Wireless monitoring of scour and re-deposited sediment evolution at bridge foundations based on soil electromagnetic properties

Hydraulic structures constitute the most vulnerable elements of transportation infrastructure. Recent increases in precipitation have resulted in severe and more frequent flash flooding incidents. This has put bridges over waterways at higher risk of failure due to scour. This study presents a new sensor for measuring scour depth variation and sediment deposition processes in the vicinity of the foundations to underpin systems for early warning of impending structural failure. The monitoring system consists of a probe with integrated electromagnetic sensors designed to detect changes in the dielectric permittivity of the surrounding bridge foundation. The probe is equipped with a wireless interface and was evaluated to assess its ability to detect scour and sediment deposition in various soil types and under temperature and water salinity conditions that would commonly occur in a practical installation environment. A novel methodology is also developed enabling discrimination between in-situ and re-deposited sediment delivering vital information about the load bearing capacity of the foundation. The experimental approach was validated using ‘static’ scour simulations and real-time open channel flume experiments. Results indicate that the sensor is highly sensitive to underwater bed level variations and can provide an economical and accurate structural health monitoring alternative to existing instruments

A novel monitoring system for fall detection in older people

Indexación: Scopus.This work was supported in part by CORFO - CENS 16CTTS-66390 through the National Center on Health Information Systems, in part by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC-03: ‘‘MONITORing for ehealth," FONDEF ID16I10449 ‘‘Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público’’, and in part by MEC80170097 ‘‘Red de colaboración científica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de Valparaíso’’. The work of V. H. C. De Albuquerque was supported by the Brazilian National Council for Research and Development (CNPq), under Grant 304315/2017-6.Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: Long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative. © 2013 IEEE.https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=842305

Genetic dissection of photoperiod response based on GWAS of pre-anthesis phase duration in spring barley

Heading time is a complex trait, and natural variation in photoperiod responses is a major factor controlling time to heading, adaptation and grain yield. In barley, previous heading time studies have been mainly conducted under field conditions to measure total days to heading. We followed a novel approach and studied the natural variation of time to heading in a world-wide spring barley collection (218 accessions), comprising of 95 photoperiod-sensitive (Ppd-H1) and 123 accessions with reduced photoperiod sensitivity (ppd-H1) to long-day (LD) through dissecting pre-anthesis development into four major stages and sub-phases. The study was conducted under greenhouse (GH) conditions (LD; 16/8 h; ∼20/∼16°C day/night). Genotyping was performed using a genome-wide high density 9K single nucleotide polymorphisms (SNPs) chip which assayed 7842 SNPs. We used the barley physical map to identify candidate genes underlying genome-wide association scans (GWAS). GWAS for pre-anthesis stages/sub-phases in each photoperiod group provided great power for partitioning genetic effects on floral initiation and heading time. In addition to major genes known to regulate heading time under field conditions, several novel QTL with medium to high effects, including new QTL having major effects on developmental stages/sub-phases were found to be associated in this study. For example, highly associated SNPs tagged the physical regions around HvCO1 (barley CONSTANS1) and BFL (BARLEY FLORICAULA/LEAFY) genes. Based upon our GWAS analysis, we propose a new genetic network model for each photoperiod group, which includes several newly identified genes, such as several HvCO-like genes, belonging to different heading time pathways in barley

Robust identification of local adaptation from allele frequencies

Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns, and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of `standardized allele frequencies' that allows investigators to apply tests of their choice to multiple populations, while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to calculate powerful tests to detect non-parametric correlations with environmental variables, which are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to FST but should be more powerful as we account for population history. We also extend the model to next-generation sequencing of population pools, which is a cost-efficient way to estimate population allele frequencies, but it implies an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by re-analyzing human SNP data from the HGDP populations. An implementation of our method will be available from http://gcbias.org.Comment: 27 pages, 7 figure

A novel intermittent fault detection algorithm and health monitoring for electronic interconnections

There are various occurrences and root causes that result in no-fault-found (NFF) events but an intermittent fault (IF) is the most frustrating. This paper describes the challenging and most important area of an IF detection and health monitoring that focuses toward NFF situation in electronics interconnections. The experimental work focuses on mechanically-induced intermittent conditions in connectors. This paper illustrates a test regime, which can be used to repeatedly reproduce intermittence in electronic connectors, while subjected to vibration. A novel algorithm is used to detect an IF in interconnection. It sends a sine wave and decodes the received signal for intermittent information from the channel. This algorithm has been simulated to capture an IF signature using PSpice (electronic circuit simulation software). A simulated circuit is implemented for practical verification. However, measurements are presented using an oscilloscope. The results of this experiment provide an insight into the limitations of existing test equipment and requirements for future IF detection techniques. Aside from scheduled maintenance, this paper considers the possibility for in-service intermittent detection to be built into future systems, i.e., can IFs be captured without external test gear

Spin characterization and control over the regime of radiation-induced zero-resistance states

Over the regime of the radiation-induced zero-resistance states and associated oscillatory magnetoresistance, we propose a low magnetic field analog of quantum-Hall-limit techniques for the electrical detection of electron spin- and nuclear magnetic- resonance, dynamical nuclear polarization via electron spin resonance, and electrical characterization of the nuclear spin polarization via the Overhauser shift. In addition, beats observed in the radiation-induced oscillatory-magnetoresistance are developed into a method to measure and control the zero-field spin splitting due to the Bychkov-Rashba and bulk inversion asymmetry terms in the high mobility GaAs/AlGaAs system.Comment: IEEE Transactions in Nanotechnology (to be published); 10 pages, 10 color figure

Early life sensory ability—ventilatory responses of thornback ray embryos (Raja clavata) to predator-type electric fields

Predator avoidance is fundamental for survival and it can be particularly challenging for prey animals if physical movement away from a predatory threat is restricted. Many sharks and rays begin life within an egg capsule that is attached to the sea bed. The vulnerability of this sedentary life stage is exacerbated in skates (Rajidae) as the compulsory ventilatory activity of embryos makes them conspicuous to potential predators. Embryos can reduce this risk by mediating ventilatory activity if they detect the presence of a predator using an acute electrosense. To determine how early in embryonic life predator elicited behavioral responses can occur, the reactions of three different age groups (1/3 developed, 2/3 developed, and near hatching) of embryonic thornback rays Raja clavata were tested using predator-type electric field stimuli. Egg capsules were exposed to continuous or intermittent stimuli in order to assess varying predator-type encounter scenarios on the ventilatory behavior of different developmental stages. All embryos reacted with a “freeze response” following initial electric field (E-field) exposure, ceasing ventilatory behavior in response to predator presence, demonstrating electroreceptive functionality for the first time at the earliest possible stage in ontogeny. This ability coincided with the onset of egg ventilatory behavior and may represent an effective means to enhance survival. A continuous application of stimuli over time revealed that embryos can adapt their behavior and resume normal activity, whereas when presented intermittently, the E-field resulted in a significant reduction in overall ventilatory activity across all ages. Recovery from stimuli was significantly quicker in older embryos, potentially indicative of the trade-off between avoiding predation and adequate respiration. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 76: 721–729, 201

A novel colorimetric biosensor based on non-aggregated Au@Ag core–shell nanoparticles for methamphetamine and cocaine detection

We report a novel colorimetric biosensor based on non-aggregation Au@Ag core-shell nanoparticles to detect methamphetamine and cocaine. The biosensor consisted of a reporter probe (RP) that is a specific single-stranded DNA (ssDNA) sequence coated on Au@Ag nanoparticles, a capture probe (CP) conjugated with magnetic beads, and an illicit drug-binding DNA aptamer (Apt). Au@Ag nanoparticles were synthesized by seed growth and characterized by scanning electron microscope (SEM), high-resolution transmission electron microscopy (HR-TEM), and UV–vis spectra. Methamphetamine (METH) was used as an example to evaluate the feasibility of the biosensor and to optimize the detection conditions. We demonstrated that this sensing platform was able to detect as low as 0.1 nM (14.9 ng L−1) METH with a negligible interference from other common illicit drugs. Various concentrations of METH were spiked into urines, and the biosensor yielded recoveries more than 83.1%. In addition, the biosensor also showed a high sensitivity to detect cocaine. These results demonstrated that our colorimetric sensor holds promise to be implemented as a visual sensing platform to detect multiple illicit drugs in biological samples and environmental matrices