Search results for: passive optical networks (PONs)

Authors: Kate Smith, Shuming Liu, Yi Liu, Dragan Savic, Gustaf Olsson, Tian Chang, Xue Wu

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In a water supply system, a great deal of care goes into sourcing, treating and delivering water to consumers, but less thought is given to the energy consumed during these processes. This study uses 2011 data to quantify energy use for urban water supply in China and investigates population density as a possible influencing factor. The objective is to provide information that can be used to develop energy-conscious water infrastructure policy, calculate the energy co-benefits of water conservation and compare energy use between China and other countries. The average electrical energy intensity and per capita electrical energy consumption for urban water supply in China in 2011 were 0.29 kWh/m3 and 33.2 kWh/cap•yr, respectively. Comparison between provinces revealed a direct correlation between energy intensity of urban water supply and population served per unit length of pipe. This could imply energy intensity is lower when more densely populated areas are supplied by relatively dense networks of pipes. This study also found that whereas the percentage of energy used for urban water supply tends to increase with the percentage of population served this increase is slower where water supply is more energy efficient and where a larger percentage of population is already supplied.

Keywords: china, electrical energy use, water-energy nexus, water supply

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

Procedia PDF Downloads 156

Authors: Meril Kate Mariano, Billy Joel Almarinez Divina Amalin, Jose Isagani Janairo

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The scale insects Aspidiotus destructor and Aspidiotus rigidus exhibit notable scale covers made of wax which provides protection against water loss and is capable to resist wetting, thus making them a desirable model for biomimetic designs. Their waxy covers enable them to infest mainly leaves of coconut trees despite the harsh wind and rain. This study aims to describe and compare the micro morphological characters on the surfaces of their scale covers consequently, how these micro structures affect their wetting properties. Scanning electron microscope was used for the surface characterization while an optical contact angle meter was employed in the wetting measurement. The scale cover of A. destructor is composed of multiple overlapping layers of wax that is arranged regularly while that of A. rigidus is composed of a uniform layer of wax with much more prominent wax ribbons irregularly arranged compared to the former. The protrusions found on the two organisms are formed by the wax ribbons that differ in arrangement with their height being A. destructor (3.57+1.29) < A. rigidus (4.23+1.22) and their density A. destructor (15+2.94) < A. rigidus (18.33+2.64). These morphological measurements could affect the contact angle (CA θ) measurement of A. destructor (102.66+9.78°) < A. rigidus (102.77 + 11.01°) wherein the assessment that the interaction of the liquid to the microstructures of the substrate is a large factor in the wetting properties of the insect scales is realized. The calculated surface free energy of A. destructor (38.47 mJ/m²) > A. rigidus (31.02 mJ/m²) shows inverse proportionality with the CA measurement. The dispersive interaction between the surface and liquid is more prevalent compared to the polar interaction for both Aspidiotus species, which was observed using the Fowkes method. The results of this study have possible applications to be a potential biomimetic design for various industries such as textiles and coatings.

Keywords: Aspidiotus spp., biomimetics, contact angle, surface characterization, wetting behavior

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Authors: Alexander Hempfing

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The desire to understand business cycle fluctuations, trade interdependencies and co-movement has a long tradition in economic thinking. From input-output economics to business cycle theory, researchers aimed to find appropriate answers from an empirical as well as a theoretical perspective. This paper empirically analyses how the production structure of the global economy and several states developed over time, what their distributional properties are and if there are network specific metrics that allow identifying structurally important nodes, on a global, national and sectoral scale. For this, the World Input-Output Database was used, and different statistical methods were applied. Empirical evidence is provided that the importance of the Eastern hemisphere in the global production network has increased significantly between 2000 and 2014. Moreover, it was possible to show that the sectoral eigenvector centrality indices on a global level are power-law distributed, providing evidence that specific national sectors exist which are more critical to the world economy than others while serving as a hub within the global production network. However, further findings suggest, that global production cannot be characterized as a scale-free network.

Keywords: economic integration, industrial organization, input-output economics, network economics, production networks

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Authors: Wu Peng, Anders Liljerehn, Martin Magnevall

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In metal cutting, tool wear gradually changes the micro geometry of the cutting edge. Today there is a significant gap in understanding the impact these geometrical changes have on the cutting forces which governs tool deflection and heat generation in the cutting zone. Accurate models and understanding of the interaction between the work piece and cutting tool leads to improved accuracy in simulation of the cutting process. These simulations are useful in several application areas, e.g., optimization of insert geometry and machine tool monitoring. This study aims to develop an extended Kienzle force model to account for the effect of rake angle variations and tool flank wear have on the cutting forces. In this paper, the starting point sets from cutting force measurements using orthogonal turning tests of pre-machined flanches with well-defined width, using triangular coated inserts to assure orthogonal condition. The cutting forces have been measured by dynamometer with a set of three different rake angles, and wear progression have been monitored during machining by an optical measuring collaborative robot. The method utilizes the measured cutting forces with the inserts flank wear progression to extend the mechanistic cutting forces model with flank wear as an input parameter. The adapted cutting forces model is validated in a turning process with commercial cutting tools. This adapted cutting forces model shows the significant capability of prediction of cutting forces accounting for tools flank wear and different-rake-angle cutting tool inserts. The result of this study suggests that the nonlinear effect of tools flank wear and interaction between the work piece and the cutting tool can be considered by the developed cutting forces model.

Keywords: cutting force, kienzle model, predictive model, tool flank wear

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Authors: V. P. Muhamed Shajudheen, K. Viswanathan, K. Anitha Rani, A. Uma Maheswari, S. Saravana Kumar

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We are reporting a simple and low-cost chemical precipitation method adopted to prepare zinc oxide nanoparticles (ZnO) using polyvinyl pyrrolidone (PVP) as a capping agent. The Differential Scanning Calorimetry (DSC) and Thermo Gravimetric Analysis (TGA) was applied on the dried gel sample to record the phase transformation temperature of zinc hydroxide Zn(OH)2 to zinc oxide (ZnO) to obtain the annealing temperature of 800C. The thermal, structure, morphology and optical properties have been employed by different techniques such as DSC-TGA, X-Ray Diffraction (XRD), Fourier Transform Infra-Red spectroscopy (FTIR), Micro Raman spectroscopy, UV-Visible absorption spectroscopy (UV-Vis), Photoluminescence spectroscopy (PL) and Field Effect Scanning Electron Microscopy (FESEM). X-ray diffraction results confirmed the wurtzite hexagonal structure of ZnO nanoparticles. The two intensive peaks at 160 and 432 cm-1 in the Raman Spectrum are mainly attributed to the first order modes of the wurtzite ZnO nanoparticles. The energy band gap obtained from the UV-Vis absorption spectra, shows a blue shift, which is attributed to increase in carrier concentration (Burstein Moss Effect). Photoluminescence studies of the single crystalline ZnO nanoparticles, show a strong peak centered at 385 nm, corresponding to the near band edge emission in ultraviolet range. The mixed shape of grapes, sphere, hexagonal and rock like structure has been noticed in FESEM. The results showed that PVP is a suitable capping agent for the preparation of ZnO nanoparticles by simple chemical precipitation method.

Keywords: ZnO nanoparticles, simple chemical precipitation route, mixed shape morphology, UV-visible absorption, photoluminescence, Fourier transform infra-Red spectroscopy

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Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

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Authors: Moataz Ammar, Ahdab Elmorshedy

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The distributed generation hosting capacity of a distribution network is typically limited at a given connection point by the upper voltage limit that can be violated due to the injection of active power into the distribution network. The upper voltage limit violation concern becomes more important as the network equivalent resistance increases with respect to its equivalent reactance. This paper investigates the impact of modifying the distribution network equivalent reactance at the point of connection such that the upper voltage limit is violated at a higher distributed generation penetration, than it would without the addition of series reactive compensation. The results show that series reactive compensation proves efficient in certain situations (based on the ratio of equivalent network reactance to equivalent network resistance at the point of connection). As opposed to the conventional case of capacitive compensation of a distribution network to reduce voltage drop, inductive compensation is seen to be more appropriate for alleviation of distributed-generation-induced voltage rise.

Keywords: distributed generation, distribution networks, series compensation, voltage rise

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Authors: Amine Rezzoug, Selsabil Rokia Laraba, Mourad Ancer, Said Abdi

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Composite materials are taking center stage in different fields thanks to their mechanical characteristics and their ease of preparation. Environmental constraints have led to the development of composite with natural reinforcements. The sandwich structure has the advantage to have good flexural proprieties for low density, which is why it was chosen in this work. The development of these materials is related to an energy saving strategy and environmental protection. The present work refers to the study of the development and characterization of sandwiches composites based on hybrids laminates with natural reinforcements (Alfa and Jute), a metal fabric was introduced into composite in order to have a compromise between weight and properties. We use different configurations of reinforcements (jute, metallic fabric) to develop laminates in order to use them as thin facings for sandwiches materials. While the core was an epoxy matrix reinforced with Alfa short fibers, a chemical treatment sodium hydroxide was cared to improve the adhesion of the Alfa fibers. The mechanical characterization of our materials was made by the tensile and bending test, to highlight the influence of jute and Alfa. After testing, the fracture surfaces are observed by scanning electron microscopy (SEM). Optical microscopy allowed us to calculate the degree of porosity and to observe the morphology of the individual layers. Laminates based on jute fabric have shown better results in tensile test as well as to bending, compared to those of the metallic fabric (100%, 65%). Sandwich Panels were also characterized in terms of bending test. Results we had provide, shows that this composite has sufficient properties for possible replacing conventional composite materials by considering the environmental factors.

Keywords: bending test, bio-composites, sandwiches, tensile test

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Authors: Gajaanuja Megalathan, Banuka Athuraliya

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Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes.

Keywords: arima model, ANN, crime prediction, data analysis

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Authors: Arsenio Jose Mindu

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The concern to carry out a study related to Energy Efficiency arose based on the various debates in international television networks and not only, but also in several forums of national debates. The concept of Energy Efficiency is not yet widely disseminated and /or taken into account in terms of energy consumption, not only at the domestic level but also at the industrial level in Mozambique. In the context of the energy audit, the time during which each of the appliances is connected to the voltage source, the time during which they are in standby mode was recorded on a spreadsheet basis. Based on these data, daily and monthly consumption was calculated. In order to have more accurate information on the daily levels of daily consumption, the electricity consumption was read every hour of the day (from 5:00 am to 11:00 pm), since after 23:00 the energy consumption remains constant. For ten days. Based on the daily energy consumption and the maximum consumption power, the design of the photovoltaic system for the residence was made. With the implementation of the photovoltaic system in order to guarantee energy efficiency, there was a significant reduction in the use of electricity from the public grid, increasing from approximately 17 kwh per day to around 11 kwh, thus achieving an energy efficiency of 67.4 %. That is to say, there was a reduction not only in terms of the amount of energy consumed but also of the monthly expenses with electricity, having increased from around 2,500,00Mt (2,500 meticais) to around 800Mt per month.

Keywords: energy efficiency, photovoltaic system, residential sector, Mozambique

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Yinghua Huang, Tsu-Hong Yen

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The latest advance in leadership research has revealed that leadership is co-created through the combined action of leading and following. The role of followers is as important as leaders in the leadership process. However, the previous leadership studies often conceptualize leadership as a leader-centric process, while the role of followers is largely neglected in the literature. Until recently, followership studies receives more attention because the character and behavior of followers are as vital as the leader during the leadership process. Yet, there is a dearth of followership research in the context of tourism and hospitality industries. Therefore, this study seeks to fill in the gap of knowledge and investigate the followership styles in the U.S. hospitality workforce. In particular, the objectives of this study are to identify popular followership practices among hospitality employees and evaluate hospitality employees' followership styles using Kelley’s followership typology framework. This study also compared the generational differences in followership styles among hospitality employees. According to the U.S. Bureau of Labor Statistics, the workforce in the lodging and foodservice sectors consists of around 12% baby boomers, 29% Gen Xs, 23% Gen Ys, and 36% Gen Zs in 2019. The diversity of workforce demographics in the U.S. hospitality industry calls for more attention to understand the generational differences in followership styles and organizational performance. This study conducted an in-depth interview and a questionnaire survey to collect both qualitative and quantitative data. A snowball sampling method was used to recruit participants working in the hospitality industry in the San Francisco Bay Area, California, USA. A total of 120 hospitality employees participated in this study, including 22 baby boomers, 32 Gen Xs, 30 Gen Ys, and 36 Gen Zs. 45% of the participants were males, and 55% were female. The findings of this study identified good followership practices across the multi-generational participants. For example, a Gen Y participant said that 'followership involves learning and molding oneself after another person usually an expert in an area of interest. I think of followership as personal and professional development. I learn and get better by hands-on training and experience'. A Gen X participant said that 'I can excel by not being fearful of taking on unfamiliar tasks and accepting challenges.' Furthermore, this study identified five typologies of Kelley’s followership model among the participants: 45% exemplary followers, 13% pragmatist followers, 2% alienated followers, 18% passive followers, and 23% conformist followers. The generational differences in followership styles were also identified. The findings of this study contribute to the hospitality human resource literature by identifying the multi-generational perspectives of followership styles among hospitality employees. The findings provide valuable insights for hospitality leaders to understand their followers better. Hospitality leaders were suggested to adjust their leadership style and communication strategies based on employees' different followership styles.

Keywords: followership, hospitality workforce, generational diversity, Kelley’s followership topology

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Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

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Authors: A. Alem, Y. Dahmani, A. Hadjali, A. Boualem

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Managing the problem of the conflict, either by using the Dempster-Shafer theory, or by the application of the fusion process to push researchers in recent years to find ways to get to make best decisions especially; for information systems, vision, robotic and wireless sensor networks. In this paper we are interested to take account of the conflict in the combination step that took the conflict into account and tries to manage such a way that it does not influence the decision step, the conflict what from reliable sources. According to [1], the conflict lead to erroneous decisions in cases where was with strong degrees between sources of information, if the conflict is more than the maximum of the functions of belief mass K > max1...n (mi (A)), then the decision becomes impossible. We will demonstrate in this paper that the multiplication of mass functions by coefficients of reliability is a decreasing function; it leads to the reduction of conflict and a good decision. The definition of reliability coefficients accurately and multiply them by the mass functions of each information source to resolve the conflict and allow deciding whether the degree of conflict. The evaluation of this technique is done by a use case; a comparison of the combination of springs with a maximum conflict without, and with reliability coefficients.

Keywords: Dempster-Shafer theory, fusion process, conflict managing, reliability factors, decision

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Authors: Amit Verma, Simranjeet Kaur, Sandeep Kaur

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Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.

Keywords: test case prioritization, classification, artificial neural networks, TF-IDF

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Authors: Muhammad Rehan Usman, Muhammad Arslan Usman, Soo Young Shin

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The new era of digital communication has brought up many challenges that network operators need to overcome. The high demand of mobile data rates require improved networks, which is a challenge for the operators in terms of maintaining the quality of experience (QoE) for their consumers. In live video transmission, there is a sheer need for live surveillance of the videos in order to maintain the quality of the network. For this purpose objective algorithms are employed to monitor the quality of the videos that are transmitted over a network. In order to test these objective algorithms, subjective quality assessment of the streamed videos is required, as the human eye is the best source of perceptual assessment. In this paper we have conducted subjective evaluation of videos with varying spatial and temporal impairments. These videos were impaired with frame freezing distortions so that the impact of frame freezing on the quality of experience could be studied. We present subjective Mean Opinion Score (MOS) for these videos that can be used for fine tuning the objective algorithms for video quality assessment.

Keywords: frame freezing, mean opinion score, objective assessment, subjective evaluation

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Kholod Khairy Salama, Shabban Hassan Thabet

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In the present study, a comprehensive investigation has been undertaken into an Egyptian mural painting with feet wear slippers approach to choose the most successful restoration methods. The mural painting under examination dates back to the Amarna period; it was detached from a wall of an unknown tomb in Egypt, and currently, it is initially displayed in a showcase at the Egyptian Museum – Tahrir Square – Cairo, Egypt. The main objectives of this research were to (a) reveal the pigment used in the mural painting, (b) reveal the medium used with colours, (c) determine the technique of manufacturing, (e) determine the ground support, and (f) reveal the main deterioration aspects. The analytical techniques used for investigation were Optical Microscopy, Raman, X-ray Florescence, X-ray diffraction, and Fourier transform infrared coupled with attenuated total reflectance “FTIR-ATR”. The investigation revealed that the vital deterioration factors affecting the object. This research aims to examine and analyze the mural painting to choose the suitable method for the restoration process (a) define the colours through comparative analysis to choose the suitable material for cleaning, (b) define the natural structure of the ground support layer, which appeared as mud layer (c) determine the medium used with colours (d) diagnosis the presence of the white wash layer, and (e) choose the suitable restoration methods according to the results. Conclusion: This study focused mainly on the physical and chemical properties of the mural painting compound and the main changes that happened to the mural painting material, which caused deterioration and fall down of the painting parts, so we can find the best and optimum restoration ways for this object.

Keywords: mural paintings, Tal Al-Amarna, digital microscope, Raman, XRF, XRD, FTIR

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Zhou Mo, Dennis Chow

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In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing protocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turn out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Shraddha B. Rai, Mahipatsinh D. Chavda, Bharat S. Trivedi

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The aim of the present research is to study the effect of education and social belonging on well-being of youth. Well-being is one of the most important aspects of human being and the state of well-being can be attained in terms of healthy body with healthy mind. Well-being has been defined as encompassing people’s cognitive and affective evaluations of their lives. Well-being has been interchangeably used with health and quality of life. According to the WHO, the main determinants of health include the social, economic, and the physical environment and the persons individual characteristics and behaviors. WHO lists other factors that can influence the well-being of a person such as the gender, education, social support networks and health services. The main objective of the present investigation is to know the effect of education and social belonging on well-being of youth. The sample of 180 students belonging to Gujarati and English (convent) culture were selected randomly from Guajarati and English (convent) schools of Ahmedabad City of Gujarat (India). General well-being Scale by Dr. Ashok Kalia and Ms. Anita Deswal was administered to measure the Physical, Emotional, and Social and school well-being. The result shows that there is significant different found between Gujarati and English (convent) culture on Well-being in school students. SES is also affect significantly to wellbeing of students.

Keywords: culture, SES, well-being, health, quality of life

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Authors: Marcelo Pereira Introini

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The global value chains (GVC) approach contributes to a better understanding of the international production organization amid globalization’s second unbundling from the 1970s on. Mainly due to the tools that help to understand the importance of critical competences, technological capabilities, and functions performed by each player, GVC research flourished in recent years, rooted in discussing the possibilities of integration and repositioning along regional and global value chains. Regarding this context, part of the literature endorsed a more optimistic view that engaging in fragmented production networks could represent learning opportunities for developing countries’ firms, since the relationship with transnational corporations could allow them build skills and competences. Increasing recognition that GVCs are based on asymmetric power relations provided another sight about benefits, costs, and development possibilities though. Once leading companies tend to restrict the replication of their technologies and capabilities by their suppliers, alternative strategies beyond the functional specialization, seen as a way to integrate value chains, began to be broadly highlighted. This paper organizes a coherent narrative about the shortcomings of the GVC analytical framework, while recognizing its multidimensional contributions and recent developments. We adopt two different and complementary perspectives to explore the idea of integration in the international production. On one hand, we emphasize obstacles beyond production components, analyzing the role played by intangible assets and intellectual property regimes. On the other hand, we consider the importance of domestic production and innovation systems for technological development. In order to provide a deeper understanding of the restrictions on technological learning of developing countries’ firms, we firstly build from the notion of intellectual monopoly to analyze how flagship companies can prevent subordinated firms from improving their positions in fragmented production networks. Based on intellectual property protection regimes we discuss the increasing asymmetries between these players and the decreasing access of part of them to strategic intangible assets. Second, we debate the role of productive-technological ecosystems and of interactive and systemic technological development processes, as concepts of the Innovation Systems approach. Supporting the idea that not only endogenous advantages are important for international competition of developing countries’ firms, but also that the building of these advantages itself can be a source of technological learning, we focus on local efforts as a crucial element, which is not replaceable for technology imported from abroad. Finally, the paper contributes to the discussion about technological development as a two-dimensional dynamic. If GVC analysis tends to underline a company-based perspective, stressing the learning opportunities associated to GVC integration, historical involvement of national States brings up the debate about technology as a central aspect of interstate disputes. In this sense, technology is seen as part of military modernization before being also used in civil contexts, what presupposes its role for national security and productive autonomy strategies. From this outlook, it is important to consider it as an asset that, incorporated in sophisticated machinery, can be the target of state policies besides the protection provided by intellectual property regimes, such as in export controls and inward-investment restrictions.

Keywords: global value chains, innovation systems, intellectual monopoly, technological development

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Authors: Jordan Georgiev Deliversky

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Fight against migrant smuggling has been put as a priority issues at the European Union policy agenda for more than a decade. The trafficked person, who has been targeted as the object of criminal exploitation, is specifically unique for human trafficking. Generally, the beginning of human trafficking activities is related to profit from the victim’s exploitation. The objective of this paper is to present measures that could result in the limitation of corruption mainly through analyzing the existing legislation framework against corruption in Europe. The analysis is focused on exploring the multiple origins of factors influencing migration processes in Europe, as corruption could be characterized as one of the most significant reasons for refugees to flee their countries. The main results show that law enforcement must turn the focus on the financing of the organized crime groups that are involved in migrant smuggling activities. Corruption has a significant role in managing smuggling operations and in particular when criminal organizations and networks are involved. Illegal migrants and refugees usually represent significant sources of additional income for officials involved in the process of boarding protection and immigration control within the European Union borders.

Keywords: corruption, influence, human smuggling, legislation, migration

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Authors: Ahasanul Haque, Abdullah Sarwar, Khaliq Ahmed

Abstract:

Networking is important among students to achieve better understanding. Social networking plays an important role in the education. Realizing its huge potential, various organizations, including institutions of higher learning have moved to the area of social networks to interact with their students especially through Facebook. Therefore, measuring the effectiveness of Facebook as a learning tool has become an area of interest to academicians and researchers. Therefore, this study tried to integrate and propose new theoretical and empirical evidences by linking the western idea of adopting Facebook as an alternative learning platform from a Malaysian perspective. This study, thus, aimed to fill a gap by being among the pioneering research that tries to study the effectiveness of adopting Facebook as a learning platform across other cultural settings, namely Malaysia. Structural equation modelling was employed for data analysis and hypothesis testing. This study findings have provided some insights that would likely affect students’ awareness towards using Facebook as an alternative learning platform in the Malaysian higher learning institutions. At the end, future direction is proposed.

Keywords: Learning Management Tool, social networking, education, Malaysia

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Authors: Anthony Credoz, Nathalie Nief, Remy Hedacq, Salvador Jordana, Laurent Cazes

Abstract:

Groundwater monitoring is classically performed in a network of piezometers in industrial sites. Groundwater flow parameters, such as direction, sense and velocity, are deduced from indirect measurements between two or more piezometers. Groundwater sampling is generally done on the whole column of water inside each borehole to provide concentration values for each piezometer location. These flow and concentration values give a global ‘static’ image of potential plume of contaminants evolution in the shallow aquifer with huge uncertainties in time and space scales and mass discharge dynamic. TOTAL R&D Subsurface Environmental team is challenging this classical approach with an innovative dynamic way of characterization of shallow aquifer groundwater. The current study aims at optimizing the tools and methodologies for (i) a direct and multilevel measurement of groundwater velocities in each piezometer and, (ii) a calculation of potential flux of dissolved contaminant in the shallow aquifer. Lab-scale experiments have been designed to test commercial and R&D tools in a controlled sandbox. Multiphysics modeling were performed and took into account Darcy equation in porous media and Navier-Stockes equation in the borehole. The first step of the current study focused on groundwater flow at porous media/piezometer interface. Huge uncertainties from direct flow rate measurements in the borehole versus Darcy flow rate in the porous media were characterized during experiments and modeling. The structure and location of the tools in the borehole also impacted the results and uncertainties of velocity measurement. In parallel, direct-push tool was tested and presented more accurate results. The second step of the study focused on mass flux of dissolved contaminant in groundwater. Several active and passive commercial and R&D tools have been tested in sandbox and reactive transport modeling has been performed to validate the experiments at the lab-scale. Some tools will be selected and deployed in field assays to better assess the mass discharge of dissolved contaminants in an industrial site. The long-term subsurface environmental strategy is targeting an in-situ, real-time, remote and cost-effective monitoring of groundwater.

Keywords: dynamic characterization, groundwater flow, lab-scale, mass flux

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Authors: Aliaa M. S. Salem, Soliman I. El-Hout, Amira Gaber, Hassan Nageh

Abstract:

Nowadays, the development of poisonous gas detectors is considered to be an urgent matter to secure human health and the environment from poisonous gases, in view of the fact that even a minimal amount of poisonous gas can be fatal. Of these concerns, various inorganic or organic sensing materials have been used. Among these are conducting polymers, have been used as the active material in the gassensorsdue to their low-cost,easy-controllable molding, good electrochemical properties including facile fabrication process, inherent physical properties, biocompatibility, and optical properties. Moreover, conducting polymer-based chemical sensors have an amazing advantage compared to the conventional one as structural diversity, facile functionalization, room temperature operation, and easy fabrication. However, the low selectivity and conductivity of conducting polymers motivated the doping of it with varied materials, especially graphene, to enhance the gas-sensing performance under ambient conditions. There were a number of approaches proposed for producing polymer/ graphene nanocomposites, including template-free self-assembly, hard physical template-guided synthesis, chemical, electrochemical, and electrospinning...etc. In this work, we aim to prepare a novel gas sensordepending on Electrospun nanofibers of conducting polymer/RGO composite that is the effective and efficient expectation of poisonous gases like ammonia, in different application areas such as environmental gas analysis, chemical-,automotive- and medical industries. Moreover, our ultimate objective is to maximize the sensing performance of the prepared sensor and to check its recovery properties.

Keywords: electro spinning process, conducting polymer, polyaniline, polypyrrole, polythiophene, graphene oxide, reduced graphene oxide, functionalized reduced graphene oxide, spin coating technique, gas sensors

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Authors: Ali Aghazadegan, Ali Shokria, Julia Mullarneya, Jon Tunnicliffe

Abstract:

River channel confluences are dynamic systems with branching structures that exhibit a high degree of complexity both in natural and man-made open channel networks. Recent and past fields and modeling have investigated the river dynamics modeling of confluent based on a series of over-simplified assumptions (i.e. straight tributary channel with a bend with a 90° junction angle). Accurate assessment of such systems is important to the design and management of hydraulic structures and river engineering processes. Despite their importance, there has been little study of the hydrodynamics characteristics of river confluences, and the link between flow hydrodynamics and confluence morphodynamics in the confluence is still incompletely understood. This paper studies flow structures in confluences, morphodynamics and deposition patterns in 30 and 90 degrees confluences with different flow conditions. The results show that the junction angle is primarily the key factor for the determination of the confluence bed morphology and sediment pattern, while the discharge ratio is a secondary factor. It also shows that super elevation created by mixing flows is a key function of the morphodynamics patterns.

Keywords: helical flow, river confluence, bed morphology , secondary flows, shear layer

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Authors: Rui Wang, Luodi Yu, Dan Huang, Hsuan-Chih Chen, Yang Zhang, Suiping Wang

Abstract:

Enhanced discrimination of pure tones but diminished discrimination of speech pitch (i.e., lexical tone) were found in children with autism who speak a tonal language (Mandarin), suggesting a speech-specific impairment of pitch perception in these children. However, in tonal languages, both lexical tone and vowel are phonemic cues and integrally dependent on each other. Therefore, it is unclear whether the presence of phonemic vowel dimension contributes to the observed lexical tone deficits in Mandarin-speaking children with autism. The current study employed a multi-feature oddball paradigm to examine how vowel and tone dimensions contribute to the neural responses for syllable change detection and involuntary attentional orienting in school-age Mandarin-speaking children with autism. In the oddball sequence, syllable /da1/ served as the standard stimulus. There were three deviant stimulus conditions, representing tone-only change (TO, /da4/), vowel-only change (VO, /du1/), and change of tone and vowel simultaneously (TV, /du4/). EEG data were collected from 25 children with autism and 20 age-matched normal controls during passive listening to the stimulation. For each deviant condition, difference waveform measuring mismatch negativity (MMN) was derived from subtracting the ERP waveform to the standard sound from that to the deviant sound for each participant. Additionally, the linear summation of TO and VO difference waveforms was compared to the TV difference waveform, to examine whether neural sensitivity for TV change detection reflects simple summation or nonlinear integration of the two individual dimensions. The MMN results showed that the autism group had smaller amplitude compared with the control group in the TO and VO conditions, suggesting impaired discriminative sensitivity for both dimensions. In the control group, amplitude of the TV difference waveform approximated the linear summation of the TO and VO waveforms only in the early time window but not in the late window, suggesting a time course from dimensional summation to nonlinear integration. In the autism group, however, the nonlinear TV integration was already present in the early window. These findings suggest that speech perception atypicality in children with autism rests not only in the processing of single phonemic dimensions, but also in the dimensional integration process.

Keywords: autism, event-related potentials , mismatch negativity, speech perception

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