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Physical reservoir computing leverages the intrinsic history-dependence and nonlinearity of hardware to encode spatiotemporal signals directly at the sensor level, enabling low-latency processing of dynamic inputs. Encoding delity depends on the separability of multi-state outputs, yet in practice it is often hampered by empirically chosen, suboptimal operating conditions. Here, we apply Bayesian optimization to improve the encoding performance of solution-processed Al₂O₃/In₂O₃ thin- lm transistors. By exploring a ve-dimensional pulse-parameter input space and using the normalized degree of separation for output state distinguishability, we demonstrate high- delity 6-bit temporal encoding corresponding to 64 output states. We further show that a model based on simpler 4-bit data can effectively guide optimization for more complex 6-bit tasks, substantially reducing experimental effort. Using a six-frame moving-car image sequence as a benchmark, we nd that the optimized 6-bit pulse conditions signi cantly enhance encoding accuracy, with 4-bit derived parameters performing comparably in terms of pixel errors. Shapley Additive Explanations (SHAP) analysis further reveals that gate-pulse amplitude and drain voltage are the dominant contributors to output state separation. This work establishes a data-driven strategy for identifying optimal operating conditions in reservoir devices and outlines a framework that can be transferred to diverse material platforms and physical reservoir implementations. 

This study tested students’ socio-cognitive outcomes in using the Open Virtual Experiment Simulator Education Tool (OVESET), a series of virtual experiment simulators designed for undergraduate polymer science education. The educational tool, covering core polymer science concepts (e.g., molecular weight distribution and polymerization kinetics), was implemented across two consecutive years in an upper-level undergraduate macromolecules course. Guided by Self-Determination Theory (SDT), this pretest–post-test study measured changes in students’ self-regulation, self-efficacy, sense of belonging, and intention to pursue a career in polymer science after using the virtual modules. In the first year, two modules were used across 3 weeks with 16 participating students; in the second year, seven modules were used over 12 weeks with 20 students. Results showed that OVESET modules significantly enhanced students’ self-efficacy in polymer science, with medium effect sizes, while changes in self-regulation, belonging, and intention to pursue a career in polymer science were not significant. This study highlights the implementation and evaluation of virtual laboratory tools in polymer science education and underscores the importance of considering student perceptions and engagement. 

Objectivity is a fundamental requirement for vortex identification, ensuring that vortex structures observed remain invariant under changes in the reference frame. However, although most conventional vortex identification methods, including Liutex, are Galilean invariant, they are not objective. Since the accelerated motion of the observer does not affect the velocity gradient tensor at an instant of time, the rotational motion is only considered for the non-inertial frame. This paper proposes a method to recover the angular velocity of a rotating observer directly from flow field data measured in the rotating frame. The approach exploits the observation that, in an inertial frame, zero-vorticity points tend to dominate the region with an almost identical nonzero vorticity in the observer’s non-inertial coordinate system. By identifying the most frequently occurring vorticity within the domain, the observer’s angular velocity can be uniquely determined, enabling reconstruction of the objective velocity gradient tensor and, consequently, the objective Liutex. The key issue is to find a reference point (RP). The RP should have zero vorticity in the inertial coordinate system, and then the RP has the same angular speed as the observer. The RP can be found by comparing the vorticity of all points in the computational domain and the RP will correspond to the vorticity vector with the highest percentage in the non-inertial coordinate system. The proposed method is validated using DNS data of the boundary layer transition over a flat plate with an artificially imposed angular velocity. The recovered angular velocity agrees closely with the true value within an acceptable margin of error. Furthermore, the objective Liutex reconstructed from the rotating frame data is visually indistinguishable from the original inertial frame Liutex. These results demonstrate that the method provides a simple and accurate way to restore objectivity for Liutex and other vortex identification techniques. The objective Liutex will be equal to the original Liutex in an inertial coordinate system when the observer does not have rotational motion.