Large Output Research Articles

Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks

The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI.

Semantically Guided Efficient Attention Transformer for Face Super-Resolution Tasks

Face super-resolution generates high-resolution face images from low-resolution inputs, supporting face recognition in challenging environments. While deep learning methods, like Stable Diffusion and origin Transformer-based framework, have advanced super-resolution, they require heavy computation, making them difficult for tasks like face recognition. However, recognition accuracy only needs images of size 112x112, reducing the necessity for extremely large outputs. Existing methods often rely on convolutional attention, limiting receptive fields and performance. To address this, we propose SETFSR, a Semantically Guided Efficient Attention Transformer Face Super-Resolution. Our model leverages efficient self-attention for global feature extraction and incorporates face parsing constraints for structural accuracy. Experiments on Helen, CelebA, and FFHQ datasets show that SETFSR outperforms state-of-the-art models in PSNR, SSIM, and identity preservation.

Energetic Characteristics of Hydrophobic Porous Materials as Candidates for Manufacturing of Nanorockets.

A new propulsion mechanism for nano- and microrocket engines is hypothesized. It is based on the instantaneous expulsion from hydrophobic nanopores triggered by irradiation from electromagnetic microwaves, ultrasound, or sudden pressure release. A large energy output is needed for the propulsion of a nanoparticle, and the value can be determined experimentally and by means of atomistic simulations. As such, we measured the heat of intrusion of water into ITQ-29 (LTA) pure silica zeolite with cage structure of pores. The heat effect is exothermic and equal to -7.3 ± 0.8 J/g of zeolite. Similar values were reported for chabazite, ZIF-8, and grafted mesoporous silica EVA. All these materials have cage structures of pores. In contrast, silicalite-1 (MFI) zeolite with a channel structure of pores exhibits endothermic intrusion. Molecular dynamics simulations of pure silica zeolites with LTA, CHA, and MFI topologies at a broad range of water loadings show that water becomes thermodynamically stable in cage-shaped pores while it is unstable in channel-shaped pores. A large energy release is expected during water expulsion from channel-type pores.

Twin echo-enabled harmonic generation for enhanced coherent bunching at short wavelengths

Harmonic up-conversion schemes represent the most successful approach in externally seeded free-electron lasers (FELs) for obtaining radiation with laserlike properties at short wavelengths. They are, however, limited in the shortest achievable wavelengths, as the harmonic conversion efficiency decreases with increasing harmonic number, even for echo-enabled harmonic generation (EEHG). In the following, we introduce a novel externally seeded cascaded FEL scheme based on two EEHG stages, capable of obtaining unprecedented bunching fraction that is independent on the final FEL wavelength. The second stage can be operated both in conventional harmonic up-conversion but also in down-conversion, i.e., going toward (slightly) longer wavelengths. Higher bunching enables emission of laserlike radiation in the soft x-rays with large output power in a compact scheme. Its performance at 4 nm and shorter is investigated, demonstrating the ability to achieve multi-GW level, fully coherent pulses with laserlike properties and paving the way for externally seeded radiation at 1 nm and beyond. Published by the American Physical Society 2024

SU(2)-Equivariant Quantum Channels: Semiclassical Analysis

We study completely positive and trace-preserving equivariant maps between operators on irreducible representations of SU(2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\ extrm{SU}\\,}}(2)$$\\end{document}. We find asymptotic approximations of channels in the limit of large output representation and we compute traces of functions of channel outputs. Our main tool is quantization using coherent states. We provide quantitative error bounds for various semiclassical formulas satisfied by quantizations of functions on the sphere.

A Neural Phillips Curve and a Deep Output Gap

Many problems plague empirical Phillips curves (PCs). Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include proxying for the absentees or extracting them via assumptions-heavy filtering procedures. I propose an alternative route: a Hemisphere Neural Network (HNN) whose architecture yields a final layer where components can be interpreted as latent states within a Neural PC. First, HNN conducts the supervised estimation of nonlinearities that arise when translating a high-dimensional set of observed regressors into latent states. Second, forecasts are economically interpretable. Among other findings, the contribution of real activity to inflation appears understated in traditional PCs. In contrast, HNN captures the 2021 upswing in inflation and attributes it to a large positive output gap starting from late 2020. The unique path of HNN’s gap comes from dispensing with unemployment and GDP in favor of an amalgam of nonlinearly processed alternative tightness indicators.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Towards automated phenotype definition extraction using large language models

Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, and hybrid approaches. Currently, the development of accurate phenotype definitions demands extensive literature reviews and clinical experts, rendering the process time-consuming and inherently unscalable. Large language models offer a promising avenue for automating phenotype definition extraction but come with significant drawbacks, including reliability issues, the tendency to generate non-factual data (“hallucinations”), misleading results, and potential harm. To address these challenges, our study embarked on two key objectives: (1) defining a standard evaluation set to ensure large language models outputs are both useful and reliable and (2) evaluating various prompting approaches to extract phenotype definitions from large language models, assessing them with our established evaluation task. Our findings reveal promising results that still require human evaluation and validation for this task. However, enhanced phenotype extraction is possible, reducing the amount of time spent in literature review and evaluation.

Compliant Mechanism Synthesis Using Nonlinear Elastic Topology Optimization With Variable Boundary Conditions

ABSTRACTIn topology optimization of compliant mechanisms, the specific placement of boundary conditions strongly affects the resulting material distribution and performance of the design. At the same time, the most effective locations of the loads and supports are often difficult to find manually. This substantially limits topology optimization's effectiveness for many mechanism design problems. We remove this limitation by developing a method which automatically determines optimal positioning of a prescribed input displacement and a set of supports simultaneously with an optimal material layout. Using nonlinear elastic physics, we synthesize a variety of compliant mechanisms with large output displacements, snap‐through responses, and prescribed output paths, producing designs with significantly improved performance in every case tested. Compared to optimal designs generated using manually designed boundary conditions used in previous studies, the mechanisms presented in this paper see performance increases ranging from 47% to 380%. The results show that nonlinear mechanism responses may be particularly sensitive to boundary condition locations and that effective placements can be difficult to find without an automated method.

Scalable watermarking for identifying large language model outputs

Large language models (LLMs) have enabled the generation of high-quality synthetic text, often indistinguishable from human-written content, at a scale that can markedly affect the nature of the information ecosystem1–3. Watermarking can help identify synthetic text and limit accidental or deliberate misuse4, but has not been adopted in production systems owing to stringent quality, detectability and computational efficiency requirements. Here we describe SynthID-Text, a production-ready text watermarking scheme that preserves text quality and enables high detection accuracy, with minimal latency overhead. SynthID-Text does not affect LLM training and modifies only the sampling procedure; watermark detection is computationally efficient, without using the underlying LLM. To enable watermarking at scale, we develop an algorithm integrating watermarking with speculative sampling, an efficiency technique frequently used in production systems5. Evaluations across multiple LLMs empirically show that SynthID-Text provides improved detectability over comparable methods, and standard benchmarks and human side-by-side ratings indicate no change in LLM capabilities. To demonstrate the feasibility of watermarking in large-scale-production systems, we conducted a live experiment that assessed feedback from nearly 20 million Gemini6 responses, again confirming the preservation of text quality. We hope that the availability of SynthID-Text7 will facilitate further development of watermarking and responsible use of LLM systems.

Regulation of Wide Bandgap Perovskite by Rubidium Thiocyanate for Efficient Silicon/Perovskite Tandem Solar Cells.

Developing high-quality wide bandgap (WBG) perovskites with ≈1.7eV bandgap (Eg) is critical to couple with silicon and create efficient silicon/perovskite tandem devices. The sufferings of large open-circuit voltage (VOC) loss and unstable power output under operation continuously highlight the criticality to fully develop high-quality WBG perovskite films. In this study, rubidium and thiocyanate as additive regulators in WBG perovskites are incorporated, significantly reducing non-radiative recombination, ion-migration, and phase segregation. The optimized 1.66 eV Eg perovskite solar cells achieved state-of-art 1.3V VOC (0.36V deficit), and delivered a stabilized power conversion efficiency of 24.3%, along with good device stability (20% degradation (T80) after over 994h of operation under 1 sun at ≈65°C). When integrated with a flat front side silicon cell, silicon/perovskite two-terminal tandem device (30% efficient) is obtained with a 1.97V VOC, and T90 operational lifetime of more than 600h at room temperature.

Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR

The pulse laser emission circuit plays a crucial role as the emission unit of time-of-flight (TOF) LiDAR. This paper proposes a nanosecond-level pulse laser diode array drive circuit for LiDAR, primarily aimed at addressing the issue of high-speed scanning drive for the laser diode array at the emission end of solid-state LiDAR. Based on the single pulse laser diode drive circuit, this paper innovatively designs a circuit that includes modules such as a boost circuit, linear power supply, high-speed gate driver, GaN field-effect transistor, and pulse narrowing circuit, realizing an 8-channel laser diode array drive circuit. This circuit can achieve a pulse laser array drive with a single channel operating frequency of greater than 100 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time that does not exceed 1 μs. A field programmable gate array (FPGA) is used to control the operation of this circuit and perform a series of performance tests. Experimental results show that this circuit has a high repetition rate, large output power, a narrow pulse width, and fast switching speeds, making it highly suitable for use in the optical emission module of solid-state LiDAR.

Wireless programmable patterns of electro-hydraulic haptic electronic skins able to create surface morphology

Haptic feedback technology represents a frontier with revolutionary potential, particularly in virtual and augmented reality. Skin, as an underexplored sensory interface, holds tremendous potential to significantly enrich our perceptual experiences while directly impacting the fields of communication, entertainment, and medicine. This article presents a programmable, ultra-lightweight, and ultra-thin wireless wearable haptic electronic skin that achieves rapid response and large mechanical feedback force and deformation displacement output. The electronic skin comprises a series of haptic electro-hydraulic actuators, each with the weight of 0.023 g and the thickness of 199 μm. Each actuator can be independently regulated and produce a maximum normal force of 150 mN and a normal displacement of 0.58 mm, simultaneously achieving the haptic presentation of textures. Relying on a portable multi-channel control circuit system designed, each electro-hydraulic actuator of the haptic electronic skin can be precisely control to achieve various haptic patterns. Showcasing its potentials, the haptic technology aids visual-impaired individuals in reading books, provides ground navigation instructions, and enhances gaming experiences, contributing to a more immersive and authentic artificial intelligence environment.

Energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization

Aiming at the problems of large number of data transmission node deaths and large transmission energy consumption output in energy-saving clustering routing communication of wireless sensor networks, an energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization is proposed. Firstly, a network communication energy consumption model is constructed, and the network node distribution is optimized in time with the goal of reducing energy consumption combined with differential bee colony algorithm; then, based on the network node distribution optimization results, an energy-saving clustering method for heterogeneous wireless sensor networks is formulated, and the cluster head is determined by using the energy iteration cluster selection method, and the cluster head radius is obtained to complete the energy-saving clustering of communication nodes in heterogeneous wireless sensor networks; finally, the distance between the communication cluster head node and the base station is set, the routing level of the node communication is determined, and the energy-saving routing communication of the heterogeneous wireless sensor network is realized by combining the multi-hop routing communication mode. The experimental results show that when the method is used for network energy-saving clustering routing communication, the number of data transmission nodes that die is at most 22, and the maximum energy consumption of node transmission is 21 nJ/bit , indicating that the node communication energy-saving effect of this method is good.

Perspectives on NO X Emissions and Impacts from Ammonia Combustion Processes.

Climate change and global warming necessitate the shift toward low-emission, carbon-free fuels. Although hydrogen boasts zero carbon content and high performance, its utilization is impeded by the complexities and costs involved in liquefaction, preservation, and transportation. Ammonia has emerged as a viable alternative that offers potential as a renewable energy storage medium and supports the global economy's decarbonization. With its broader applicability in large power output applications, decentralized energy sources, and industrial locations off the grid, ammonia is increasingly regarded as an essential fuel for the future. Although ammonia provides a sustainable solution for future low-carbon energy fields, its wide-scale adoption is limited by NO X emissions and poor combustion performance under certain conditions. As research on ammonia combustion expands, recent findings reveal factors impacting the chemical reaction pathways of ammonia-based fuels, including the equivalence ratio, fuel mixture, pressure, and temperature. Investigations into ammonia combustion and NO X emissions, at both laboratory and industrial scales, have identified NO X production peaks at equivalence ratios of 0.8-0.9 for ammonia/hydrogen blends. The latest studies about the NO X emissions of the ammonia flame at different conditions and their generating pathways are reviewed in this work. Effective reduction in NO production from ammonia-based flames can be achieved with richer blends, which generate more NH i radicals. Other advanced NO X mitigation techniques such as plasma-assisted combustion have been also explored. Further research is required to address these challenges, reduce emissions, and improve efficiencies of ammonia-based fuel blends. Finally, the extinction limit of ammonia turbulent flame, its influential factors, and different strategies to promote the ammonia flame stability were discussed. The present review contributes to disseminating the latest advancements in the field of ammonia combustion and highlights the importance of refining reaction mechanisms, computational models, and understanding fundamental phenomena for practical implications.

Growth, first-principles calculations and optical properties of a novel near-infrared Ho: NLGW laser crystal

A rigid structure was constructed using an isotopic doping strategy, and a novel near-infrared 1 at% Ho3+: NaLa0.93Gd0.06(WO4)2 (Ho: NLGW) laser crystal with a size of Φ 18 mm × 30 mm was successfully grown by the Czochralski method. X-ray rocking curves and Rietveld refinement indicate that the grown crystal possesses high crystalline quality and crystallizes in the I41/a space group with lattice parameters: a = b = 5.3518 Å, c = 11.6438 Å and V = 333.5051 Å3. The Ho: NLGW crystal exhibits a low coefficient of thermal expansion (8.472 × 10−6 K−1), and the c-axis thermal conductivity increases from 1.235 W/(m × K) to 1.404 W/(m × K). The thermal properties of the Ho: NLGW crystal surpass those of other Ho3+-doped tungstate series and mainstream near-infrared laser crystals, suggesting that it is easier to obtain excellent laser gain. For revealing the excellent thermal properties, the elastic moduli of the NLW and NLGW crystals have been calculated by first principles. With the introduction of Gd3+, the structural stiffness of the NLW as a whole has been increased to optimize the mechanical properties in each axial direction and to obtain the reason for the larger Debye temperature. Finally, the transmission, absorption and near-infrared emission spectra of the Ho: NLGW crystal were investigated. The crystal exhibits a high transmittance of 88.5 % and an emission cross-section of 0.84 × 10−20 cm2 calculated according to the J-O theory. Experimental results indicate that the grown Ho: NLGW crystal can achieve large laser output more easily and are expected to be an excellent medium for 2 μm solid-state lasers.

Application of model predictive control scheme for piezoelectric ceramic micro-displacement

Abstract Currently, in the domain of micro-control technology, many operations need to meet the standard of nanometer-level positioning. As a high-precision micro-displacement element, piezoelectric ceramics are widely used in the field of micro-control due to their characteristics such as no need for transmission devices, high resolution, large output force, small size, and fast response speed. However, the inherent hysteresis nonlinearity of piezoelectric ceramics can lead to decreased displacement accuracy, oscillation, and even system instability. Therefore, eliminating the influence of nonlinear characteristics and improving displacement accuracy possess significant practical importance. In this paper, a model predictive control scheme is designed, which achieves faster response speed and higher accuracy compared to traditional control methods such as Proportional Integral Derivative control. Relevant simulations and tests have been conducted. In this scheme, hysteresis nonlinearity is first modeled and then compensated through feedforward, thereby simplifying the displacement control of piezoelectric ceramics into a more manageable linear problem. Subsequently, a linear model predictive control method is adopted to control the entire system. Both simulation and experimental results have verified the effectiveness of this control method.

High performance (K,Na)NbO3-based textured ceramics for piezoelectric energy harvesting

Piezoelectric energy harvesters (PEHs), which can provide sustainable green energy for low-power electronics have received extensive attention in the viewpoint of science and industrialization. In order to achieve large output power of PEHs, high-performance piezoelectric materials are extremely desired. Herein, excellent electromechanical conversion properties of d33 ∼ 447 pC/N, g33 ∼ 30 × 10−3 Vm/N, k33 ∼ 47 % and Suni ∼0.18 % were achieved in <001> grain aligned KNN-based lead-free ceramics with a favorable rhombohedral (R), orthorhombic (O) and tetragonal (T) multiphase structure. The superior electromechanical conversion properties can be attributed to grain orientation and crystal symmetry controlled anisotropy, and multiphase coexistence significantly decreased poling energy barrier, as well asthe flexible response of the nanometer domains to external stimuli. The PEHs fabricated using the optimal performance KNN-based textured ceramics exhibit high output power of 0.46 mW and power density of 6.7 mW/cm3 at a resonant frequency of 41 Hz, which is associated with the large k33 (∼47 %) and figure-of-merit (d33×g33 ∼ 13.4 × 10−12 m2/N) values. The output power can actually light up more than 145 commercial LEDs, demonstrating great potential of KNN-based PEHs to power wireless sensors.

Phase Engineering-Mediated D-Band Center of Ru Sites Promote the Hydrogen Evolution Reaction Under Universal pH Condition.

The rational design of pH-universal electrocatalyst with high-efficiency, low-cost and large current output suitable for industrial hydrogen evolution reaction (HER) is crucial for hydrogen production via water splitting. Herein, phase engineering of ruthenium (Ru) electrocatalyst comprised of metastable unconventional face-centered cubic (fcc) and conventional hexagonal close-packed (hcp) crystalline phase supported on nitrogen-doped carbon matrix (fcc/hcp-Ru/NC) is successfully synthesized through a facile pyrolysis approach. Fascinatingly, the fcc/hcp-Ru/NC displayed excellent electrocatalytic HER performance under a universal pH range. To deliver a current density of 10mAcm-2, the fcc/hcp-Ru/NC required overpotentials of 16.8, 23.8 and 22.3mV in 1M KOH, 0.5M H2SO4 and 1M phosphate buffered solution (PBS), respectively. Even to drive an industrial-level current density of 500 and 1000mAcm-2, the corresponding overpotentials are 189.8 and 284mV in alkaline, 202 and 287mV in acidic media, respectively. Experimental and theoretical calculation result unveiled that the charge migration from fcc-Ru to hcp-Ru induced by work function discrepancy within fcc/hcp-Ru/NC regulate the d-band center of Ru sites, which facilitated the water adsorption and dissociation, thus boosting the electrocatalytic HER performance. The present work paves the way for construction of novel and efficient electrocatalysts for energy conversion and storage.

A wafer-level 3D integrated T/R microsystem with 0.5-wavelength self-matching connectors

Achieving high performance, multifunctionality, and a compact size simultaneously in a single T/R module presents a significant challenge. In this work, both the 'chiplet' technique and 3D integration technique are adopted to form a T/R microsystem using self-matching interconnectors. Such T/R microsystem consists of four GaAs chips and one CMOS chip. The designed microsystem with a small size is utilized for the application of tile-type mmWave active phased array. Its measured results demonstrate good performance in terms of high gain, large output power, 6-bit phase shift, and 6-bit attenuation, confirming the effectiveness of our adopted architecture and design method. As such, this work provides a valuable design guideline for the chiplet-based 3D integrated RF microsystem.