Characteristics, potential sources, and cancer risk apportionment of PM-bound polycyclic aromatic hydrocarbons in Bengbu, Central China.

Polycyclic aromatic hydrocarbons (PAHs) were measured in 342 daily PM samples collected in four seasons at a site in Bengbu, China. This study was a qualitative and quantitative investigation of the emission sources of atmospheric PAHs in Bengbu and the spatial distribution of regional PAH sources in PM samples. The annual concentrations of the 16 EPA priority PAHs ranged from 1.

Environmental factors and particle size shape the community structure of airborne total and pathogenic bacteria in a university campus.

Given the dense population on university campuses, indoor and outdoor airborne bacterial contamination may lead to the rapid spread of diseases in a university environment. However, there are few studies of the characteristics of airborne and pathogenic bacterial communities in different sites on a university campus. In this study, we collected particulate matter samples from indoor and outdoor locations at a university in Bengbu City, Anhui Province, China, and analyzed the community characteristics of airborne and pathogenic bacteria using a high-throughput sequencing technique.

Source analysis and health risk assessment of polycyclic aromatic hydrocarbon (PAHs) in total suspended particulate matter (TSP) from Bengbu, China.

The polycyclic aromatic hydrocarbon (PAH) concentrations in total suspended particulate matter (TSP) samples collected from October, 2021 to September, 2022 were analyzed to clarify the pollution characteristics and sources of 16 PAHs in the atmospheric TSP in Bengbu City. The ρ(PAHs) concentrations ranged from 1.71 to 43.

Deep Temporal Model-Based Identity-Aware Hand Detection for Space Human-Robot Interaction.

Hand detection is a crucial technology for space human-robot interaction (SHRI), and the awareness of hand identities is particularly critical. However, most advanced works have three limitations: 1) the low detection accuracy of small-size objects; 2) insufficient temporal feature modeling between frames in videos; and 3) the inability of real-time detection. In the article, a temporal detector (called TA-RSSD) is proposed based on the SSD and spatiotemporal long short-term memory (ST-LSTM) for real-time detection in SHRI applications.

How to Represent Paintings: A Painting Classification Using Artistic Comments.

The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques.

Compare the performance of the models in art classification.

Because large numbers of artworks are preserved in museums and galleries, much work must be done to classify these works into genres, styles and artists. Recent technological advancements have enabled an increasing number of artworks to be digitized. Thus, it is necessary to teach computers to analyze (e.

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing.

The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations.

Towards Zero Re-Training for Long-Term Hand Gesture Recognition via Ultrasound Sensing.

While myoelectric pattern recognition is a prevailing way for gesture recognition, the inherent nonstationarity of electromyography signals hinders its long-term application. This study aims to prove a hypothesis that morphological information of muscle contraction detected by ultrasound image is potentially suitable for long-term use. A set of ultrasound-based algorithms are proposed to realize robust hand gesture recognition over multiple days, with user training only at the first day.

Ultrasound-Based Sensing Models for Finger Motion Classification.

Motions of the fingers are complex since hand grasping and manipulation are conducted by spatial and temporal coordination of forearm muscles and tendons. The dominant methods based on surface electromyography (sEMG) could not offer satisfactory solutions for finger motion classification due to its inherent nature of measuring the electrical activity of motor units at the skin's surface. In order to recognize morphological changes of forearm muscles for accurate hand motion prediction, ultrasound imaging is employed to investigate the feasibility of detecting mechanical deformation of deep muscle compartments in potential clinical applications.

Towards Wearable A-Mode Ultrasound Sensing for Real-Time Finger Motion Recognition.

It is evident that surface electromyography (sEMG) based human-machine interfaces (HMI) have inherent difficulty in predicting dexterous musculoskeletal movements such as finger motions. This paper is an attempt to investigate a plausible alternative to sEMG, ultrasound-driven HMI, for dexterous motion recognition due to its characteristic of detecting morphological changes of deep muscles and tendons. A multi-channel A-mode ultrasound lightweight device is adopted to evaluate the performance of finger motion recognition; an experiment is designed for both widely acceptable offline and online algorithms with eight able-bodied subjects employed.