Search results for: micro data

Authors: M. Palizvan, M. T. Abadi, M. H. Sadr

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Due to variations in damage mechanisms in the microscale, the behavior of fiber-reinforced composites is nonlinear and difficult to model. To make use of computational advantages, homogenization method is applied to the micro-scale model in order to minimize the cost at the expense of detail of local microscale phenomena. In this paper, the effective stiffness is calculated using the homogenization of nonlinear behavior of a composite representative volume element (RVE) containing fiber-matrix debonding. The damage modes for the RVE are considered by using cohesive elements and contacts for the cohesive behavior of the interface between fiber and matrix. To predict more realistic responses of composite materials, different random distributions of fibers are proposed besides square and hexagonal arrays. It was shown that in some cases, there is quite different damage behavior in different fiber distributions. A comprehensive comparison has been made between different graphs.

Keywords: homogenization, cohesive zone model, fiber-matrix debonding, RVE

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Authors: Benchalak Muangmeesri

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Glazes ceramics are ceramic materials produced through controlled crystallization of a parent glass. The great variety of compositions and the possibility of developing special micro structures with specific technological properties have allowed glass ceramic materials to be used in a wide range of applications. At the same time, glazes ceramics need to improvement in the mechanical and chemical properties of glazed. The pick and place station is equipped with a three-axis module. test piece housings placed on the vacuum are detected module picks up a test piece insert from the slide and places it on the test piece housing. Overall, glazes ceramics are compared with automatically and manually of speed and position control. The handling modules of automatic transfer are a new generation of high speed and precision then these color results from absorption and thickness than manual is also included.

Keywords: glaze, PID control, pick and place, ceramic

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Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Aziz Ibrahim Abdulla, Omar Mohanad Mahdi

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The objective of this paper is to explore the effect of incorporating Nano-Silica with Silica-fume in ferrocement mortar to enhancing mechanical properties of it. One type of Nano silica with average diameter size 23nm and silica fume have been used with two percentage (1%, 2% Nano silica and 5%, 10% silica fume per weight of cement) and w/c with / without superplasticizer was been calculated by flow test method. Also three sand: cement ratios have been used (1.5, 2.0 and 2.5) with max. Aggregate size 0.6mm in this study for reference and other mixtures. Results reveal adding Nano silica with silica fume to ferrocement mortar enhances its physical and mechanical properties such as compressive strength and flexural strength. The SEM pictures and density with absorption ratio demonstrate that Nano silica with silica fume contributes to enhancement of mortar through yielding denser, more compact and uniform mixtures.

Keywords: nano silica, ferrocement mortar, compresion strength, flexural strength

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Authors: Sh. Minapoor, S. Ajeli, M. Javadi Toghchi

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Non-crimp 3D orthogonal fabric composite is one of the textile-based composite materials that are rapidly developing light-weight engineering materials. The present paper focuses on geometric and micro mechanical modeling of non-crimp 3D orthogonal carbon fabric and composites reinforced with it for aerospace applications. In this research meso-finite element (FE) modeling employs for stress analysis in different load conditions. Since mechanical testing of expensive textile carbon composites with specific application isn't affordable, simulation composite in a virtual environment is a helpful way to investigate its mechanical properties in different conditions.

Keywords: woven composite, aerospace applications, finite element method, mechanical properties

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Authors: Sudhir Kumar Sharma, Abiy D. Woldetsadik, Mazin Magzoub, Ramesh Jagannathan

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It is well known that bioactive ceramics exhibit specific biological affinities, especially in the area of tissue re-generation. In this context, we report the development of an eminently scalable, novel, supercritical CO₂ based process for the fabrication of hierarchically porous 'soft' CaCO₃ scaffolds. Porosity at the macro, micro, and nanoscales was obtained through process optimization of the so-called 'coffee-ring effect'. Exposure of these CaCO₃ scaffolds to monocytic THP-1 cells yielded negligible levels of tumor necrosis factor-alpha (TNF-α) thereby confirming the lack of immunogenicity of the scaffolds. ECM protein treatment of the scaffolds showed enhanced adsorption comparable to standard control such as glass. In vitro studies using osteoblast precursor cell line, MC3T3, also demonstrated the cytocompatibility of hierarchical porous CaCO₃ scaffolds.

Keywords: supercritical CO2, CaCO3 scaffolds, coffee-ring effect, ECM proteins

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Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: data analytics, green production, industrial energy management, optimization, renewable energies, simulation

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Authors: K. Umbleja, M. Ichino, H. Yaguchi

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In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

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Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: Abdelrazzag Elmajdoub

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The present study was designed to investigate the bio-equivalence of doxycycline in Dolistin® and Colidox® at a dose rate of 10 mg doxycycline/kg of body weight in 48 clinically normal broiler chickens. After oral administration, plasma levels of doxycycline peaked after 2 hours post-dosing without significant differences between the two products and it could be detected therapeutically and exceeded the minimum inhibitory concentration (MIC) for most micro-organisms sensitive to doxycycline for 12 hours. The disposition kinetics of doxycycline in the two products following oral administration revealed that the maximum plasma concentrations (Cmax.) were 22.65 and 21.80 µg/ml and attained at (Tmax.) 2.10 and 2.20 hours, respectively. Doxycycline in both of the products was eliminated with half- lives (t0.5α) equal to 7.70 and 6.93 hours, respectively. The mean systemic bio availabilities of doxycycline in both of the products after oral administration in chickens were 80.60 and 79.70%, respectively. It was concluded that doxycycline in the form of Dolistin® and Colidox® needs a dose equivalent to 20 mg doxycycline/kg of body weight a day is better to keep the plasma concentration higher than the MIC.

Keywords: tetracyclines, doxycycline, bioavailability, broilers, chickens

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Authors: Fatima Zohra Bissaad, Farid Bounaceur, Nassima Behidj, Nadjiba Chebouti, Fatma Halouane, Bahia Doumandji-Mitiche

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Effect of Beauveria bassiana and Metarhizium anisopliae var. acridum on the 5^th instar nymphs of Schistocerca gregaria was studied in the laboratory. Infection by these both entomopathogenic fungi caused reduction in the hemolymph total protein. The average amounts of total proteins were 2.3, 2.07, 2.09 µg/100 ml of haemolymph in the control and M. anisopliae var. acridum, and B. bassiana based-treatments, respectively. Three types of haemocytes were recognized and identified as prohaemocytes, plasmatocytes and granulocytes. The treatment caused significant reduction in the total haemocyte count and in each haemocyte type on the 9^th day after its application.

Keywords: Beauveria bassiana, haemolymph picture, haemolymph protein, Metarhizium anisopliae var. acridum, Schistocerca gregaria

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Adam Janásek, Marek Pagáč

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The article is looking for an experimental determination of tool life tests for ceramic cutting inserts. Mentioned experimental determination should provide an added information about cutting process. The mechanism of tool wear, cutting temperature in machining, quality machined surface and machining process itself is the information, which are important for whole manufacturing process. Mainly, the roughness plays very important role in determining how a real object will interact with its environment. The main aim was to determine the number of machined inserts, tool life and micro-geometry, as well. On the basis of previous tests the tool-wear was measured at constant cutting parameter which is more typical for high volume manufacturing processes.

Keywords: ceramic, insert, machining, surface roughness, tool-life, tool-wear

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Vijay Korra

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The main focus of this paper is to examine the performance, outcomes and impacts of MGNREGA Scheme in particular on migrant beneficiary households. This article is based on a field survey carried out in 2010 in three randomly selected villages in Mahabubnagar district of Telangana State, India. It was found that majority of the job card holders are only able to get employment/work between 30-60 days and receive wages maximum between Rs.60 to 70 per day wherein wage discrimination was prevalent in line with gender. It concludes by saying that the government sponsored employment programme has indeed given rural poor a sense of hope about livelihood security through guaranteed employment. On the other hand, the scheme is defected in providing full employment days, wages, and thus unable to prevent the working class from migrating to cities/towns in search of employment mainly due to malpractices involved in the implementation of the scheme.

Keywords: MGNREGA(s), labour, employment, wages, livelihood, seasonal migration

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Authors: Sylvia Chan-Olmsted, Lisa-Charlotte Wolter

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With the continuous proliferation of media platforms, there have been tremendous changes in media consumption behaviors. From the perspective of sports sponsorship, while there is now a multitude of platforms to create brand associations, the changing media landscape and shift of message control also mean that sports sponsors will have to take into account the nature of and consumer responses toward these emerging digital media to devise effective marketing strategies. Utilizing the personal interview methodology, this study is qualitative and exploratory in nature. A total of 18 experts from European and American academics, sports marketing industry, and sports leagues/teams were interviewed to address three main research questions: 1) What are the major changes in digital technologies that are relevant to sports sponsorship; 2) How have digital media influenced the channels and platforms of sports sponsorship; and 3) How have these technologies affected the goals, strategies, and measurement of sports sponsorship. The study found that sports sponsorship has moved from consumer engagement, engagement measurement, and consequences of engagement on brand behaviors to micro-targeting one on one, engagement by context, time, and space, and activation and leveraging based on tracking and databases. From the perspective of platforms and channels, the use of mobile devices is prominent during sports content consumption. Increasing multiscreen media consumption means that sports sponsors need to optimize their investment decisions in leagues, teams, or game-related content sources, as they need to go where the fans are most engaged in. The study observed an imbalanced strategic leveraging of technology and digital infrastructure. While sports leagues have had less emphasis on brand value management via technology, sports sponsors have been much more active in utilizing technologies like mobile/LBS tools, big data/user info, real-time marketing and programmatic, and social media activation. Regardless of the new media/platforms, the study found that integration and contextualization are the two essential means of improving sports sponsorship effectiveness through technology. That is, how sponsors effectively integrate social media/mobile/second screen into their existing legacy media sponsorship plan so technology works for the experience/message instead of distracting fans. Additionally, technological advancement and attention economy amplify the importance of consumer data gathering, but sports consumer data does not mean loyalty or engagement. This study also affirms the benefit of digital media as they offer viral and pre-event activations through storytelling way before the actual event, which is critical for leveraging brand association before and after. That is, sponsors now have multiple opportunities and platforms to tell stories about their brands for longer time period. In summary, digital media facilitate fan experience, access to the brand message, multiplatform/channel presentations, storytelling, and content sharing. Nevertheless, rather than focusing on technology and media, today’s sponsors need to define what they want to focus on in terms of content themes that connect with their brands and then identify the channels/platforms. The big challenge for sponsors is to play to the venues/media’s specificity and its fit with the target audience and not uniformly deliver the same message in the same format on different platforms/channels.

Keywords: digital media, mobile media, social media, technology, sports sponsorship

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Authors: Tiffany Baetens, Sophie Halliez, Luc Buée, Emiliano Pallecchi, Vincent Thomy, Steve Arscott

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We demonstrate here a viable stretchable bio-microelectromechanical systems (BioMEMS) technology for use with biological studies concerned with the effect of high mechanical strains on living cells. An example of this is traumatic brain injury (TBI) where neurons are damaged with physical force to the brain during, e.g., accidents and sports. Robust, miniaturized integrated systems are needed by biologists to be able to study the effect of TBI on neuron cells in vitro. The major challenges in this area are (i) to develop micro, and nanofabrication processes which are based on stretchable substrates and to (ii) create systems which are robust and performant at very high mechanical strain values—sometimes as high as 100%. At the time of writing, such processes and systems were rapidly evolving subject of research and development. The BioMEMS which we present here is composed of an elastomer substrate (low Young’s modulus ~1 MPa) onto which is patterned robust electrodes and insulators. The patterning of the thin films is achieved using standard photolithography techniques directly on the elastomer substrate—thus making the process generic and applicable to many materials’ in based systems. The chosen elastomer used is commercial ‘Sylgard 184’ polydimethylsiloxane (PDMS). It is spin-coated onto a silicon wafer. Multistep ultra-violet based photolithography involving commercial photoresists are then used to pattern robust thin film metallic electrodes (chromium/gold) and insulating layers (parylene) on the top of the PDMS substrate. The thin film metals are deposited using thermal evaporation and shaped using lift-off techniques The BioMEMS has been characterized mechanically using an in-house strain-applicator tool. The system is composed of 12 electrodes with one reference electrode transversally-orientated to the uniaxial longitudinal straining of the system. The electrical resistance of the electrodes is observed to remain very stable with applied strain—with a resistivity approaching that of evaporated gold—up to an interline strain of ~50%. The mechanical characterization revealed some interesting original properties of such stretchable BioMEMS. For example, a Poisson effect induced electrical ‘self-healing’ of cracking was identified. Biocompatibility of the commercial photoresist has been studied and is conclusive. We will present the results of the BioMEMS, which has also characterized living cells with a commercial Multi Electrode Array (MEA) characterization tool (Multi Channel Systems, USA). The BioMEMS enables the cells to be strained up to 50% and then characterized electrically and optically.

Keywords: BioMEMS, elastomer, electrical impedance measurements of living cells, high mechanical strain, microfabrication, stretchable systems, thin films, traumatic brain injury

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Authors: Reza Hadjiaghaie Vafaie, Aysan Madanpasandi, Behrooz Zare Desari, Seyedmohammad Mousavi

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High lamination in microchannel is one of the main challenges in on-chip components like micro total analyzer systems and lab-on-a-chips. Electro-osmotic force is highly effective in chip-scale. This research proposes a microfluidic-based micropump for low ionic strength solutions. Narrow microchannels are designed to generate an efficient electroosmotic flow near the walls. Microelectrodes are embedded in the lateral sides and actuated by low electric potential to generate pumping effect inside the channel. Based on the simulation study, the fluid velocity increases by increasing the electric potential amplitude. We achieve a net flow velocity of 100 µm/s, by applying +/- 2 V to the electrode structures. Our proposed low voltage design is of interest in conventional lab-on-a-chip applications.

Keywords: integration, electrokinetic, on-chip, fluid pumping, microfluidic

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Yusuf Emirov, Abdullatif Hakami, Prasanta K Biswas, Elias K Stefanakos, Sesha S Srinivasan

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This study is aimed to develop microencapsulated thermochromic materials and the analysis of core-shell formation using high resolution electron microscopy. The candidate metal oxides (e.g., titanium oxide and silicon oxide) used for the microencapsulation of thermochromic materials are based on the microemulsion route that involves the micelle formation using different surfactants. The effectiveness of the core-shell microstructure formationrevealed the influence of surfactants and the metal oxide precursor concentrations. Additionally, a detailed thermal and color chromic behavior of these core-shell microcapsules are evaluated with the pristine thermochromic dye particles.

Keywords: core-shell thermochromic materials, core-shell microstructure formation, thermal and color chromic behavior of core-shell microcapsules, development micro-capsulated thermochromic materials

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Aliro Villacorta, Laura Rubio, Mohamed Alaraby, Montserrat López Mesas, Victor Fuentes-Cebrian, Oscar H. Moriones, Ricard Marcos, Alba Hernández.

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Micro and nano plastics (MNPLs) are emergent environmental pollutants requiring urgent information on their potential risks to human health. One of the problems associated with the evaluation of their undesirable effects is the lack of real samples matching those resulting from the environmental degradation of plastic wastes. To such end, we propose an easy method to obtain polyethylene terephthalate nano plastics from water plastic bottles (PET-NPLs) but, in principle, applicable to any other plastic goods sources. An extensive characterization indicates that the proposed process produces uniform samples of PET-NPLs of around 100 nm, as determined by using a multi-angle and dynamic light scattering methodology. An important point to be highlighted is that to avoid the metal contamination resulting from methods using metal blades/burrs for milling, trituration, or sanding, we propose to use diamond burrs to produce metal-free samples. To visualize the toxicological profile of the produced PET-NPLs, we have evaluated their ability to be internalized by cells, their cytotoxicity, and their ability to induce oxidative stress and induce DNA damage. In this preliminary approach, we have detected their cellular uptake, but without the induction of significant biological effects. Thus, no relevant increases in toxicity, reactive oxygen species (ROS) induction, or DNA damage -as detected with the comet assay- have been observed. The use of real samples, as produced in this study, will generate relevant data in the discussion about the potential health risks associated with MNPLs exposures.

Keywords: nanoplastics, polyethylene terephthalate, physicochemical characterization, cell uptake, cytotoxicity

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Authors: Raffaele Danilo Esposito, Domingo Planes Meseguer, Maria Del Pilar Dorado Rodriguez

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As far as we know, it is still unavailable a commercial solution which would allow to manage, openly and configurable up to user needs, the huge amount of data generated in a modern Radiation Oncology Department. Currently, available information management systems are mainly focused on Record & Verify and clinical data, and only to a small extent on physical data. Thus, results in a partial and limited use of the actually available information. In the present work we describe the implementation at our department of a centralized information management system based on a web server. Our system manages both information generated during patient planning and treatment, and information of general interest for the whole department (i.e. treatment protocols, quality assurance protocols etc.). Our objective it to be able to analyze in a simple and efficient way all the available data and thus to obtain quantitative evaluations of our treatments. This would allow us to improve our work flow and protocols. To this end we have implemented a relational data base which would allow us to use in a practical and efficient way all the available information. As always we only use license free software.

Keywords: information management system, radiation oncology, medical physics, free software

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Authors: Komol Phaisarn, Natcha Wattanaprapa

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This research is a survey research with an objective to study the safety of data storage devices of graduate students of academic year 2013, Suan Sunandha Rajabhat University. Data were collected by questionnaire on the safety of data storage devices according to CIA principle. A sample size of 81 was drawn from population by purposive sampling method. The results show that most of the graduate students of academic year 2013 at Suan Sunandha Rajabhat University use handy drive to store their data and the safety level of the devices is at good level.

Keywords: security, safety, storage devices, graduate students

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Authors: F. Karim

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In this paper, triangular lattice index-guiding photonic crystal fibers (PCFs) are synthesized to compensate the chromatic dispersion of a single mode fiber (SMF-28) for an 80 km optical link operating at 1.55 µm, by using the directed tabu search algorithm. Hole-to-hole distance, circular air-hole diameter, solid-core diameter, ring number and PCF length parameters are optimized for this purpose. Three Synthesized PCFs with different physical parameters are compared in terms of their objective functions values, residual dispersions and compensation ratios.

Keywords: triangular lattice index-guiding photonic crystal fiber, dispersion compensation, directed tabu search, synthesis

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Authors: Kaddi Mohammed, A. Benatiallah, D. Benatiallah

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Data grid is a technology that has full emergence of new challenges, such as the heterogeneity and availability of various resources and geographically distributed, fast data access, minimizing latency and fault tolerance. Researchers interested in this technology address the problems of the various systems related to the industry such as task scheduling, load balancing and replication. The latter is an effective solution to achieve good performance in terms of data access and grid resources and better availability of data cost. In a system with duplication, a coherence protocol is used to impose some degree of synchronization between the various copies and impose some order on updates. In this project, we present an approach for placing replicas to minimize the cost of response of requests to read or write, and we implement our model in a simulation environment. The placement techniques are based on a cost model which depends on several factors, such as bandwidth, data size and storage nodes.

Keywords: response time, query, consistency, bandwidth, storage capacity, CERN

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Authors: Rwei-Ching Chang, Bo-Yu Su

Abstract:

Adhesion of copper thin films deposited on polyethylene terephthAdhesion of copper thin films deposited on polyethylene terephthalate substrate by direct current sputtering with different sputtering parameters is discussed in this work. The effects of plasma treatment with 0, 5, and 10 minutes on the thin film properties are investigated first. Various argon flow rates at 40, 50, 60 standard cubic centimeters per minute (sccm), deposition power at 30, 40, 50 W, and film thickness at 100, 200, 300 nm are also discussed. The 3-dimensional surface profilometer, micro scratch machine, and optical microscope are used to characterize the thin film properties. The results show that the increase of the plasma treatment time on the polyethylene terephthalate surface affects the roughness and critical load of the films. The critical load increases as the plasma treatment time increases. When the plasma treatment time was adjusted from 5 minutes to 10 minutes, the adhesion increased from 8.20 mN to 13.67 mN. When the argon flow rate is decreased from 60 sccm to 40 sccm, the adhesion increases from 8.27 mN to 13.67 mN. The adhesion is also increased by the condition of higher power, where the adhesion increased from 13.67 mN to 25.07 mN as the power increases from 30 W to 50 W. The adhesion of the film increases from 13.67 mN to 21.41mN as the film thickness increases from 100 nm to 300 nm. Comparing all the deposition parameters, it indicates the change of the power and thickness has much improvement on the film adhesion.alate substrate by direct current sputtering with different sputtering parameters is discussed in this work. The effects of plasma treatment with 0, 5, and 10 minutes on the thin film properties are investigated first. Various argon flow rates at 40, 50, 60 standard cubic centimeters per minute (sccm), deposition power at 30, 40, 50 W, and film thickness at 100, 200, 300 nm are also discussed. The 3-dimensional surface profilometer, micro scratch machine, and optical microscope are used to characterize the thin film properties. The results show that the increase of the plasma treatment time on the polyethylene terephthalate surface affects the roughness and critical load of the films. The critical load increases as the plasma treatment time increases. When the plasma treatment time was adjusted from 5 minutes to 10 minutes, the adhesion increased from 8.20 mN to 13.67 mN. When the argon flow rate is decreased from 60 sccm to 40 sccm, the adhesion increases from 8.27 mN to 13.67 mN. The adhesion is also increased by the condition of higher power, where the adhesion increased from 13.67 mN to 25.07 mN as the power increases from 30 W to 50 W. The adhesion of the film increases from 13.67 mN to 21.41mN as the film thickness increases from 100 nm to 300 nm. Comparing all the deposition parameters, it indicates the change of the power and thickness has much improvement on the film adhesion.

Keywords: flexible substrate, sputtering, adhesion, copper thin film

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Authors: Siqin Wang, Jonathan Corcoran, Yan Liu, Thomas Sigler

Abstract:

Australia is a quintessentially immigrant nation with 28 percent of its residents being foreign-born. By 2011, People’s Republic of China (PRC) overtook the United Kingdom to become the largest source country in Australia. Significantly, the profile of PRC-born migrants has changed to mirror broader global shifts towards high-skilled labour, education-related, and investment-focussed migration, all of which reflect an increasing trend in the mobility of wealthy and/or educated cohorts. Together, these coalesce to form a more complex pattern of migrant settlement –both spatially and socio-economically. This paper focuses on the PRC-born migration, redresses these lacunae, with regard to the settlement outcomes of PRC migrants to Australia, with a particular focus on spatial evolution and residential mobility at both the metropolitan and national scales. By drawing on Census Data and migration Micro Datasets, the aim of this paper is to examine the shifting dynamics of PRC-born migrants in Australian capital cities to unveil their socioeconomic characteristics, residential patterns and change of spatial concentrations during their transition into the new host society. This paper finds out three general patterns in the residential evolution of PRC-born migrants depending on the size of capital cities where they settle down, as well as the association of socio-economic characters with the formation of enclaves. It also examines the residential mobility across states and cities from 2001 to 2011 indicating the rising status of median-size Australian capital cities for receiving PRC-born migrants. The paper concludes with a discussion of evidences for policy formation, facilitates the effective transition of PRC-born populations into the mainstream of host society and enhances social harmony to help Australia become a more successful multicultural nation.

Keywords: Australia, Chinese migrants, residential mobility, spatial evolution

Procedia PDF Downloads 231