Understanding the Effect of Social Media Overload on Academic Performance: A Stressor-Strain-Outcome Perspective

Social media has deeply penetrated into university students’ daily lives, inducing excessive usage that can result in social media overload. However, only few studies have explored the adverse consequences of social media use from a pedagogical perspective. This paper aims to investigate the effects of overload on students’ academic performance and the underlying mechanism. Based on the stressor-strain-outcome model, we propose that information, communication, and social overloads influence technostress and exhaustion of students, which in turn impair their academic performance. Results from a study of 249 Chinese social media users in universities reveal that all three types of overload enhance technostress, but only information overload significantly affect exhaustion. Both technostress and exhaustion have negative effects on academic performance. This study enriches social media literature by identifying a more comprehensive classification of social media-related overload among university students and investigating the exact mechanism of excessive social media use in educational environment

Correction: Yoshie, T. et al. Optical Microcavity: Sensing down to Single Molecules and Atoms. Sensors 2011, 11, 1972–1991

The coefficient of the expression of Equation (6) was not properly written

Learning Spiking Neural Network from Easy to Hard task

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL

Intelligent Knowledge Beyond Data Mining: Influences of Habitual Domains

Data mining is a useful analytic method and has been increasingly used by organizations to gain insights from large-scale data. Prior studies of data mining have focused on developing automatic data mining models that belong to first-order data mining. Recently, researchers have called for more study of the second-order data mining process. Second-order data mining process is an important step to convert data mining results into intelligent knowledge, i.e., actionable knowledge. Specifically, second-order data mining refers to the post-stage of data mining projects in which humans collectively make judgments on data mining models’ performance. Understanding the second-order data mining process is valuable in addressing how data mining can be used best by organizations in order to achieve competitive advantages. Drawing on the theory of habitual domains, this study developed a conceptual model for understanding the impact of human cognition characteristics on second-order data mining. Results from a field survey study showed significant correlations between habitual domain characteristics, such as educational level and prior experience with data mining, and human judgments on classifiers’ performance

Electroacupuncture Inhibits Visceral Nociception via Somatovisceral Interaction at Subnucleus Reticularis Dorsalis Neurons in the Rat Medulla

Electroacupuncture (EA) is an efficacious treatment for alleviating visceral pain, but the underlining mechanisms are not fully understood. This study investigated the role of medullary subnucleus reticularis dorsalis (SRD) neurons in the effects of EA on visceral pain. We recorded the discharges of SRD neurons extracellularly by glass micropipettes on anesthetized rats. The responses characteristics of SRD neurons to different intensities of EA (0.5, 1, 2, 4, 6, and 8 mA, 0.5 ms, and 2 Hz) on acupoints “Zusanli” (ST 36) and “Shangjuxu” (ST 37) before and during noxious colorectal distension (CRD) were analyzed. Our results indicated that SRD neurons responded to either a noxious EA stimulation ranging from 2 to 8 mA or to noxious CRD at 30 and 60 mmHg by increasing their discharge frequency at an intensity-dependent manner. However, during the stimulation of both CRD and EA, the increasing discharges of SRD neurons induced by CRD were significantly inhibited by 2–8 mA of EA. Furthermore, SRD neurons can encode the strength of EA, where a positive correlation between current intensity and the magnitude of neuronal responses to EA was observed within 2–6 mA. Yet, the responses of SRD neurons to EA stimulation reached a plateau when EA exceeded 6 mA. In addition, 0.5–1 mA of EA had no effect on CRD-induced nociceptive responses of SRD neurons. In conclusion, EA produced an inhibiting effect on visceral nociception in an intensity-dependent manner, which probably is due to the somatovisceral interaction at SRD neurons