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River monitoring, particulate Cs-137 measurements and rainfall monintoring of the Mano Dam catchment (Japan) from 2014 to 2019

This dataset is composed of river monitoring, laboratory particulate Cs-137 measurements and rainfall monitoring used in the article: Vandromme, R., Hayashi, S.,Tsuji, H., Evrard, O., Grangeon, T., Landemaine, V., Laceby, J.P., Wakiyama, Y., Cerdan, O. The unprecedented soil decontamination program in Fukushima only reduced radioactive fluxes in rivers by 17% (PNAS, under review)  River measurements were conducted by Hayashi S. and Tsuji H.  A hydrological station was installed immediately upstream of Mano Dam Lake (Lake Hayama) in June 2014. The water height in the river was measured using an AquiStar PT12 pressure/temperature sensor (INW, Kirkland, WA, USA) at 5-min intervals and then converted into flow rate data using gage curves. Water and SS samples were also collected from the river section using an ISCO 6712 automatic water sampler (Teledyne Technologies, Lincoln, NE, USA) at 1-h intervals during flood events. The SS concentration was determined in the laboratory by filtration using a Whatman GF/F filter with 0.7-µm pores (Cytiva, Tokyo, Japan), and these concentrations were then used to convert the turbidity logs measured by a DTS-12 digital turbidity sensor (FTS Inc., Victoria, BC CANADA) at 5-min intervals into SS logs.  This station was damaged by Typhoon Etau in September 2015. Following this damage, the data analysis and discovery of anomalies that persisted until the end of 2016 prompted us to omit the data acquired during this period. The hydrological station was subsequently moved a few hundred meters upstream in 2017. Water flows and sediment concentrations were subsequently acquired using the same systems described above at 10-min intervals until the end of 2019.  River water samples were punctually collected to measure 137Cs concentration in Suspended Sediment (SS) (66 measurements during floods) from June 2014 to December 2019 (this chronicle ends in the middle of a flood). Station location before 2017 (coordinate system:WGS84) : lat 37.738391, long 140.803943 Station location after 2017 (coordinate system:WGS84) :  lat 37.733191, long 140.807709 Rainfall record was acquired by the Fukushima Prefecture (Maenori station, lat 37.73644, long 140.74233, coordinate system:WGS84) at 10-minute time steps.

opencc-zeroMay 2023View details →

Monitoring the evolution of relative product populations at early times during a photochemical reaction

Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps towards understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species amongst the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with ab initio molecular dynamics calculations offers a unique route to determine time-resolved populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule thiophenone. This strategy reveals an unexpectedly high (~50%) yield of an episulfide isomer containing a strained 3-membered ring within ~1 ps at early times and rapid interconversions between the rival photoproducts.

opencc-zeroMay 2023View details →

Coffee Leaf Rust monitoring data on Hawaii Island

CLR was detected in Hawaii for the first time in 2020 and quickly spread across the state. We initiated a CLR monitoring program in Kona, West Hawaii Island to track the spread of this new invasive disease across a broad elevational gradient. The goals of the program were to assist growers in the early detection of CLR, to characterize patterns of disease incidence across the region, and to collect information on farm agronomics, management practices, and costs to apply fungicides, all of which can be used to develop IPM strategies for this pathogen. We monitored 30 coffee lots in Kona, located between 204 and 875 m elevation. Average CLR incidence remained below 4% early in the season and increased to 36% during harvest. We observed no significant difference in CLR incidence between low-, mid- and high-elevation farms. A significant reduction in the number of leaves per branch was observed at the end of the harvest season, and a significant negative correlation was found between the number of leaves per branch and maximum CLR severity. Mean disease incidence and mean severity were observed to have a significant positive correlation. Incidence increased above threshold levels (5%) despite most growers applying preventative fungicides 3-10 times throughout the season, suggesting that improved coverage and timing of applications is needed along with the addition of systemic fungicides. Our study provides the first insights into CLR disease patterns under the unique and variable conditions under which Hawaiian coffee is grown and will aid in the development of IPM programs that can be used to sustain Hawaii's coffee industry under this new threat.

A Benchmark Dataset for Vision-based Bridge Traffic Load Monitoring in a Cable-stayed Bridge

Traffic load monitoring based on deep learning and computer vision has garnered significant attention in bridge engineering worldwide. Unlike traditional traffic load monitoring systems, computer vision-based techniques can accurately extract the spatiotemporal load distribution across the entire bridge in an autonomous manner. However, many of the related studies in the literature used datasets that were collected from a few specific areas of different bridges, and there are very limited datasets that provide complete coverage of the entire bridge, making a detailed comparison of different methods difficult. This paper presents a benchmark dataset that provides a series of annotations and field measurements required for traffic load detection, tracking and continuous monitoring on the bridge. The dataset was collected by five cameras, and two weigh-in-motion systems installed on a cable-stayed bridge and is divided into three subsets. The first subset contains over 32,000 images and annotation files of eleven types of vehicle-related targets, which are necessary for the training of vehicle detection models. The second subset consists of photos of the calibration board and coordinates of reference points that are used for camera calibration. The last subset is designated for the field verification of various algorithms, providing synchronized vehicle weight data and monitoring videos covering the whole bridge. To the author’s knowledge, this dataset is the first open-source dataset for vision-based traffic load monitoring in a bridge, which will have tremendous value in promoting research in the area of innovative bridge health monitoring technologies.

opencc-by-4.0Jul 2022View details →

Performance monitoring leveraging advanced AI technique with CNN

The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting time of the signal, the resolution of images, and the number of images. 2) The second step is to study damage prediction with four different stress levels. The FE model is used to simulate structural performance with different delamination conditions. Moreover, except for field test results, the artificial delamination model is created.  We performed numerous finite element (FE) simulation to create inputs for CNN for damage detection. The result shows improved NDT results, and CNN can achieve structural performance prediction. We performed six tasks based on these objectives: Task 1. literature review; Task 2. Collect data from bridges; Task 3. perform filed test NDT results; Task 4. Develop FE model based on field test results; Task 5. development of a machine learning model for damage prediction.

Growth monitoring and promotion_Riskofbias2

Presents the risk of bias assessments for the Cochrane review: "impact of growth monitoring and promotion on health indicators in children under 5 in low - and middle- income countries"

Timelapse of Outdoor Pre-experiment of Monitoring Framework

The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy -  CITA in Copenhagen, Denmark. The timelapse is a pre-experiment of the Outdoor Monitoring Framework developed for investigating the behaviour of bio-based materials to weathering.

Sensed Data of Indoor Monitoring Framework in Aedes Architecture Forum Exhibition called Living Prototypes

The presented Dataset is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy -  CITA in Copenhagen, Denmark. The collected data is part of Indoor Monitoring Framework sensed data (temperature and humidity) stored in InfluxDB in CSV format for Living Prototype Exhibition at Aedes Forum in Berlin for a period of 35 days. The dataset can be used in InfluxDB and Grafana for visualization.

Timelapse of Indoor Monitoring of 3D printed Panels installed in Living Prototypes Exhibition in Aedes Forum, Berlin

The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy -  CITA in Copenhagen, Denmark. The timelapse is part of Indoor Monitoring Framework developed for Living Prototype Exhibition at Aedes Forum in Berlin.

opencc-by-4.0Dec 2015View details →

Figure 1 in Preliminary data on the chironomid fauna of the Middle Volga region within the Republic of Tatarstan (Russia) based on hydrobiological monitoring studies

Figure 1. Map of the region. Sampling sites are given in black circles.

opencc-zeroJun 2023View details →

Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted

Barred Owls (Strix varia) have recently expanded westward from eastern North America, contributing to substantial declines in Northern Spotted Owls (Strix occidentalis caurina). Passive acoustic monitoring (PAM) represents a potentially powerful approach for tracking range expansions like the Barred Owl's, but further methods development is needed to ensure that PAM-informed occupancy models meaningfully reflect population processes. Focusing on the leading edge of the Barred Owl range expansion in coastal California, we used a combination of PAM data, GPS-tagging, and active surveys to (1) estimate breeding home range size, (2) identify patterns of vocal activity that reflect resident occupancy, and (3) estimate resident occupancy rates. Mean breeding season home range size (452 ha) was reasonably consistent with the size of cells (400 ha) sampled with autonomous recording units (ARUs). Nevertheless, false-positive acoustic detections of Barred Owls frequently occurred within cells not containing an activity center such that site occupancy estimates derived using all detected vocalizations (0.61) were unlikely to be representative of resident occupancy. However, the proportion of survey nights with confirmed vocalizations (VN) and the number of ARUs within a sampling cell with confirmed vocalizations (VU) were indicative of Barred Owl residency. Moreover, the false positive error rate could be reduced for occupancy analyses by establishing thresholds of VN and VU to define detections, although doing so increased false negative error rates in some cases. Using different thresholds of VN and VU, we estimated resident occupancy to be 0.29–0.44, which indicates that Barred Owls have become established in the region but also that timely lethal removals could still help prevent the extirpation of Northern Spotted Owls. Our findings provide a scalable framework for monitoring Barred Owl populations throughout their expanded range and, more broadly, a basis for converting site occupancy to resident occupancy in PAM programs.

Optimising the xWORM Assay for Monitoring Hookworm Larvae Motility

Parasitic worms (helminths) infect almost all taxa across the animal kingdom, and pose significant challenges to public health and economies, particularly in developing countries. To address this problem, researchers have developed various tools to measure the motility and viability of helminths. However, the conditions used in anthelmintic screening assays are often not optimised, and they can vary considerably between research teams. These un-optimised conditions can impact new drug screens as little is known about the effect of different conditions on the health of the target parasites. To improve future research this study determined the effect of key assay parameters such as media type, media concentration, in-well parasite density, and assay duration on the infective third-stage larva (L3) of two types of hookworms, Nippostrongylus brasiliensis in rodents and Necator americanus in humans. Conditions were screened over several days using the xWORM assay, a real time impedance-based helminth motility assay using the xCELLigence system with 96-well microplates. While results varied depending on the species and media used, the study found that 500-1000 L3/200µL well volume and a media concentration of 3.13 – 25% generally produced good to excellent assay conditions. This data set provides the outcomes for all conditions tested by this study. This study’s findings can guide future selection of xWORM assay parameters for novel drug trials involving these parasite species and serve as a suggested model for optimising trial conditions for alternate parasite targets and assays.

opencc-by-nc-sa-4.0Jun 2023View details →

Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy - Raw Data

Raw data for publication Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy

opencc-by-4.0Dec 2012View details →

Abb. 1 in Monitoring von Populationen der Helm-Azurjungfer Coenagrion mercuriale (Odonata: Coenagrionidae): Was taugen zwei Begehungen pro Saison?

Abb. 1. Männchen von Coenagrion mercuriale.

opencc-by-4.0Jun 2023View details →

Monitoring of postpartum body condition at the cow and herd levels: assessing explanatory and predictive power of disease risk models

Objectives 1- To define the herd threshold for cows with poor body condition based on its predictive capacity for disease risk at the herd level, and 2- to estimate the impact measures on disease rates due to body condition indicators in transition period. Two commercial grazing dairy herds (Herd A=5.034 and herd B=7.965 lactations) from Argentinean Pampa region were used to perform a longitudinal retrospective study during a 4-year period (2014 –2017).Health, reproductive and body condition score (BCS) records were gathered. The BCS (5-point scale) was performed at calving and at the time of reproductive release. The difference between both measures of BCS was used to assess the body condition loss (∆BCS). All the cows not bred by 70 DIM were checked for anestrus.Calving cohorts of 21-day were defined at each herd and parity group through the entire study period. The frequency of cows with BCS<3 or ∆BC>-0.5 at each cohort were calculated and used to define quartiles through whole study period. Quartiles were used, one at a time, as threshold to dichotomize the cohorts to predict the risk that a cohort has a frequency of anestrus over the median.The higher AUC was used as selection criterium to determine the herd level threshold at each HERD and PARITY level. The population attributable fraction (AFP) of anestrus rate to body condition indicators at each cohort was calculated, for every HERD and PARITY level.

opencc-by-4.0Jul 2023View details →

Design Of A Condition Monitoring System (CMS) For A Gas Compressor Using Shell Bonny Terminal As A Case Study

Subcontracted Researcher & Field Electrical and Electronics Engineer, Shell Bonny Terminal, Nigeria— 2013 I served as a Hooks Electric subcontracted researcher and field electrical and electronics engineer at Shell Bonny Terminal, spearheading a groundbreaking project to design and construct a state-of-the-art software condition monitoring system (CMS) for a gas compressor. In addition to my technical responsibilities, I also took charge of integrating this innovative design into the existing SCADA (Supervisory Control and Data Acquisition) infrastructure of the plant. Working collaboratively with a team of four talented engineers, I successfully managed and coordinated their efforts throughout the project. Together, we leveraged our collective expertise to develop and implement a fully functional software condition monitoring system. This collaborative approach ensured that our project benefitted from diverse perspectives and skill sets, leading to a comprehensive and robust solution. Utilizing my proficiency in LabVIEW, C#, native C, Java and ActiveX library, I led the development of a cutting-edge data acquisition (DAQ) device for CM system. This device seamlessly captured sensor readings from the live gas compressor in real-time, while the systemic algorithm I designed skillfully tracked false alarms using fallback sensors. By monitoring critical parameters such as ambient and self-temperature, axial speed, vibration, inflow pressure, and outflow pressure, we were able to enhance the system's reliability and performance. Furthermore, I effectively integrated the software condition monitoring system into the plant's existing SCADA infrastructure, ensuring seamless communication and compatibility with the overall plant monitoring and control system. This integration facilitated centralized data management and provided comprehensive insights into the gas compressor's operation, enabling proactive maintenance and efficient decision-making. By successfully managing the team and overseeing the integration process, I ensured that our project aligned seamlessly with the plant's existing infrastructure, resulting in an optimized and harmonized system. Through our collective efforts, we delivered a highly sophisticated and fully integrated software condition monitoring system that significantly improved the management and performance of the gas compressor at Shell Bonny Terminal. I have curated all the resource files that will enable anyone replicate our work. This is shared under the CC-by-4 license. The LabVIEW codes are documented in Work.zip file and all manuals and equipment specifications have been uploaded. I have created a test bench to help anyone quickly test the LabVIEW software in simulation mode.  I have also provided a slide presentation that summarizes our work, including methodologies and results.

opencc-zeroJul 2023View details →

Monitoring moose in Montana using hunter observations to count moose by district

During 2012–2016, we queried hunters of deer and elk for observations of groups of a non-target species, moose, across their statewide distribution in Montana. We used buffer analyses to filter data according to independent observations in space and time and then tallied observations of groups by hunting district. We then analyzed data in an abundance-detection framework with n-mixture models and evaluated the effects of covariates such as hunter effort, survey response totals, weekly session, and forest cover on detection probability before using models to predict moose abundance. Lastly, we converted model predictions of group abundnace to total abundance by multiplying number of groups by average group size per administrative region.

opencc-by-4.0Aug 2023View details →

Results of pheromone monitoring of Dendrolimus sibiricus Chetverikov, 1908 on the territory of the Komi Republic

Collection and counting of adults of Dendrolimus sibiricus on the territory of the Komi Republic has been carried out annually since 2016 in the period from June to August by capturing males using pheromone traps. Seven forest areas located in the middle and southern taiga subzone were surveyed as part of the monitoring. More than 4000 pheromone traps of "Kvadro" type produced by the All-Russian Plant Quarantine Center (FGBU "VNIIKR") were used. Such a trap consists of a cardboard case with entomological glue on the inner surface, on which a silicone dispenser with a species-specific sex attractant - pheromone - is placed. The pheromone is a mixture of substances Z5,E7-dodecadienal and Z5,E7-dodecadienol. By analyzing the material from 2016-2022, a total of 1,618 individuals of D. sibiricus were detected and identified

opencc-zeroAug 2023View details →

Afognak Lake Sockeye Salmon Monitoring Project (2020–2022)

The Afognak River (Litnik) has historically supported one of the largest sockeye salmon subsistence fisheries for Kodiak Archipelago residents. In the 2000s, the number of sockeye salmon returning to Afognak River had diminished substantially resulting in closures to commercial, sport, and subsistence fishing in Afognak Bay. Local subsistence users, represented by the Kodiak-Aleutians Regional Advisory Council, Kodiak Fish and Game Advisory Committee, and Kodiak Tribal Council, stressed the importance of the Afognak River system to local subsistence users and contended that continued closures would make it more difficult for local residents to meet their sockeye salmon needs and shift fishing efforts to small nearby sockeye salmon runs. The Afognak Lake Sockeye Salmon Stock Monitoring Project enumerates adult sockeye salmon into Afognak Lake through a weir located on the Afognak River, describes run-timing, or proportional daily passage, of sockeye salmon through the weir, estimates the age and sex composition of adult sockeye salmon returns, and estimates the average length (mm) by age and sex of sockeye salmon passing through the weir. Data collected from this project provide salmon management biologists the ability to make in-season decisions to execute commercial and sport fisheries. In addition, the information that is gathered from these projects refines the escapement goals and improves preseason run forecasts to allow for maximum sustainable yield and prevent unnecessary restrictions of federal and state subsistence fisheries.