Evaluating the accuracy of automated processing of child and adult language production in preschool classrooms.

Young children's language and social development is influenced by the linguistic environment of their classrooms, including their interactions with teachers and peers. Measurement of the classroom linguistic environment typically relies on observational methods, often providing limited 'snapshots' of children's interactions, from which broad generalizations are made. Recent technological advances, including artificial intelligence, provide opportunities to capture children's interactions using continuous recordings representing much longer durations of time. The goal of the present study was to evaluate the accuracy of the Interaction Detection in Early Childhood Settings (IDEAS) system on 13 automated indices of language output using recordings collected from 19 children and three teachers over two weeks in an urban preschool classroom. The accuracy of language outputs processed via IDEAS were compared to ground truth via linear correlations and median absolute relative error. Findings indicate high correlations between IDEAS and ground truth data on measures of teacher and child speech, and relatively low error rates on the majority of IDEAS language output measures. Study findings indicate that IDEAS may provide a useful measurement tool for advancing knowledge about children's classroom experiences and their role in shaping development.

Automated System for Attomolar-Level Detection of MiRNA as a Biomarker for Influenza A Virus.

We have developed an automated sensing system for the repeated detection of a specific microRNA (miRNA) of the influenza A (H1N1) virus. In this work, magnetic particles functionalized with DNAs, target miRNAs, and alkaline phosphate (ALP) enzymes formed sandwich structures. These particles were trapped on nickel (Ni) patterns of our sensor chip by an external magnetic field. Then, additional electrical signals from electrochemical markers generated by ALP enzymes were measured using the sensor, enabling the highly sensitive detection of target miRNA. The magnetic particles used on the sensor were easily removed by applying the opposite direction of external magnetic fields, which allowed us to repeat sensing measurements. As a proof of concept, we demonstrated the detection of miRNA-1254, one of the biomarkers for the H1N1 virus, with a high sensitivity down to 1 aM in real time. Moreover, our sensor could selectively detect the target from other miRNA samples. Importantly, our sensor chip showed reliable electrical signals even after six repeated miRNA sensing measurements. Furthermore, we achieved technical advances to utilize our sensor platform as part of an automated sensing system. In this regard, our reusable sensing platform could be utilized for versatile applications in the field of miRNA detection and basic research.

The potential impact of acute coronary syndromes on automatic sensing system in Subcutaneous-ICDs.

BACKGROUND: The Subcutaneous-ICD (S-ICD) is emerging as a suitable option for most ICD candidates, however some open issues regarding the sensing algorithm still remain. OBJECTIVES: We aimed to examine the performance of the S-ICD sensing algorithm in patients hospitalized for ST elevation myocardial infarction (STEMI), non ST elevation acute coronary syndrome (NSTE-ACS) or chronic coronary syndrome (CCS), before and after revascularization. METHODS: We performed a S-ICD automated screening on 75 patients, 21 hospitalized for STEMI, 23 for NSTE-ACS and 31 for CCS, before and after percutaneous revascularization, regardless their eligibility to ICD implantation. RESULTS: Patients did not differ in clinical, electrocardiographic and echocardiographic parameters. Rates of screening pass were significantly lower in STEMI patients compared to NSTE-ACS and CCS (5% vs 56.7% vs 81% respectively, p < .0001). The viability of the primary vector was lower in STEMI patients compared to NSTE-ACS and CCS (33% vs 56% vs 71%, p .027 respectively). After revascularization, there were no more significant differences between groups. Pairing subjects at baseline and after revascularization, STEMI subjects percentages of screening success were respectively 5% and 81% (p < .001) and the rates of primary vector viability were 33% and 81% (p .002). STEMI was the only independent predictor of screening failure at multivariate logistic regression analysis (odds ratio 10.68 confidence interval 2.77-41.38, p = .001). CONCLUSION: The performance of the S-ICD and possible malfunction detections in the context of an acute ischemic event deserve further evaluation. Adequate patient selection and the development of dynamic device programming are warranted.

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning.

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel's work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data.

In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.

An Opto-Electronic Sensor for Detecting Soil Microarthropods and Estimating Their Size in Field Conditions.

Methods to estimate density of soil-dwelling arthropods efficiently, accurately and continuously are critical for investigating soil biological activity and evaluating soil management practices. Soil-dwelling arthropods are currently monitored manually. This method is invasive, and time- and labor-consuming. Here we describe an infrared opto-electronic sensor for detection of soil microarthropods in the size range of 0.4-10 mm. The sensor is built in a novel microarthropod trap designed for field conditions. It allows automated, on-line, in situ detection and body length estimation of soil microarthropods. In the opto-electronic sensor the light source is an infrared LED. Two plano-convex optical lenses are placed along the virtual optical axis. One lens on the receiver side is placed between the observation space at 0.5-1 times its focal length from the sensor, and another emitter side lens is placed between the observation space and the light source in the same way. This paper describes the setup and operating mechanism of the sensor and the control unit, and through basic tests it demonstrates its potential in automated detection of soil microarthropods. The sensor may be used for monitoring activities, especially for remote observation activities in soil and insect ecology or pest control.