A research team has developed a dual-mode reconfigurable phototransistor based on a PtTe₂/WS₂ van der Waals heterostructure. By modulating the source-drain bias, the device's operating mechanism can be switched between photoconductive (PC) and photovoltaic (PV) modes. The PC mode delivers high photoconductive gain and a responsivity of 1.37 A/W, while the PV mode offers outstanding specific detectivity (9.42×10¹⁴ Jones) and fast response times (rise/fall of ~26 µs/23 µs). Leveraging the combination of light illumination states and bias polarity, the device produces four stable and distinguishable current levels, enabling multiple optical logic gates (XNOR, NOR, XOR) and robust four-state image encryption with a low average pixel correlation coefficient of 0.03. This work provides a versatile, low-power platform for advanced reconfigurable optoelectronics and secure optical communication.

A research team from NVIDIA and Thinking Machines Lab has explored whether the internal responses of artificial-intelligence models can serve as order parameters for physical systems. The study uses a three-dimensional convolutional neural network to analyze crystalline structures under controlled vibrational disorder. By treating the model output as a continuous measure of structural order, the work shows that body-centered cubic crystals lose their specific lattice identity before they lose generic solid-like order. This result suggests a new way to extend classical order-parameter ideas using machine-learned structural signals.

Materials phases are usually described by order parameters. For example, density is used to distinguish liquid and gas phases, while magnetization is used to distinguish ferromagnetic and paramagnetic phases. These order parameters are experimentally meaningful variables: they summarize collective order in a material rather than the behavior of a single atom or molecule.

MANCHESTER, United Kingdom, September 11, 2026 — NEW HORIZON PRESS LIMITED took part in the ALPSP Annual Conference and Awards 2026, held from 9 to 11 September at The Manchester Deansgate Hotel, joining publishers, learned societies, librarians, funders and technology partners from across the scholarly communications community.

Lodging, the bending and breaking of plant stems, is a primary threat to global rapeseed production, causing significant yield losses and complicating mechanical harvesting. 

A research team has developed a neural network-based framework that significantly improves the prediction of satellite clock bias (SCB) for low earth orbit (LEO) satellites, addressing a major challenge for next-generation navigation systems.