit dimension. Systems thinking and adaptation of mental models can help shift organizational behavior. Shifting the organizational behavior requires organizational learning. Organizational learning occurs through single-loop, double-loop, and triple-loop learning. Triple-loop learning is the most difficult, but the most rewarding. Tools such as theory U can aid in developing a landscape for organizational behavioral development. Additionally, awareness to espoused and portrayed actions is imperatives. Theories of motivation, cross-cultural diversity, and communications are instrumental in founding an organizational behavior suited for the 21st century.

Keywords: global, leadership, network marketing, organizational behavior

Procedia PDF Downloads 551

Authors: Hossain A., Chowdhury S. I.

Abstract:

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

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

Procedia PDF Downloads 98

Authors: Joanne Haddad

Abstract:

The distinctive traits of early settlers at initial stages of institutional development may be crucial for cultural formation. In 1973, the cultural geographer Wilbur Zelinsky postulated this in his doctrine of “first effective settlement”. There is however little empirical evidence supporting the role of early settlers in shaping culture over the long run. This paper tests this hypothesis by relating early settlers’ culture to within state variation in gender norms in the United States. Settlers’ culture is captured using past female labor force participation, women’s suffrage, and financial rights at their place of origin. The paper documents the distinctive characteristics of settlers’ populations and provide suggestive evidence in support of the transmission of gender norms across space and time. Results from this analysis show that women’s labor supply is higher, in both the short and long run, in U.S. counties that historically hosted a larger settler population originating from places with favorable gender attitudes. Findings from this study shed new light on the importance of the characteristics of immigrants and their place of origin for cultural formation in hosting societies.

Keywords: female labor force participation, settlers, gender norms, cultural formation.

Procedia PDF Downloads 105