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The extant literature has indicated that comparison with high-performers could lead to negative emotions and behaviors among colleagues (including superiors). In contrast, other researchers also demonstrated that comparison can effectively motivate low-performance employees to work hard. In this study, we apply the temporal dimension to social comparison to explore how temporal social comparison influences the actor's coping behaviors. A scenario experiment and a three-wave questionnaire survey study show that the performance temporal social comparison (PTSC) can lead to the actor's social undermining towards the target (leveling-down) and effectively promote the actor's self-improvement (leveling-up) simultaneously. Based on the cognitive appraisal theory of stress, we have further tested the significant mediating effects of challenge/hindrance stress. We also find that the actor's general self-efficacy is an essential individual characteristic that affects stress perceptions and moderates the indirect effects of PSTC on different coping behaviors. Overall, our findings enrich the social comparisons theory development and provide critical managerial implications for high-potential talent management., Author(s): Jinfan Zhou [sup.1] , Yuanfang Zhan [sup.2] , Huan Cheng [sup.2] , Guanglei Zhang [sup.3] Author Affiliations: (1) grid.41156.37, 0000 0001 2314 964X, School of Business, Nanjing University, , [...]

Advances in information and communication technologies have made organizations more efficient and flexible. However, such technologies have changed how employees work and blur the lines between work and nonwork domains. Drawing on theories of self‐regulation and moral disengagement, this study investigates how and when work‐related electronic communication during nonwork time leads to employees' negative behaviors. Using two daily survey across two consecutive weeks, we found that work‐related electronic communication during nonwork time increased the level of employees' self‐regulation depletion and subsequent moral disengagement, resulting in increased workplace deviance the next day. Moreover, segmentation preference amplified the serial indirect effect of work‐related electronic communication during nonwork time on workplace deviance (via self‐regulation depletion and moral disengagement). Our findings extend the research on work‐related electronic communication during nonwork time by offering further insights into its behavioral consequences. [ABSTRACT FROM AUTHOR]

With the rise of deep learning in recent years, it has made major breakthroughs in machine learning fields such as target detection, image classification, speech recognition, and natural language processing. Among them, convolutional neural networks are widely used in deep learning. Since the emergence of VGGNet, neural networks have gradually developed deeper and the network has become deeper. This not only increases the demand for operating memory and running memory, but also greatly increases the amount of computation. The requirements for hardware platform resources are getting higher and higher. Therefore, it is particularly difficult to apply deep learning to embedded platforms. In this paper, by pruning the model, the trained network model is compressed, the unimportant parameters are eliminated, the model is reduced, and the computational complexity of the network is reduced. The deep learning will be applied to the embedded platform.

This paper proposes a design scheme for chest X-ray images analysis by using embedded technology and deep learning technology. The hardware platform of the analysis system using NIVIDIA′s Jetson TX2 as the core board, equipped with Ethernet modules, WiFi modules and other functional modules. It uses the MobileNets convolutional neural network on GPU server to train the marked chest X-ray image dataset then transplants the trained model to the Jetson TX2 core board, detecting the symptoms of pleural effusion, infiltration, emphysema, pneumothorax and atelectasis on the embedded platform. The chest X-ray image data provided by the National Institutes of Health(NIH) were tested in the trained model. Experiments have shown that this method gets higher accuracy and requires less time than other methods.

Ultra-high-concentration PNP-contained wastewaters are produced sometimes due to the wide application of this nitrophenolic compound in the chemical industry. However, there is a lack of appropriate technologies to rapidly pretreat the ultra-high-concentration wastewater. Therefore, a new microwave-enhanced Fe/Cu bimetallic particles (MW-Fe/Cu) system was developed to rapidly remove ultra-high-concentration PNP. First, the priority of the determinative parameters was obtained by orthogonal experiment. Based on this result, the effects of initial pH, microwave power, Fe/Cu dosage and initial PNP concentration on PNP removal were optimized thoroughly. Under the optimal conditions (i.e. initial pH = 1.0, MW power = 385 W, Fe/Cu dosage = 30 g/L and initial PNP concentration = 4000 mg/L), four control treatment systems (i.e. MW-Fe0, heating-Fe/Cu, MW alone and Fe/Cu alone system) were set up to compare with the MW-Fe/Cu system. The results suggest that high PNP removal (more than 99% with 2.5 min, k1/k2 = 1.18/6.91 min−1) and COD removal (26.6% with 5 min treatment) could be obtained by the MW-Fe/Cu system, which were much superior to those obtained using the MW-Fe0 (k1/k2 = 0.62/2.21 min−1) and the heating-Fe/Cu system (k1/k2 = 0.53/1.52 min−1). Finally, the determination of the intermediates of PNP degradation by HPLC indicated that the MW assistance process did not change the degradation pathway of PNP. This concludes that the new MW-Fe/Cu system was the promising technology for pretreatment of wastewater containing ultra-high-concentration toxic and refractory pollutants at a fairly short treatment time. [ABSTRACT FROM AUTHOR]

Purpose: Microsurgical Aneurysm Clipping Surgery (MACS) carries a high risk for intraoperative aneurysm rupture. Automated recognition of instances when the aneurysm is exposed in the surgical video would be a valuable reference point for neuronavigation, indicating phase transitioning and more importantly designating moments of high risk for rupture. This article introduces the MACS dataset containing 16 surgical videos with frame-level expert annotations and proposes a learning methodology for surgical scene understanding identifying video frames with the aneurysm present in the operating microscope's field-of-view., Methods: Despite the dataset imbalance (80% no presence, 20% presence) and developed without explicit annotations, we demonstrate the applicability of Transformer-based deep learning architectures (MACSSwin-T, vidMACSSwin-T) to detect the aneurysm and classify MACS frames accordingly. We evaluate the proposed models in multiple-fold cross-validation experiments with independent sets and in an unseen set of 15 images against 10 human experts (neurosurgeons)., Results: Average (across folds) accuracy of 80.8% (range 78.5-82.4%) and 87.1% (range 85.1-91.3%) is obtained for the image- and video-level approach, respectively, demonstrating that the models effectively learn the classification task. Qualitative evaluation of the models' class activation maps shows these to be localized on the aneurysm's actual location. Depending on the decision threshold, MACSWin-T achieves 66.7-86.7% accuracy in the unseen images, compared to 82% of human raters, with moderate to strong correlation., Conclusions: Proposed architectures show robust performance and with an adjusted threshold promoting detection of the underrepresented (aneurysm presence) class, comparable to human expert accuracy. Our work represents the first step towards landmark detection in MACS with the aim to inform surgical teams to attend to high-risk moments, taking precautionary measures to avoid rupturing., (© 2023. The Author(s).)