Parametric Oscillator (PO)-based Ising machines (IMs) are promising candidates for electronic solvers. They are able to solve nondeterministic polynomial-time hard (NP-hard) combinatorial optimization problems that current von Neumann architectures struggle to address. PO-based IMs relying on coupled networks of parametric frequency divider (PFD) circuits were recently demonstrated, showing the capability to achieve power levels per spin in the range of hundreds of nanowatts. This has created an unprecedented opportunity to realize electronic Ising machines capable of solving NP-hard problems with a million variables while requiring <1 W. However, a major limitation still remains: due to its nonlinear dynamics, a typical PFD circuit requires multiple inductors to enable low-power activation of the period-doubling mechanism that is the basis of its PO-like operation, making it difficult to achieve wafer-scale Ising machines suited to solve problems with millions of variables. To overcome this limitation and enable large-scale PFD-based IMs without passive inductors, we propose a novel inductorless PFD architecture. This architecture leverages gyrator-based circuits to achieve inductive elements out of active networks of resistors and capacitors, allowing to achieve a low-power threshold for the activation of the period-doubling mechanism without requiring inductors. The design approach creates a path to significantly reduce circuit area and improve integration capability. While being demonstrated using lumped electronic components on a printed circuit board, this architecture can be replicated using complementary metal–oxide–semiconductor electronics in the future, providing a promising path toward large-scale, fully integrated PFD-based Ising machines.
Argon is the primary pressure-transmitting medium in Hot Isostatic Pressing (HIP), and its thermophysical properties under high-temperature, high-pressure conditions strongly affect control accuracy and component quality. However, accurate and efficient property prediction remains difficult because conventional equations of state (EOS) are computationally expensive and may become numerically stiff, while experiments are costly and limited in range. This study develops a machine-learning framework to predict argon thermophysical properties, including density, enthalpy, heat capacities, and transport coefficients, over a wide thermodynamic domain. Six regression models (XGBoost, LightGBM, Random Forest, Extra Trees, Support Vector Regression, and Multi-Layer Perceptron) were trained, among which Extra Trees showed the best overall robustness for the evaluated tasks. The proposed framework avoids iterative EOS solving during inference and achieves R2 values above 0.998 for core energy-related properties. For thermal conductivity, comparison with independent Green–Kubo molecular dynamic simulation showed good agreement on the test set and reproduced the main temperature- and pressure-dependent trends. These results indicate that the framework is a robust and numerically stable tool for argon property prediction in HIP and supports process monitoring and digital-twin applications within the sampled domain.
This study introduces “green technostress,” a phenomenon in which environmentally conscious consumers experience psychological strain from navigating complex digital platforms for sustainable electronics purchases. Grounded in conservation of resources theory, this novel construct developed, validated, and tested a comprehensive model using partial least squares-structural equation modeling with 427 electronics consumers. The findings demonstrate that environmental consciousness, rather than straightforwardly promoting sustainable purchasing, instead heightens green technostress (β = 0.52, p < 0.001), which in turn erodes both consumer well-being (β = −0.44) and sustainable purchase behavior (β = −0.38); information overload and decision complexity together account for 47.5% of this effect. This reveals a counterintuitive mechanism: environmental consciousness itself, not merely cost or convenience, can suppress the very behavior it is meant to encourage (direct effect β = −0.19). This paper shows that sustainability communication is currently misdirected: consumers attend heavily to visible attributes such as packaging (M = 5.68) while largely overlooking invisible digital impacts such as server energy and data mining (M = 2.31), yet it is only attention to these invisible impacts that predicts sustainable behavior (β = 0.41). Digital literacy, retailer support, and environmental self-efficacy each partially buffer these effects, indicating that both individual competence and retailer information design matter. Collectively, these findings challenge the assumption that environmental consciousness reliably facilitates sustainable consumption and instead show how digital information environments can paradoxically work against the behaviors they are intended to support, carrying direct implications for how retailers design sustainability information, how policymakers regulate digital disclosure, and how sustainability theory conceptualizes the consumer decision process.
Accurate experimental data on impact ionization rate in silicon have recently become achievable because of UV-resistant induced junction photodiodes, which eliminate the charge-carrier recombination losses in the bulk, leaving only a small contribution from surface recombination centers to be corrected in spectral responsivity data. We measured the spectral responsivity of trap detectors built from such photodiodes using low-uncertainty cryogenic radiometer setups, thereby gaining highly accurate responsivity data over the wavelength range of 250–476 nm. Reflectance correction of a three-element trap detector was measured at laser wavelengths of 325 and 405 nm and then modeled with low uncertainty between 250 and 410 nm. A minor correction due to recombination loss of electrons and holes was modeled over the spectral range of 250–476 nm. When combined, these measurements and models enabled us to achieve unprecedentedly accurate data on impact ionization. The results thus obtained are in good agreement with the theoretical impact ionization rate by Kamakura et al. [J. Appl. Phys. 75, 3500 (1994)] over four orders of magnitude, characterized by a power-law exponent of 4.6 describing the dependence on the energy of incident photons. These results have significance in developing silicon photodiode detectors with predictable spectral responsivity for photons or other particles and also in electronics when modeling electron tunneling in metal-oxide-semiconductor transistors.
Following cuprates and iron-based superconductors, nickel-based high-temperature superconductors have emerged as a third major family with the potential to advance understanding of the mechanisms underlying high-temperature superconductivity. This study analyzes 1057 publications related to nickel-based superconductivity indexed in the Web of Science Core Collection from 1993 to 2025. The analytical framework integrates multilevel network analysis, thematic clustering, main-path analysis, burst detection, and strategic diagram analysis to investigate the complex network topology, fine-grained textual semantics, and temporal burst patterns embedded in the literature. Results show that two key breakthroughs in the field were the discovery of superconductivity in infinite-layer Nd0.8Sr0.2NiO2 in 2019 and in bilayer La3Ni2O7 under high pressure in 2023. China and the United States are the leading contributors, while France, Spain, and Germany play important bridging roles in international collaboration. Current research frontiers include infinite-layer and bilayer Ruddlesden–Popper nickelates, high-pressure superconductivity, and investigations of electronic structure and pairing mechanisms, with first-principles calculations serving as a major theoretical approach. The literature review further shows that materials research is shifting from the exploration of naturally occurring structures to the deliberate design of artificial architectures; experimental methods have expanded to include epitaxial growth, in situ control, extreme-condition experiments, and advanced characterization; mechanistic studies focus on multiorbital coupling; and device research extends toward interface, strain, and micro- and nanoscale control.
