Active Working Memory and Simple Cognitive Operations

Working memory is thought to serve as a buffer for ongoing cognitive operations, even in tasks that have no obvious memory requirements. This conceptualization has been supported by dual-task experiments, in which interference is observed between a primary task involving short-term memory storage and a secondary task that presumably requires the same buffer as the primary task. Little or no interference is typically observed when the secondary task is very simple. Here, we test the hypothesis that even very simple tasks require the working memory buffer, but interference can be minimized by using activity-silent representations to store the information from the primary task. We tested this hypothesis using dual-task paradigm in which a simple discrimination task was interposed in the retention interval of a change detection task. We used contralateral delay activity (CDA) to track the active maintenance of information for the change detection task. We found that the CDA was massively disrupted after the interposed task. Despite this disruption of active maintenance, we found that performance in the change detection task was only slightly impaired, suggesting that activity-silent representations were used to retain the information for the change detection task. A second experiment replicated this result and also showed that automated discriminations could be performed without producing a large CDA disruption. Together, these results suggest that simple but non-automated discrimination tasks require the same processes that underlie active maintenance of information in working memory.

Aviation Services in Tourism during the COVID-19 Pandemic

In this paper, an investigation is being made over the tendency of air flights rate around the world due to the COVID-19 pandemic in 2020 considering all the negative effects in limiting tourist mobility. There is an attempt to support the customer activities in tourism through travel information portals with suitably extended marketing strategies some of which are being effectively applied in this study. These include but are not limited to active maintenance of Facebook page, weekly Newsletters, and others which prove to be stimulating the user’s interests as some of the presented statistics show.

Active maintenance of proton motive force mediates starvation-induced bacterial antibiotic tolerance in Escherichia coli

Recent evidence suggests that metabolic shutdown alone does not fully explain how bacteria exhibit phenotypic antibiotic tolerance. In an attempt to investigate the range of starvation-induced physiological responses underlying tolerance development, we found that active maintenance of the transmembrane proton motive force (PMF) is essential for prolonged expression of antibiotic tolerance in bacteria. Eradication of tolerant sub-population could be achieved by disruption of PMF using the ionophore CCCP, or through suppression of PMF maintenance mechanisms by simultaneous inhibition of the phage shock protein (Psp) response and electron transport chain (ETC) complex activities. We consider disruption of bacterial PMF a feasible strategy for treatment of chronic and recurrent bacterial infections.

Active Maintenance: A Proposal for the Long-Term Computational Reproducibility of Scientific Results

ABSTRACT Computational reproducibility, or the ability to reproduce analytic results of a scientific study on the basis of publicly available code and data, is a shared goal of many researchers, journals, and scientific communities. Researchers in many disciplines including political science have made strides toward realizing that goal. A new challenge, however, has arisen. Code too often becomes obsolete within only a few years. We document this problem with a random sample of studies posted to the Institution for Social and Policy Studies (ISPS) Data Archive; we encountered nontrivial errors in seven of 20 studies. In line with similar proposals for the long-term maintenance of data and commercial software, we propose that researchers dedicated to computational reproducibility should have a plan in place for “active maintenance” of their analysis code. We offer concrete suggestions for how data archives, journals, and research communities could encourage and reward the active maintenance of scientific code and data.

Active Working Memory and Simple Cognitive Operations

Working memory is thought to serve as a buffer for ongoing cognitive operations, even in tasks that have no obvious memory requirements. This conceptualization has been supported by dual-task experiments, in which interference is observed between a primary task involving short-term memory storage and a secondary task that presumably requires the same buffer as the primary task. Little or no interference is typically observed when the secondary task is very simple. Here, we test the hypothesis that even very simple tasks require the working memory buffer, but interference can be minimized by using activity-silent representations to store the information from the primary task. We tested this hypothesis using dual-task paradigm in which a simple discrimination task was interposed in the retention interval of a change detection task. We used contralateral delay activity (CDA) to track the active maintenance of information for the change detection task. We found that the CDA was abruptly disrupted following the interposed task. Despite this disruption of active maintenance, we found that performance in the change detection task was only slightly impaired, suggesting that activity-silent representations were used to retain the information for the change detection task. A second experiment replicated this result and also showed that automated discriminations could be performed without producing a large CDA disruption. Together, these results suggest that simple but non-automated discrimination tasks require the same processes that underlie active maintenance of information in working memory.

Active Maintenance: A Proposal for the Long-term Computational Reproducibility of Scientific Results

Computational reproducibility, or the ability to reproduce analytic results of a scientific study on the basis of publicly available code and data, is a shared goal of many researchers, journals, and scientific communities. Researchers in many disciplines including political science have made strides towards realizing that goal. A new challenge, however, has arisen. Code too often becomes obsolete within just a few years. We document this problem with a random sample of studies posted to the ISPS Data Archive; we encountered nontrivial errors in seven of 20 studies. In line with similar proposals for the long-term maintenance of data and commercial software, we propose that researchers dedicated to computational reproducibility should have a plan in place for "active maintenance" of their analysis code. We offer concrete suggestions forhow data archives, journals, and research communities could encourage and reward the active maintenance of scientific code and data.

Active maintenance of eligibility trace in rodent prefrontal cortex

Even though persistent neural activity has been proposed as a mechanism for maintaining eligibility trace, direct empirical evidence for active maintenance of eligibility trace has been lacking. We recorded neuronal activity in the medial prefrontal cortex (mPFC) in rats performing a dynamic foraging task in which a choice must be remembered until its outcome on the timescale of seconds for correct credit assignment. We found that mPFC neurons maintain significant choice signals during the time period between action selection and choice outcome. We also found that neural signals for choice, outcome, and action value converge in the mPFC when choice outcome was revealed. Our results indicate that the mPFC maintains choice signals necessary for temporal credit assignment in the form of persistent neural activity in our task. They also suggest that the mPFC might update action value by combining actively maintained eligibility trace with action value and outcome signals.

Health Monitoring of IGBTs with a Rule-Based Sub-safety Recognition Model Using Neural Networks

IGBTs are used everywhere ranging from aerospace, to transportation systems to the grid but it’s the most fragile device in power electronics. So it’s very critical to evaluate the health state and take advanced and active maintenance measures to avoid the accidents. This paper develops a rule-based sub-safety recognition model using neural networks to evaluate the degradation degree of the IGBTs and determine the health state. The model was validated with two groups of experimental data.