When robots perform complex tasks, the pose accuracy of the end-effector is critical. However, errors from individual joints tend to accumulate along the kinematic chain, making it challenging to guarantee high pose precision at the end-effector. To address this issue, this study proposes a virtual-constraints-based end-effector pose compensator (VEPC). The method treats the actual angles of specific joints as known inputs and automatically adjusts the remaining joint angles in real time, effectively eliminating the pose errors of the end-effector caused by the joints. Experimental results demonstrate that the method can reduce the maximum end-effector position error by over 75%. Moreover, the method requires no additional sensors, offering low cost and high compatibility.

Perovskite-based photovoltaic devices have garnered significant interest owing to their remarkable performance in converting light into electricity. Recently, the focus in the field of perovskite solar cells (PSCs) has shifted towards enhancing their durability over extended periods. One promising strategy is the incorporation of two-dimensional (2D) perovskites, known for their ability to enhance stability due to the large organic cations that act as a barrier against moisture. However, the broad optical bandgap and limited charge transport properties of 2D perovskites hinder their efficiency, making them less suitable as the sole light-absorbing material when compared to their three-dimensional (3D) counterparts. An innovative approach involves using 2D perovskite structures to modify the surface properties of 3D perovskite. This hybrid approach, known as 2D/3D perovskites, while enhancing their performance. Beyond solar energy applications, 2D perovskites offer a flexible platform for chemical engineering, allowing for significant adjustments to crystal and thin-film configurations, bandgaps, and charge transport properties through the different organic ligands and halide mixtures. Despite these advantages, challenges remain in integration of 2D perovskites into solar cells without compromising device stability. This review encapsulates the latest developments in 2D perovskite research, focusing on their structural, optoelectronic, and stability attributes, while delving into the challenges and future potential of these materials.

Hydrogen, with its carbon-free composition and the availability of abundant renewable energy sources for its production, holds significant promise as a fuel for internal combustion engines (ICEs). Its wide flammability limits and high flame speeds enable ultra-lean combustion, which is a promising strategy for reducing NOx emissions and improving thermal efficiency. However, lean hydrogen-air flames, characterized by low Lewis numbers, experience thermo-diffusive instabilities that can significantly influence flame propagation and emissions. To address this challenge, it is crucial to gain a deep understanding of the fundamental flame dynamics of hydrogen-fueled engines. This study uses high-speed planar SO2-LIF to investigate the evolutions of the early flame kernels in hydrogen and methane flames, and analyze the intricate interplay between flame characteristics, such as flame curvature, the gradients of SO2-LIF intensity, tortuosity of flame boundary, the equivalent flame speed, and the turbulent flow field. Differential diffusion effects are particularly pronounced in H2 flames, resulting in more significant flame wrinkling. In contrast, CH4 flames, while exhibiting smoother flame boundaries, are more sensitive to turbulence, resulting in increased wrinkling, especially under stronger turbulence conditions. The higher correlation between curvature and gradient of H2 flames indicates enhanced reactivity at the flame troughs, leading to faster flame propagation. However, increased turbulence can mitigate these effects. Hydrogen flames consistently exhibit higher equivalent flame speeds due to their higher thermo-diffusivity, and both hydrogen and methane flames accelerate under high turbulence conditions. These findings provide valuable insights into the distinct flame behaviors of hydrogen and methane, highlighting the importance of understanding the interactions between thermo-diffusive effects and turbulence in hydrogen-fueled engine combustion.

The advent of the evolutionary scale modeling (ESM) series of protein language models (PLMs) is a significant innovation in the convergence of large language models (LLMs) with protein representation. These models, trained on large amounts of unlabeled protein sequence data, learn the intricate patterns of mutation and conservation that have sculpted protein families through evolutionary history. ESM has become a widely used foundation model family for protein representation and downstream biological tasks.

Structured light is a new frontier in optics because it can be programmed across multiple degrees of freedom—amplitude, phase, spatial patterns, frequency, and polarization. Yet in practice, generating and controlling such light still often relies on bulky, alignment-sensitive optical setups. Researchers led by Prof. Hongtao Lin at Zhejiang University (ZJU), China, have introduced a unified inverse-design method to bring vectorial structured light onto a chip, using topological valley photonic crystals. Their tiny couplers show ultra-low loss and broad bandwidth at telecom wavelengths, offering a practical route to compact photonic chips for communications, sensing, and quantum technologies.

POSTECH Professor Seung-Ki Min’s Research Team Compares Future Extreme Fire Weather Under ‘Net-Zero’ vs. ‘Net-Negative’ Emission Scenarios.

A research team led by Professor Lizhi Xu from the Department of Mechanical Engineering under the Faculty of Engineering at the University of Hong Kong (HKU) has created a new type of ionogel that overcomes this challenge. The material is soft and flexible, yet strong enough to withstand significant mechanical stress, making it ideal for wearable and biomedical applications.

A team led by Professor Ed X. WU, Chair Professor of Biomedical Engineering & Lam Woo Professorship in Biomedical Engineering, and Dr. Alex T. L. LEONG, Research Assistant Professor from the Department of Electrical and Computer Engineering (ECE) under the Faculty of Engineering at the University of Hong Kong (HKU) has achieved a major breakthrough in understanding how the brain processes information through large-scale network changes. Their findings, published in Nature Communications, reveal that the brain can rewire its networks in just seconds after a brief neural signal—challenging the long-held belief that such changes are slow and gradual, based on imaging techniques like functional MRI (fMRI).