Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH₄/O₂ ratio and diluents gas (mol% N₂) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na₂WO₄/SiO₂ catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C₂ products selectivity, C2 yielding and C₂H₄/C₂H₆ ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C₂ was used. The average error in comparing the experimental and simulated values for methane conversion, C₂ products selectivity, yield of C₂ and C₂H₄/C₂H₆ ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.     

Influence of a supplement containing conjugated linoleic acid on dairy performance,milk fatty acid composition,and adipose tissue reactivity to lipolytic challenge in mid-lactation goats

The objective of this work was to evaluate the effect of the supplementation of conjugated linoleic acid (CLA; 4.5 g of cis-9,trans-11 C18:2 and 4.5 g of trans-10,cis-12 C18:2) on milk performance, milk fatty acid (FA) composition, and adipose tissue reactivity in dairy goats fed a high-concentrate diet based on corn silage. Twenty-four multiparous dairy goats in early to mid lactation were used in a 10-wk trial, with a 3-wk adaptation to the experimental total mixed ration that contained corn silage (35%, dry matter basis), beet pulp (20%), barley (15%), and a commercial concentrate (30%). Goats were randomly allocated to 2 experimental groups and they were fed 45 g/d of a lipid supplement (either CLA or Ca salts of palm oil added on top of the total mixed ration). Individual milk production and composition were recorded weekly, and milk FA composition was analyzed in wk 2, 5, and 6. In the last week of the trial, an isoproterenol challenge was performed for 12 goats before morning feeding. The CLA supplementation had no effect on dry matter intake (DMI), body weight (BW), milk yield, milk protein content, and lactose yield and content, but it significantly decreased milk fat yield and content by 18 and 15%, respectively. The decrease in milk fat yield was related to a lower secretion of FA synthesized de novo, of the medium-chain FA, and to a lesser extent of the long-chain FA that are taken up from the peripheral circulation. The CLA supplementation decreased the proportion of the sum of C16:0 and C16:1 and the sum of total cis C18:1, and it increased the proportions of the sum of long-chain (C >16) and the sum of iso FA without modification of the total trans C18:1 and the sum of FA synthesized de novo (C <16). During the first 25 min relative to isoproterenol injection, the maximal concentrations, the increases above basal concentration, the changes in area under the curve, and the total area under the curve for glucose and nonesterified FA were not affected by CLA treatment. In conclusion, CLA supplementation associated with a high-concentrate diet based on corn silage resulted in decreased milk fat yield, increased net energy balance, and it did not affect the sensitivity of the adipose tissue to lipolytic challenge in lactating goats.     

On the Dissolution of Nitrided Titanium Defects During Vacuum Arc Remelting of Ti Alloys

The elimination of high interstitial defects (also known as hard-α inclusions) is of great importance to the titanium industry. This article presents a model capable of simulating the motion and dissolution of such defects during their residence in the pool of a vacuum arc remelted (VAR) ingot. To predict the complete history of that inclusion, the study couples a dissolution model of the defect and a Lagrangian particle-tracking model. This numerical tool is implemented in SOLAR (solidification during arc remelting), a computational fluid dynamics code developed at the Nancy School of Mines in the framework of an important research project conducted during the last 15 years, which aims to study and optimize the VAR process. The dissolution model numerically solves the nitrogen diffusion equation in a spherical inclusion and in thermal equilibrium with the surrounding fluid. The computational domain is divided into a central zone (α phase) and a surrounding layer (β phase), which appears because the diffusion of nitrogen into the liquid pool causes some solidification. The dissolution kinetics strongly depend on the liquid temperature and velocity of the inclusion. The model can compute the nitrogen profile in the defect at each moment as well as the thickness of the different layers; therefore, it can compute the overall size of the inclusion. The trajectory model consists of solving Newton’s law of motion. Because the inclusion size is large, the consequence of fluid-flow turbulence is to modify the local flow around the inclusion so that the drag is affected. Results presented and discussed in this article include a parametric study of the influence of the pool thermohydrodynamics, the relative inclusion–fluid density, and the initial diameter of the defect as it enters the melt pool. Finally, an example of the full history of an inclusion during triple VAR illustrates the possibility to remove such a defect effectively by dissolving it in the liquid phase.     

Robust global motion estimation oriented to video object segmentation.

Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods.     

Heterogenous Server Placement for Delay Sensitive Applications in Green Mobile Edge Computing

Wireless Personal Communications - The importance of environmental pollution has motivated the researchers to investigate the electrical power consumption of any emerging technology. Edge Computing...     

Gold nanocages in cancer diagnosis,therapy, and theranostics: A brief review

Regarding the increasing number of cancer patients, the global burden of this disease is continuing to grow. Despite a considerable improvement in the diagnosis and treatment of various types of cancer, new diagnosis and treatment strategies are required. Nanotechnology, as an interesting and advanced field in medicine, is aimed to further advance both cancer diagnosis and treatment. Gold nanocages (AuNCs), with hollow interiors and porous walls, have received a great deal of interest in various biomedical applications such as diagnosis, imaging, drug delivery, and hyperthermia therapy due to their special physicochemical characteristics including the porous structure and surface functionalization as well as optical and photothermal properties. This review is focused on recent developments in therapeutic and diagnostic and applications of AuNCs with an emphasis on their theranostic applications in cancer diseases.     

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Investigating the effect of weather on punctuality of Norwegian railways: a case study of the Nordland Line

The overall punctuality of the railway system is of particular importance for customers in order to use trains as a means of public transport. Investigating the drivers of low punctuality of trains is an essential step for improving their punctuality. Severe weather conditions are assumed to be one of the drivers of delays and low punctuality of the transportation system. This paper addresses the extent to which the weather factors such as temperature, snow, precipitation and wind influence the punctuality of trains on the Norwegian railways. The data for the study are collected from the passenger trains on the Nordland Line. The study period is 10 years, from the beginning of 2007 to the end of 2016. Data are analysed on a daily and weekly basis, and correlation and regression analyses are used to perform the statistical analysis. The results show that extreme cold weather during the winters is a crucial influencing factor contributing to delays and low punctuality. We also found that the weather variable that best explains variations in punctuality of passenger trains on this line is snow depth.