Why GPS makes distances bigger than they are

Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are among the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation error. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS: the distance between two points recorded with a GPS is -- on average -- bigger than the true distance between these points. This systematic `overestimation of distance' becomes relevant if the influence of interpolation error can be neglected, which is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and we illustrate that it functionally depends on the autocorrelation of GPS measurement error ($C$). We argue that $C$ can be interpreted as a quality measure for movement data recorded with a GPS. If there is strong autocorrelation any two consecutive position estimates have very similar error. This error cancels out when average speed, distance or direction are calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine $C$ in real-world GPS movement data sampled at high frequencies. We apply our approach to a set of pedestrian and a set of car trajectories. We find that the measurement error in the data is strongly spatially and temporally autocorrelated and give a quality estimate of the data. Finally, we want to emphasize that all our findings are not limited to GPS alone. The systematic bias and all its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.Comment: 17 pages, 8 figures, submitted to IJGI

Computational Identification and Analysis of the Key Biosorbent Characteristics for the Biosorption Process of Reactive Black 5 onto Fungal Biomass

The performances of nine biosorbents derived from dead fungal biomass were investigated for their ability to remove Reactive Black 5 from aqueous solution. The biosorption data for removal of Reactive Black 5 were readily modeled using the Langmuir adsorption isotherm. Kinetic analysis based on both pseudo-second-order and Weber-Morris models indicated intraparticle diffusion was the rate limiting step for biosorption of Reactive Black 5 on to the biosorbents. Sorption capacities of the biosorbents were not correlated with the initial biosorption rates. Sensitivity analysis of the factors affecting biosorption examined by an artificial neural network model showed that pH was the most important parameter, explaining 22%, followed by nitrogen content of biosorbents (16%), initial dye concentration (15%) and carbon content of biosorbents (10%). The biosorption capacities were not proportional to surface areas of the sorbents, but were instead influenced by their chemical element composition. The main functional groups contributing to dye sorption were amine, carboxylic, and alcohol moieties. The data further suggest that differences in carbon and nitrogen contents of biosorbents may be used as a selection index for identifying effective biosorbents from dead fungal biomass

Hybrid Shell Engineering of Animal Cells for Immune Protections and Regulation of Drug Delivery: Towards the Design of “Artificial Organs”

BACKGROUND: With the progress in medicine, the average human life expectancy is continuously increasing. At the same time, the number of patients who require full organ transplantations is augmenting. Consequently, new strategies for cell transplantation are the subject of great interest. METHODOLOGY/PRINCIPAL FINDINGS: This work reports the design, the synthesis and the characterisation of robust and biocompatible mineralised beads composed of two layers: an alginate-silica composite core and a Ca-alginate layer. The adequate choice of materials was achieved through cytotoxicity LDH release measurement and in vitro inflammatory assay (IL-8) to meet the biocompatibility requirements for medical purpose. The results obtained following this strategy provide a direct proof of the total innocuity of silica and alginate networks for human cells as underscored by the non-activation of immune defenders (THP-1 monocytes). The accessible pore size diameter of the mineralised beads synthesized was estimated between 22 and 30 nm, as required for efficient immuno-isolation without preventing the diffusion of nutrients and metabolites. The model human cells, HepG2, entrapped within these hybrid beads display a high survival rate over more than six weeks according to the measurements of intracellular enzymatic activity, respiration rate, as well as the "de novo" biosynthesis and secretion of albumin out of the beads. CONCLUSIONS/SIGNIFICANCE: The current study shows that active mammalian cells can be protected by a silica-alginate hybrid shell-like system. The functionality of the cell strain can be maintained. Consequently, cells coated with an artificial and a biocompatible mineral shell could respond physiologically within the human body in order to deliver therapeutic agents in a controlled fashion (i.e. insulin), substituting the declining organ functions of the patient

Pore confinement effects and stabilization of carbon nitride oligomers in macroporous silica for photocatalytic hydrogen production

An ordered macroporous host (mac-SiO2) has been used to prevent aggregation of layered photocatalysts based on carbon nitride. Using typical carbon nitride synthesis conditions, cyanamide was condensed at 550 °C in the presence and absence of mac-SiO2. Condensation in the absence of mac-SiO2 results in materials with structural characteristics consistent with the carbon nitride, melon, accompanied by ca. 2 wt% carbonization. For mac-SiO2 supported materials, condensation occurs with greater carbonization (ca. 6 wt%). On addition of 3 wt% Pt cocatalyst photocatalytic hydrogen production under visible light is found to be up to 10 times greater for the supported composites. Time-resolved photoluminescence spectroscopy shows that excited state relaxation is more rapid for the mac-SiO2 supported materials suggesting faster electron-hole recombination and that supported carbon nitride does not exhibit improved charge separation. CO2 temperature programmed desorption indicates that enhanced photoactivity of supported carbon nitride is attributable to an increased surface area compared to bulk carbon nitride and an increase in the concentration of weakly basic catalytic sites, consistent with carbon nitride oligomers

Biogenic nano-magnetite and nano-zero valent iron treatment of alkaline Cr(VI) leachate and chromite ore processing residue

Highly reactive nano-scale biogenic magnetite (BnM), synthesized by the Fe(III)-reducing bacterium Geobacter sulfurreducens, was tested for the potential to remediate alkaline Cr(VI) contaminated waters associated with chromite ore processing residue (COPR). The performance of this biomaterial, targeting aqueous Cr(VI) removal, was compared to a synthetic alternative, nano-scale zero valent iron (nZVI). Samples of highly contaminated alkaline groundwater and COPR solid waste were obtained from a contaminated site in Glasgow, UK. During batch reactivity tests, Cr(VI) removal from groundwater was inhibited by ~25% (BnM) and ~50% (nZVI) when compared to the treatment of less chemically complex model pH 12 Cr(VI) solutions. In both the model Cr(VI) solutions and contaminated groundwater experiments the surface of the nanoparticles became passivated, preventing complete coupling of their available electrons to Cr(VI) reduction. To investigate this process, the surfaces of the reacted samples were analyzed by TEM-EDX, XAS and XPS, confirming Cr(VI) reduction to the less soluble Cr(III) on the nanoparticle surface. In groundwater reacted samples the presence of Ca, Si and S was also noted on the surface of the nanoparticles, and is likely responsible for earlier onset of passivation. Treatment of the solid COPR material in contact with water, by addition of increasing weight % of the nanoparticles, resulted in a decrease in aqueous Cr(VI) concentrations to below detection limits, via the addition of ≥5% w/w BnM or ≥1% w/w nZVI. XANES analysis of the Cr K edge, showed that the % Cr(VI) in the COPR dropped from 26% to a minimum of 4-7% by the addition of 5% w/w BnM or 2% w/w nZVI, with higher additions unable to reduce the remaining Cr(VI). The treated materials exhibited minimal re-mobilization of soluble Cr(VI) by re-equilibration with atmospheric oxygen, with the bulk of the Cr remaining in the solid fraction. Both nanoparticles exhibited a considerable capacity for the remediation of COPR related Cr(VI) contamination, with the synthetic nZVI demonstrating greater reactivity than the BnM. However, the biosynthesized BnM was also capable of significant Cr(VI) reduction and demonstrated a greater efficiency for the coupling of its electrons towards Cr(VI) reduction than the nZVI

Evaluating the Brownian Bridge Movement Model to Determine Regularities of People’s Movements. GI_Forum|GI_Forum 2016, Volume 2 – open:spatial:interfaces|

The movements of animals or humans are characterized by starting points, transitions and end points, where starting and end points typically represent distinct locations. Knowledge of such locations and movement patterns is relevant to predict future movements or to detect regularities in movement behaviour. We present a Brownian bridge-based approach applied to human movement data to extract regularities of people staying in distinct locations. Such information is, for example, of interest in zoology (e.g. animal home range estimation) or health (e.g. detection of deviations from regular behaviour of people with cognitive impairments). To obtain information about where a person stayed, we derived the areas of their whereabouts from GPS trajectories by using a Brownian bridge movement model (BBMM). The resulting whereabouts areas were intersected with these GPS trajectories to create so-called whereabouts tables, which describe time of day, duration and place of a person’s stay. The probability of finding a person in a particular area within a particular time window was determined. The whereabouts areas of two people were investigated and assessed by using two tailored quality measures: spatial accuracy and spatial uniqueness. In order to reduce the computational costs of BBMM, down-sampling was investigated. With respect to spatial accuracy, results improved by down-sampling

CarSense: Evaluation of the Potential of In-Vehicle Sensor Data for Road Operators

Modern vehicles are equipped with dozens of sensors collecting data for supporting automotive trends like Autonomous and/or Connected Driving. For road operators, in-vehicle data or so-called Extended Floating Car Data (XFCD) are often promised as the Holy Grail for supporting efficient road operations. In this work, we tackle the question of benefits with respect to potential in-vehicle sensor data usage from a road operator’s point of view, focusing on traffic information, traffic management and road maintenance. Key results are amongst others: (1) the need for customized XFCD solutions supporting road operator’s tasks, (2) standardized data access and formats for in-vehicle sensor data, (3) cost-effective integration of XFCD into existing monitoring infrastructure, and (4) the need to actively promote road operator’s interests

Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units

In alpine skiing, four commonly used turning styles are snowplow, snowplow-steering, drifting and carving. They differ significantly in speed, directional control and difficulty to execute. While they are visually distinguishable, data-driven classification is underexplored. The aim of this work is to classify alpine skiing styles based on a global navigation satellite system (GNSS) and inertial measurement units (IMU). Data of 2000 turns of 20 advanced or expert skiers were collected with two IMU sensors on the upper cuff of each ski boot and a mobile phone with GNSS. After feature extraction and feature selection, turn style classification was applied separately for parallel (drifted or carved) and non-parallel (snowplow or snowplow-steering) turns. The most important features for style classification were identified via recursive feature elimination. Three different classification methods were then tested and compared: Decision trees, random forests and gradient boosted decision trees. Classification accuracies were lowest for the decision tree and similar for the random forests and gradient boosted classification trees, which both achieved accuracies of more than 93% in the parallel classification task and 88% in the non-parallel case. While the accuracy might be improved by considering slope and weather conditions, these first results suggest that IMU data can classify alpine skiing styles reasonably well

Development of an Automatic Alpine Skiing Turn Detection Algorithm Based on a Simple Sensor Setup

In order to gain insight into skiing performance, it is necessary to determine the point where each turn begins. Recent developments in sensor technology have made it possible to develop simpler automatic turn detection methodologies, however they are not feasible for regular use. The aim of this study was to develop a sensor set up and an algorithm to precisely detect turns during alpine ski, which is feasible for a daily use. An IMU was attached to the posterior upper cuff of each ski boot. Turn movements were reproduced on a ski-ergometer at different turn durations and slopes. Algorithms were developed to analyze vertical, medio-lateral, anterior-posterior axes, and resultant accelerometer and gyroscope signals. Raw signals, and signals filtered with 3, 6, 9, and 12 Hz cut-offs were used to identify turn switch points. Video recordings were assessed to establish a reference turn-switch and precision (mean bias = 5.2, LoA = 51.4 ms). Precision was adjusted based on reference and the best signals were selected. The z-axis and resultant gyroscope signals, filtered at 3Hz are the most precise signals (0.056 and 0.063 s, respectively) to automatically detect turn switches during alpine skiing using this simple system.(VLID)346362