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Virtual contact improves intergroup relations between non-Muslim American and Muslim students from the Middle East, North Africa and Southeast Asia in a field quasi-experiment
Given the current polarized climate in many parts of the world, finding effective interventions to address psychological factors that drive conflict is critical. Direct, face-to-face contact has the demonstrated potential to stem the tide of intergroup antipathy. However, modern socio-political conflicts often span great physical distances, making direct contact difficult, costly and rare. Programs for “virtual contact” have emerged in recent years, combining text-based computer-mediated communication with live video to extend intergroup contact’s benefits to broader audiences. While compelling, studies of such programs are typically conducted in laboratory settings, focusing only on change in outgroup attitudes. The current research tests how a semester-long virtual contact intervention that brings together non-Muslim US American students and Muslim students from the Middle East, North Africa and Southeast Asia shapes varied intergroup processes, across two large-scale field quasi-experiments (combined N = 2886). Compared to before the intervention and a control group, participants who engaged in virtual contact showed greater self-outgroup overlap, improved outgroup attitudes, and less outgroup dehumanization and meta-dehumanization. This research provides evidence that virtual contact can be an effective tool for promoting better intergroup relations.
In-vitro accuracy of the virtual patient model with maxillomandibular relationship at centric occlusion using 3D-printed customized transfer key
This study aimed to create a 3D-printed customized transfer key and evaluate the accuracy of the virtual patient model with maxillomandibular relationship at centric occlusion using the transfer key.
Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning
Designing molecules with desirable properties is a critical endeavor in drug discovery. Because of recent advances in deep learning, molecular generative models have been developed. However, the existing compound exploration models often disregard the important issue of ensuring the feasibility of organic synthesis. To address this issue, we propose TRACER, which is a framework that integrates the optimization of molecular property optimization with synthetic pathway generation. The model can predict the product derived from a given reactant via a conditional transformer under the constraints of a reaction type. The molecular optimization results of an activity prediction model targeting DRD2, AKT1, and CXCR4 revealed that TRACER effectively generated compounds with high scores. The transformer model, which recognizes the entire structures, captures the complexity of the organic synthesis and enables its navigation in a vast chemical space while considering real-world reactivity constraints.
Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics
Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG’s performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na+). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics.
Curiosity shapes spatial exploration and cognitive map formation in humans
Cognitive maps are thought to arise, at least in part, from our intrinsic curiosity to explore unknown places. However, it remains untested how curiosity shapes aspects of spatial exploration in humans. Combining a virtual reality task with indices of exploration complexity, we found that pre-exploration curiosity states predicted how much individuals spatially explored environments, whereas markers of visual exploration determined post-exploration feelings of interest. Moreover, individual differences in curiosity traits, particularly Stress Tolerance, modulated the relationship between curiosity and spatial exploration, suggesting the capacity to cope with uncertainty enhances the curiosity-exploration link. Furthermore, both curiosity and spatial exploration predicted how precisely participants could recall spatial-relational details of the environment, as measured by a sketch map task. These results provide new evidence for a link between curiosity and exploratory behaviour, and how curiosity might shape cognitive map formation.
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