SEIS: Insight's Seismic Experiment for Internal Structure of Mars.

By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars' surface the SEIS (Seismic Experiment for Internal Structure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking's Mars seismic monitoring by a factor of ∼ 2500 at 1 Hz and ∼ 200 000 at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars' surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of M w ∼ 3 at 40 ∘ epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution.Electronic supplementary materialThe online version of this article (10.1007/s11214-018-0574-6) contains supplementary material, which is available to authorized users

Medulloblastoma Exome Sequencing Uncovers Subtype-Specific Somatic Mutations

Medulloblastomas are the most common malignant brain tumors in children1. Identifying and understanding the genetic events that drive these tumors is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. Recently, our group and others described distinct molecular subtypes of medulloblastoma based on transcriptional and copy number profiles2–5. Here, we utilized whole exome hybrid capture and deep sequencing to identify somatic mutations across the coding regions of 92 primary medulloblastoma/normal pairs. Overall, medulloblastomas exhibit low mutation rates consistent with other pediatric tumors, with a median of 0.35 non-silent mutations per megabase. We identified twelve genes mutated at statistically significant frequencies, including previously known mutated genes in medulloblastoma such as CTNNB1, PTCH1, MLL2, SMARCA4 and TP53. Recurrent somatic mutations were identified in an RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR, and LDB1, novel findings in medulloblastoma. We show that mutant DDX3X potentiates transactivation of a TCF promoter and enhances cell viability in combination with mutant but not wild type beta-catenin. Together, our study reveals the alteration of Wnt, Hedgehog, histone methyltransferase and now N-CoR pathways across medulloblastomas and within specific subtypes of this disease, and nominates the RNA helicase DDX3X as a component of pathogenic beta-catenin signaling in medulloblastoma

SEIS: Insight’s Seismic Experiment for Internal Structure of Mars

By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars’ surface the SEIS (Seismic Experiment for Internal Structure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking’s Mars seismic monitoring by a factor of ∼ 2500 at 1 Hz and ∼ 200 000 at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars’ surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of Mw ∼ 3 at 40◦ epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution

Design and evaluation of a mobile robot kit for undergraduate computer science and computer engineering practicum classes

The goal of this project was to design and build a custom mobile robot kit to be used in the new curriculum of Multi-Disciplinary Project (MDP). MDP is an undergraduate project compulsory for Computer Science and Computer Engineering students. The project teams will be provided with a robot kit, which they should assemble and program to perform a specified task. The objective of this FYP is to come up with this demo robot kit. This project was carried out in two phases. First phase focused on “How can we get a popular robot operating system like ROS to run on an affordable (low cost) custom robot platform? And how feasible would it be”. Phase 2 was more focused on designing and building a customized low-cost platform for MDP. In phase 1 we were able to use a low cost off-the-shelf robot base platform with an Arduino microcontroller, Raspberry Pi and a sensor module, which was compatible with ROS. First we were able to control the robot through ROS via a laptop. Then we went into replacing the laptop with a $35 credit card size computer called Raspberry Pi. This was never been tried by the industry before. We managed to overcome many difficulties and successfully port ROS over to Raspberry Pi. We got ROS working in a distributed computing setup except for a few minor issues in communication with Raspberry Pi. In phase 2 we designed and build a robot from scratch. We made use of the knowledge and experience gained from phase 1, as well as the components designed in phase 1 to come up with a demo kit for MDP. This report provides detailed information on how the hardware and software parts were designed and implemented in this project.Bachelor of Engineering (Computer Engineering

A Temporal-Causal Model for Spread of Messages in Disasters

In this paper we describe a temporal-causal model for the spread of messages in disaster situations based on emotion contagion and awareness works. An evaluation of the model has been done by simulation experiments and mathematical analysis. Parameter tuning was done based on two scenarios, including a credible message and a dubious message. The results are useful for the prediction of reactions during disasters, and can be extended to other applications that involve panic and supportive systems to assist people

Modelling the Integration of Costs and Benefits During Decision Making

In this paper a computational cognitive model for decision making based on cost-benefit comparison is presented. For such decision making, it is required that the bene-fits of an option gets weighed against the costs. The brain weighs costs against benefits by combining reward and loss signals in the brain into a single, difference-based neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option. The presented model integrates such findings of the litera-ture and is able to explain a person’s decision making behaviour through several sce-narios. A general parameter setting was found that is used throughout all the simulation scenarios

Make carbon nanotube-PMMA composite thin: application to water quality

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