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Parental burnout refers to exhaustion caused by the parenting role. This devastating negative emotion can have repercussions for adolescent social development. Nevertheless, much remains unclear about the association between parental burnout and adolescent prosocial behavior and the potential mechanisms underlying this relationship. Based on theoretical and empirical evidence, the current study examined the relationship between parental burnout and adolescent prosocial behavior by using a sequential mediation model that included both parental empathy and adolescent empathy as potential mediators. A total of 488 parent–adolescent dyads (for adolescents: 45.7% men, 54.3% women, Mage = 15.28 ± 1.67 years; for parents: 36.5% fathers, 63.5% mothers, Mage = 41.30 ± 3.79 years) completed questionnaires regarding demographics, social desirability, parental burnout, parental empathy, adolescent empathy, and adolescent prosocial behavior. After controlling for demographic covariates and social desirability, the results showed that parental burnout had a negative effect on adolescent-reported prosocial behavior through parental cognitive empathy and adolescent other-oriented empathy (adolescent cognitive empathy and empathic concern) sequentially. These findings contribute to our understanding of the role of parental burnout as a family environmental factor detrimental to the positive functioning of adolescents through parental reactions to their children's emotions and children's own social competence. [ABSTRACT FROM AUTHOR]

In this paper, the influence of the silicon (Si) content on microstructure and impact property of submerged arc weld metals (WMs) for weathering bridge steel was clarified. Actual submerged arc welding (SAW) was carried out to produce WMs with 0.18 wt.%, 0.36 wt.%, 0.51 wt.%, and 0.60 wt.% of Si. The low temperature impact property of weld metal was detected, and the weld microstructures were characterized by optical microscopy (OM), scanning and transmission electron microscope (SEM and TEM), and electron backscatter diffraction (EBSD). The results indicate that WMs consist of polygon ferrite (PF), acicular ferrite (AF), granular bainitic ferrite (GBF), and martensite/austenite (M/A) constituents in each Si content. With increasing Si, the proportion of PF increased, while AF and GBF coarsened, the area fraction, fM/A, and the mean size, dM/A, of M/A constituents increased, the number of inclusions decreased, but the size increased. Further, the fraction of high-angle grain boundaries (HAGBs) with the misorientation tolerance angles (MTAs) greater than 15° reduced, while the mean equivalent diameter, MEDMTA≥15°, of ferrite grains with HAGBs increased. Accordingly, the impact toughness of WM was degraded from 108.1 J to 39.4 J with the increase in Si. The increase in M/A constituents and inclusions size reduced the critical fracture stress, thereby formation of larger microcracks and cleavage planes occurred. The reduced HAGBs exhibited a low hindering effect on crack propagation, and, consequently, the impact toughness decreased with increasing Si content. [ABSTRACT FROM AUTHOR]

The development of high-efficiency multi-wire submerged arc welding technology in bridge engineering has been limited due to the high mechanical performance standards required. In this paper, weld metal was obtained by welding at three different high heat inputs with the laboratory-developed high-efficiency submerged arc welding wire for bridges. The effect of changing different high heat inputs on the microstructure and impact toughness of high efficiency submerged arc weld metal was systematically investigated by cutting and Charpy V-notch impact tests at −40 °C, using optical microscopy, scanning electron microscopy, energy-dispersive electron spectroscopy, electron backscatter diffraction, and transmission electron microscopy to characterize and analyze. With the increase in heat input from 50 kJ/cm to 100 kJ/cm, the impact absorption energy decreased significantly from 130 J to 38 J. The number of inclusions in the weld metal significantly decreased and the size increased, which led to a significant decrease in the number of inclusions that effectively promote acicular ferrite nucleation, further leading to a decrease in the proportion of acicular ferrite in the weld metal. At the same time, the microstructure of the weld metal was significantly coarsened, the percentage of high-angle grain boundaries was decreased, and the size of martensite/austenite constituents was significantly increased monotonically. The crack initiation energy was reduced by the coarsened martensite/austenite constituents and inclusions, which produced larger local stress concentrations, and the crack propagation was easier due to the coarsened microstructure and lower critical stress for crack instability propagation. The martensite/austenite constituents and inclusions in large sizes worked together to cause premature cleavage fracture of the impact specimen, which significantly deteriorated the impact toughness. The heat input should not exceed 75 kJ/cm for high-efficiency submerged arc welding wires for bridges. [ABSTRACT FROM AUTHOR]

Recently, Wi-Fi channel state information (CSI) motion detection systems have been widely researched for applications in human health care and security in flat floor environments. However, these systems disregard the indoor context, which is often complex and consists of unique features, such as staircases. Motion detection on a staircase is also meaningful and important for various applications, such as fall detection and intruder detection. In this paper, we present the difference in CSI motion detection in flat floor and staircase environments through analysing the radio propagation model and experiments in real settings. For comparison in the two environments, an indoor CSI motion detection system is proposed with several novel methods including correlation-based fusion, moving variance segmentation (MVS), Doppler spread spectrum to improve the system performance, and a correlation check to reduce the implementation cost. Compared with existing systems, our system is validated to have a better performance in both flat floor and staircase environments, and further utilized to verify the superior CSI motion detection performance in staircase environments versus flat floor environments.

In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference., Competing Interests: The authors declare no conflicts of interest.

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise., Competing Interests: The authors declare no conflicts of interest.

Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices' random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.