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Over the past two decades, numerous research projects have concentrated on cognitive radio wireless sensor networks (CR-WSNs) and their benefits. To tackle the problem of energy and spectrum shortfall in CR-WSNs, this research proposes an underpinning decode-&-forward (DF) relaying technique. Using the suggested time-slot architecture (TSA), this technique harvests energy from a multi-antenna power beam (PB) and delivers source information to the target utilizing energy-constrained secondary source and relay nodes. The study considers three proposed relay selection schemes: enhanced hybrid partial relay selection (E-HPRS), conventional opportunistic relay selection (C-ORS), and leading opportunistic relay selection (L-ORS). We present evidence for the sustainability of the suggested methods by examining the outage probability (OP) and throughput (TPT) under multiple primary users (PUs). These systems leverage time switching (TS) receiver design to increase end-to-end performance while taking into account the maximum interference constraint and transceiver hardware inadequacies. In order to assess the efficacy of the proposed methods, we derive the exact and asymptotic closed-form equations for OP and TPT & develop an understanding to learn how they affect the overall performance all across the Rayleigh fading channel. The results show that OP of the L-ORS protocol is 16% better than C-ORS and 75% better than E-HPRS in terms of transmitting SNR. The OP of L-ORS is 30% better than C-ORS and 55% better than E-HPRS in terms of hardware inadequacies at the destination. The L-ORS technique outperforms C-ORS and E-HPRS in terms of TPT by 4% and 11%, respectively.

An important aspect of any social interaction involves inferring other people's mental states, intentions, and their likely next actions, by way of facial expression, body posture, eye gaze, and limb movements. An actor's production of actions during social interactions and the observer's perception of these actions are thus closely linked. In this review, we outline an action-observation methodology, which not only allows for separate analyses of production and perception, but also promotes the study of the dynamic interaction between these two sides of every social exchange. We review two lines of research that have benefited from its application. The first line focuses on individuals performing tasks alone and the observation of their actions by other individuals in order to make inferences about their attentional states. The second line of study focused on pairs of individuals performing collaborative tasks in naturalistic settings and the observation of these performances by other individuals. We offer several suggestions for how this methodology can be extended to improve on the limitations of the present studies, as well as some suggestions of how to use this methodology to venture into new territory. Our aim is to inspire future research applications of this methodology in order to advance our understanding of social action production and perception. [ABSTRACT FROM AUTHOR]

Recently, deep-learning-based low-light image enhancement (LLIE) methods have made great progress. Benefiting from elaborately designed model architectures, these methods enjoy considerable performance gains. However, the generalizability of these methods may be weak, and they may suffer from the overfitting risk in the case of insufficient data as a result. At the same time, their complex model design brings serious computational burdens. To further improve performance, we exploit dual information, including spatial and channel (contextual) information, in the high-dimensional feature space. Specifically, we introduce customized spatial and channel blocks according to the feature difference of different layers. In shallow layers, the feature resolution is close to that of the original input image, and the spatial information is well preserved. Therefore, the spatial restoration block is designed for leveraging such precise spatial information to achieve better spatial restoration, e.g., revealing the textures and suppressing the noise in the dark. In deep layers, the features contain abundant contextual information, which is distributed in various channels. Hence, the channel interaction block is incorporated for better feature interaction, resulting in stronger model representation capability. Combining the U-Net-like model with the customized spatial and channel blocks makes up our method, which effectively utilizes dual information for image enhancement. Through extensive experiments, we demonstrate that our method, despite its simplicity of design, can provide advanced or competitive performance compared to some state-of-the-art deep learning- based methods. [ABSTRACT FROM AUTHOR]

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU. [ABSTRACT FROM AUTHOR]

Over the past two decades, numerous research projects have concentrated on cognitive radio wireless sensor networks (CR-WSNs) and their benefits. To tackle the problem of energy and spectrum shortfall in CR-WSNs, this research proposes an underpinning decode-&-forward (DF) relaying technique. Using the suggested time-slot architecture (TSA), this technique harvests energy from a multi-antenna power beam (PB) and delivers source information to the target utilizing energy-constrained secondary source and relay nodes. The study considers three proposed relay selection schemes: enhanced hybrid partial relay selection (E-HPRS), conventional opportunistic relay selection (C-ORS), and leading opportunistic relay selection (L-ORS). We present evidence for the sustainability of the suggested methods by examining the outage probability (OP) and throughput (TPT) under multiple primary users (PUs). These systems leverage time switching (TS) receiver design to increase end-to-end performance while taking into account the maximum interference constraint and transceiver hardware inadequacies. In order to assess the efficacy of the proposed methods, we derive the exact and asymptotic closed-form equations for OP and TPT & develop an understanding to learn how they affect the overall performance all across the Rayleigh fading channel. The results show that OP of the L-ORS protocol is 16% better than C-ORS and 75% better than E-HPRS in terms of transmitting SNR. The OP of L-ORS is 30% better than C-ORS and 55% better than E-HPRS in terms of hardware inadequacies at the destination. The L-ORS technique outperforms C-ORS and E-HPRS in terms of TPT by 4% and 11%, respectively. [ABSTRACT FROM AUTHOR]

People frequently regulate their own behaviour in an effort to be socially appropriate. Here we ask how self-monitoring influences our accuracy when reading others' facial expressions. We used webcams and pre-programmed conversations to induce self-monitoring or other-monitoring in participants, before they classified the affective facial expressions of video-recorded actors. Two experiments showed that self-monitoring reduces sensitivity to affective facial expression in others. Experiment 1 showed that self-monitoring participants were less sensitive to emotional facial expressions than other-monitoring and neutral condition participants. Experiment 2 found the same result, but only in participants who rated the pre-programmed conversations as high in believability. We discuss possible mechanisms by which this may occur, including the role of social stress, divided attention, and automatic latent imitation when processing others' facial expressions. [ABSTRACT FROM AUTHOR]

We use the transaction cost theory, originally proposed by Coase (1937), as a theoretical framework to examine the potential impact of the blockchain technology on accounting and auditing processes in terms of information timeliness, information quality, and auditing costs. Since a blockchain enables recording, tracking, and managing business transactions and assets of firms, it offers natural advantages to accounting and auditing processes, and has the potential to lower various transaction costs. A use case on a supply chain of food products is presented to illustrate how a blockchain can be used to trace the movement of goods and record the related transactions. Subsequently, building on this scenario, we use the transaction cost framework to generate propositions related to accounting and auditing that can be empirically tested in future studies as data become available. JEL Classifications: D83; G14; M41; M42; O33. [ABSTRACT FROM AUTHOR]