Epistemic agency for costructuring expansive knowledge-building practices

As a hallmark of authentic science practices, students need to enact epistemic agency to shape/reshape the key aspects of their inquiry work as a collaborative community. This study elaborates an emergent temporal mechanism for engaging students\u27 epistemic agency: “reflective structuration” by which members of a classroom community coconstruct ever-evolving inquiry directions and group structures as their collective inquiry work proceeds. Using an interactional ethnography method, we examined how students (n = 22) in a Grade 5 classroom coconstructed shared inquiry directions and flexible group structures to guide their sustained inquiry about human body systems over 7 months supported by a collaborative online environment. Rich data were collected to trace the work of the eye inquiry group as a telling case. With their teacher\u27s support, students took agentic moves to construct an evolving set of wondering areas as a way to frame what their whole class needed to investigate. Flexible groups, such as the eye inquiry group, emerged and evolved in the various areas, leading to progressively deepening inquiry and extensive idea exchanges among students. Implications for research and practice are discussed

IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability

Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process. Currently, SAT solvers are employed for these problems and neural network is tried as assistance to solvers. However, as SAT problems in the LEC context are distinctive due to their predominantly unsatisfiability nature and a substantial proportion of UNSAT-core variables, existing neural network assistance has proven unsuccessful in this specialized domain. To tackle this challenge, we propose IB-Net, an innovative framework utilizing graph neural networks and novel graph encoding techniques to model unsatisfiable problems and interact with state-of-the-art solvers. Extensive evaluations across solvers and datasets demonstrate IB-Net's acceleration, achieving an average runtime speedup of 5.0% on industrial data and 8.3% on SAT competition data empirically. This breakthrough advances efficient solving in LEC workflows.Comment: 7 pages, 12 figure

The Efficiency Prediction of the Laser Charging Based on GA-BP

In IoT applications, energy supply, especially wireless power transfer (WPT), has attracted the most attention in the relevant literature. In this paper, we present a new approach to laser irradiance solar cell panels and predicting energy transfer efficiency. From the previous experimental datasets, it has been discovered that in the laser charging (LC) process, temperature has a great impact on the efficiency, which is highly correlated with the laser intensity. Then, based on artificial neural network (ANN), we set the above temperature and laser intensity as inputs, and the efficiency as output through back propagation (BP) algorithm, and use neural network and BP to train and modify the network parameters to approach the real efficiency value. We also propose the genetic algorithm (GA) to optimize the learning rate of the BP, which achieved slightly superior results. The results of the experiment indicate that the prediction method reaches a high accuracy of about 99.4%. The research results in this paper provide an improved solution for the LC application, particularly the energy supply of IoT devices or small electronic devices through WPT

The Efficiency Prediction of the Laser Charging Based on GA-BP

In IoT applications, energy supply, especially wireless power transfer (WPT), has attracted the most attention in the relevant literature. In this paper, we present a new approach to laser irradiance solar cell panels and predicting energy transfer efficiency. From the previous experimental datasets, it has been discovered that in the laser charging (LC) process, temperature has a great impact on the efficiency, which is highly correlated with the laser intensity. Then, based on artificial neural network (ANN), we set the above temperature and laser intensity as inputs, and the efficiency as output through back propagation (BP) algorithm, and use neural network and BP to train and modify the network parameters to approach the real efficiency value. We also propose the genetic algorithm (GA) to optimize the learning rate of the BP, which achieved slightly superior results. The results of the experiment indicate that the prediction method reaches a high accuracy of about 99.4%. The research results in this paper provide an improved solution for the LC application, particularly the energy supply of IoT devices or small electronic devices through WPT

In pursuit of feedback activation: New insights into redox-responsive hydropersulfide prodrug combating oxidative stress

Redox-responsive hydropersulfide prodrugs are designed to enable a more controllable and efficient hydropersulfide (RSSH) supply and to thoroughly explore their biological and therapeutic applications in oxidative damage. To obtain novel activation patterns triggered by redox signaling, we focused on NAD(P)H: quinone acceptor oxidoreductase 1 (NQO1), a canonical antioxidant enzyme, and designed NQO1-activated RSSH prodrugs. We also performed a head-to-head comparison of two mainstream structural scaffolds with solid quantitative analysis of prodrugs, RSSH, and metabolic by-products by LC-MS/MS, confirming that the perthiocarbamate scaffold was more effective in intracellular prodrug uptake and RSSH production. The prodrug was highly potent in oxidative stress management against cisplatin-induced nephrotoxicity. Strikingly, this prodrug possessed potential feedback activation properties by which the delivered RSSH can further escalate the prodrug activation via NQO1 upregulation. Our strategy pushed RSSH prodrugs one step further in the pursuit of efficient release in biological matrices and improved druggability against oxidative stress