Implementation and initial analysis of Cardiff Model data collection procedures in a level I trauma adult emergency department

ObjectivesOur understanding of community violence is limited by incomplete information, which can potentially be resolved by collecting violence-related injury information through healthcare systems in tandem with prior data streams. This study assessed the feasibility of implementing Cardiff Model data collection procedures in the emergency department (ED) setting to improve multisystem data sharing capabilities and create more representative datasets.DesignInformation collection fields were incorporated into the ED electronic health record (EHR), which gathered additional information from patients reporting assaultive injuries. ED nurses were surveyed to evaluate implementation and feasibility of information collection. Logistic regression was performed to determine associations between missing location information and patient demographic data.Setting60-bed academic level I trauma adult ED in a large Midwestern city.Participants2648 patients screened positive for assault injuries between 2017 and 2020. 198 patients were omitted due to age outside the range served by this ED. Unselected inclusion of 150 ED nurses was surveyed.Main outcome measuresMain outcomes include nursing staff survey responses and ORs for providing complete injury information across various patient demographics.ResultsMost ED nurses believed that information collection aligned with the hospital’s mission (92%), wanted information collection to continue (88%), did not believe that information collection impacted their workflow (88%), and reported taking under 1 min to screen and document violence information (77%). 825 patients (31.2%) provided sufficient information for geospatial mapping. Likelihood of providing complete location information was significantly associated with patient gender, race, arrival means, accompaniment, trauma type and year.ConclusionsIt is feasible to implement information collection procedures about location-based, assault-related injuries through the EHR in the adult ED setting. Nurses reported being receptive to collecting information. Analyses suggest patient-level and time variables impact information collection completeness. The geospatial information collected can greatly improve preexisting law enforcement and emergency medical systems datasets.

Distribution of the alpine newt Ichthyosaura alpestris in Great Britain updated using social media

The alpine newt (Ichthyosaura alpestris) is an alien species in Great Britain. Using location information derived from photographs posted on social media we have updated its known distribution, validated previously unconfirmed populations, and present an updated distribution map. Comparison of the records collected from social media with those in the National Biodiversity Network Atlas indicates eleven new confirmed populations, although three of these had previously been shown as unconfirmed records in the NBN Atlas. The new records have been deposited with NBN.

Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.

The Implementation of a Red Object Tracking Algorithm with a Single, Static Camera

In recent years, activity recognition and object tracking are receiving extensive attention due to the increasing demand for adaptable surveillance systems. Activity recognition is guided by the parameters such as the shape, size, and color of the object. This article purposes an examination of the performance of existing color-based object detection and tracking algorithms using thermal/visual camera-based video steaming in MATLAB. A framework is developed to detect and track red moving objects in real time. Detection is carried out based on the location information acquired from an adaptive image processing algorithm. Coordinate extraction is followed by tracking and locking the object with the help of a laser barrel. The movement of the laser barrel is controlled with the help of an 8051 microcontroller. Location information is communicated from the image-processing algorithm to the microcontroller serially. During implementation, a single static camera is used that provides 30 frames per second. For each frame, 88 ms are required to complete all three steps from detection to tracking, to locking, so a processing speed of 12 frames per second is implemented. This repetition makes the setup adaptive to the environment despite the presence of a single static camera. This setup can handle multiple objects with shades of red and has demonstrated equally good results in varying outdoor conditions. Currently, the setup can lock only single targets, but the capacity of the system can be increased with the installation of multiple cameras and laser barrels.

Twin Similarity for Neighborhood, Geographic Mobility, and Health Outcomes in Late Adulthood

Socioeconomic status (SES) is one of the most robust predictors of health. The source of SES-health associations is heavily debated; one approach is investigating neighborhood-level environmental characteristics. Challenges include selection effects and the possibility of reverse causation: people choose their neighborhoods. Longitudinal twin research can overcome these issues by assessing location choice over time as well as twin similarity; however, few existing twin studies have incorporated neighborhood-level data, and none of those focus on aging. Using longitudinal data from the Swedish Adoption/Twin Study of Aging, the current study examined the impact of location at various points in life. Location at birth and in 1993 were available for 972 participants. Birth years ranged from 1926 to 1948; mean age in 1993 was 54.55 (range = 35-67). Thirty-nine percent of the sample had moved to a different county between birth and midlife: individuals who moved had significantly higher parental SES and had achieved significantly higher education. Moreover, identical twin concordance for geographic mobility (77%) was significantly higher than fraternal twin concordance (65%), indicating a modest but significant genetic contribution. Geographic mobility did not impact identical twin similarity on a functional aging factor (corrected for age and education), but fraternal twins concordant for mobility were more similar than discordant twins, suggesting genetic contributions to mobility may also impact health. Ongoing retrieval of location information for twins born 1900-1925 and geocoding of location information available at 9 waves of data collection will allow for expanded investigation of the SES-health relationship at the neighborhood level.

Data Privacy in Wearable IoT Devices: Anonymization and Deanonymization

With the development of IoT devices, wearable devices are being used to record various types of information. Wearable IoT devices are attached to the user and can collect and transmit user data at all times along with a smartphone. In particular, sensitive information such as location information has an essential value in terms of privacy, and therefore some IoT devices implement data protection by introducing methods such as masking. However, masking can only protect privacy to a certain extent in logs having large numbers of recorded data. However, the effectiveness may decrease if we are linked with other information collected from within the device. Herein, a scenario-based case study on deanonymizing anonymized location information based on logs stored in wearable devices is described. As a result, we combined contextual and direct evidence from the collected information. It was possible to obtain the result in which the user could effectively identify the actual location. Through this study, not only can a deanonymized user location be identified but we can also confirm that cross-validation is possible even when dealing with modified GPS coordinates.

Artificial neural network for improving the estimation of weighted mean temperature in Egypt

One of the most important parameters in meteorological data is the Precipitable Water Vapor (PWV). It can be measured by radiosonde stations (RS), but the fact is that RS are not available in all times. Therefore, GNSS satellite signals are considered an accurate function to compute it within a conversation factor. The conversation factor depends on the weighted mean temperature ( T m {T_{m}} ) which is non-measurable. In this research, a new idea to estimate T m {T_{m}} is provided, which can potentially contribute to the GNSS meteorology. The T m {T_{m}} was designed, including six RS, over one year in Egypt as input parameters. The machine learning (ML) model has been utilized in the design (IBM SPSS Statistics 25 package). The new model needs to collect the day of year (DOY), site location information and surface temperature to predict the T m {T_{m}} . The results of ML model and four other empirical models (Bevis et al., Wayan and Iskanda, Yao and Elhaty et al. models) are compared. The validation work is carried out, using the radiosonde data, and results indicate that the new T m {T_{m}} model can achieve the best performance with RMS of 1.7 K.

Effects of species and geo-information on the 137Cs concentrations in edible wild mushrooms and plants collected by residents after the Fukushima nuclear accident

After the accident at the Fukushima Daiichi Nuclear Power Plant (FDNPP), much of the wild and edible mushrooms and plants in the surrounding areas were contaminated with radiocesium (137Cs). To elucidate their concentration characteristics, we analyzed 137Cs radioactivity data in edible forest products brought in for food inspection by the residents of Kawauchi Village, 12–30 km away from the FDNPP, from 2012 to 2019. A Bayesian model to estimate 137Cs concentration was constructed. Parameters of the normalized concentration of species (NCsp) for mushrooms were similar to those of the same species obtained in a previous study. Although NCsp values were highly varied among species, mycorrhizal mushrooms tended to have high NCsp values, followed by saprotrophic mushrooms, and wild edible plants values were low. Also, half of mycorrhizal mushroom species (8 of 16) showed an increasing trend in concentration with time; however, saprotrophic mushrooms and wild plants generally demonstrated a decreasing trend (22 of 24). The model considering the sub-village location information decreased the error of individual samples by 40% compared to the model not considering any location information, indicating that the detailed geo-information improved estimation accuracy. Our results indicate that the radioactivity data from samples collected by local residents can be used to accurately assess internal exposure to radiation due to self-consumption of contaminated wild mushrooms and plants.

Obstacle Detection Based on 3D Lidar Euclidean Clustering

Environment perception is the basis of unmanned driving and obstacle detection is an important research area of environment perception technology. In order to quickly and accurately identify the obstacles in the direction of vehicle travel and obtain their location information, combined with the PCL (Point Cloud Library) function module, this paper designed a euclidean distance based Point Cloud clustering obstacle detection algorithm. Environmental information was obtained by 3D lidar, and ROI extraction, voxel filtering sampling, outlier point filtering, ground point cloud segmentation, Euclide clustering and other processing were carried out to achieve a complete PCL based 3D point cloud obstacle detection method. The experimental results show that the vehicle can effectively identify the obstacles in the area and obtain their location information.