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.     

A new control method for Dynamic Voltage Restorer with asymmetrical inverter legs based on fuzzy logic controller

Dynamic Voltage Restorer (DVR) is used in power distribution system to protect sensitive loads in voltage disturbances. The performance of DVR is related to the adopted configuration and control strategy used for inverters. In this paper, an asymmetrical voltage-source inverter controlled with fuzzy logic method based on hysteresis controller, is used to improve operation of DVR to compensate voltage sag/swell. Simulation results using MATLAB/Simulink are presented to demonstrate the feasibility and the practicality of the proposed novel Dynamic Voltage Restorer topology. Total Harmonic Distortion (THD) is calculated. The simulation results of new DVR presented in this paper, are found quite satisfactory to eliminate voltage sag/swell.     

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.     

Effects of conjugated linoleic acid supplementation and feeding level on dairy performance,milk fatty acid composition,and body fat changes in mid-lactation goats

The objective of this trial was to study the interaction between the supplementation of lipid-encapsulated 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) and feeding level to test if milk performance or milk fatty acid (FA) profile are affected by the interaction between CLA and feeding level. Twenty-four dairy goats were used in an 8-wk trial with a 3-wk adaptation to the experimental ration that contained corn silage, beet pulp, barley, and a commercial concentrate. During the third week, goats were assigned into blocks of 2 goats according to their dry matter intake (DMI), raw milk yield, and fat yield. Each block was randomly allocated to control (45 g of Ca salt of palm oil/d) or CLA treatment. Within each block, one goat was fed to cover 100% (FL100) of the calculated energy requirements and the other was fed 85% of the DMI of the first goat (FL85). Individual milk production and composition were recorded weekly, and milk FA composition was analyzed in wk 3, 5, and 7. Conjugated linoleic acid supplementation reduced milk fat content and fat yield by 17 and 19%, respectively, independent of the feeding level. It reduced both the secretion of milk FA synthesized de novo, and those taken up from the blood. No interaction between CLA and feeding level was observed on milk secretion of any group of FA. The CLA supplementation had no effect on DMI, milk yield, protein, and lactose yields but it improved calculated net energy for lactation balance. Goats fed the FL100 × CLA diet tended to have the highest DMI and protein yield. The interaction between CLA and feeding level was not significant for any other variables. Compared with the goats fed FL100, those fed FL85 had lower DMI, lower net energy for lactation balance, and lower digestible protein in the intestine balance. The body weight; milk yield; milk fat, protein, and lactose yields; and fat, protein, lactose, and urea contents in milk were not affected by feeding level. In conclusion, reduction in energy spared via fat yield reduction after CLA supplementation was not partitioned toward milk lactose or protein in goats at a low feeding level, possibly because of a simultaneous shortage of energy and amino acids. In goats on the high feeding level, energy spared tended to be partitioned toward milk protein yield, and at the same time to the prevention of excessive lipid mobilization.     

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.     

High-voltage portable pulsed power supply fed by low voltage source

This article proposes a new structure of voltage multiplier for portable pulsed power applications. In this configuration, which is based on capacitor-diode voltage multiplier, the capacitors are charged by low AC input voltage and discharge through the load in series during pulse generation mode. The proposed topology is achieved by integrating of solid-state switches with conventional voltage multiplier, which can increase the low input voltage step by step and generate high-voltage high-frequency pulsed power across the load. After some discussion, simulations and experimental results are provided to verify the effectiveness of the proposed topology.     

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.     

Real-time face detection and lip feature extraction using field-programmable gate arrays.

This paper proposes a new technique for face detection and lip feature extraction. A real-time field-programmable gate array (FPGA) implementation of the two proposed techniques is also presented. Face detection is based on a naive Bayes classifier that classifies an edge-extracted representation of an image. Using edge representation significantly reduces the model's size to only 5184 B, which is 2417 times smaller than a comparable statistical modeling technique, while achieving an 86.6% correct detection rate under various lighting conditions. Lip feature extraction uses the contrast around the lip contour to extract the height and width of the mouth, metrics that are useful for speech filtering. The proposed FPGA system occupies only 15050 logic cells, or about six times less than a current comparable FPGA face detection system.     

Comparison of deposition models for a TEOS LPCVD process

We present a comparison of models describing the pyrolytic deposition of SiO₂ with a low pressure chemical vapor deposition process. In order to meet industrial simulation requirements, e.g. accuracy and fast delivery of results, we present an overview of established and new models, their use within TCAD applications, and their best results which have been obtained by calibrations according to SEM measurements.     

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.