Weak Enhancement Research Articles

Catalyst-On-Hotspot Nanoarchitecture: Plasmonic Focusing of Light onto Co-Photocatalyst for Efficient Light-To-Chemical Transformation.

Plasmon-mediated catalysis utilizing hybrid photocatalytic ensembles promises effective light-to-chemical transformation, but current approaches suffer from weak electromagnetic field enhancements from polycrystalline and isotropic plasmonic nanoparticles as well as poor utilization of precious co-catalyst. Here, efficient plasmon-mediated catalysis is achieved by introducing a unique catalyst-on-hotspot nanoarchitecture obtained through the strategic positioning of co-photocatalyst onto plasmonic hotspots to concentrate light energy directly at the point-of-reaction. Using environmental remediation as a proof-of-concept application, the catalyst-on-hotspot design (edge-AgOcta@Cu2O) enhances photocatalytic advanced oxidation processes to achieve superior organic-pollutant degradation at ≈81% albeit having lesser Cu2O co-photocatalyst than the fully deposited design (full-AgOcta@Cu2O). Mass-normalized rate constants of edge-AgOcta@Cu2O reveal up to 20-fold and 3-fold more efficient utilization of Cu2O and Ag nanoparticles, respectively, compared to full-AgOcta@Cu2O and standalone catalysts. Moreover, this design also exhibits catalytic performance >4-fold better than emerging hybrid photocatalytic platforms. Mechanistic studies unveil that the light-concentrating effect facilitated by the dense electromagnetic hotspots is crucial to promote the generation and utilization of energetic photocarriers for enhanced catalysis. By enabling the plasmonic focusing of light onto co-photocatalyst at the single-particle level, the unprecedented design offers valuable insights in enhancing light-driven chemical reactions and realizing efficient energy/catalyst utilizations for diverse chemical, environmental, and energy applications.

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Motor Bearing Fault Diagnosis in an Industrial Robot Under Complex Variable Speed Conditions

Abstract Motor bearing is the key vulnerable part of the servomotor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong nonstationary characteristics. Early nonstationary fault signals are often weak and submerged in background noise. The nonstationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the nonstationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which are representative of the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study

Rationale and ObjectivesThis study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared with conventional radiomics and deep learning models. Materials and methodsA retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). ResultsA total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P<0.05). Compared with the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. ConclusionMRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

On mechanisms for high-frequency pump-enhanced optical emissions at 557.7 and 630.0 nm from atomic oxygen in the high-latitude F-region ionosphere

Abstract. The EISCAT (European Incoherent Scatter Scientific Association) Heating facility was used to transmit powerful high-frequency (HF) electromagnetic waves into the F-region ionosphere to enhance optical emissions at 557.7 and 630.0 nm from atomic oxygen. The emissions were imaged by several stations of ALIS (Auroral Large Imaging System) in northern Sweden, and the EISCAT UHF incoherent scatter radar was used to obtain plasma parameter values. The ratio of the 557.7 to 630.0 nm column emission rates changed from I5577/I6300≈0.2 for the HF pump frequency f0=6.200 MHz ≈4.6fe to I5577/I6300≈0.5 when f0=5.423 MHz ≲4fe, where fe is the ionospheric electron gyro frequency. The observations are interpreted in terms of decreased electron heating efficiency and thereby weaker enhancement at 630.0 nm for f0=5.423 MHz ≲ 4 fe. The emissions at 557.7 nm are attributed to electron acceleration by upper hybrid waves of metre-scale wavelengths that can be excited with f0=5.423 MHz ≲ 4 fe.

Temperature-Enhanced Exciton Emission from GaAs Cone-Shell Quantum Dots.

The temperature-dependent intensities of the exciton (X) and biexciton (XX) peaks from single GaAs cone-shell quantum dots (QDs) are studied with micro photoluminescence (PL) at varied excitation power and QD size. The QDs are fabricated by filling self-assembled nanoholes, which are drilled in an AlGaAs barrier by local droplet etching (LDE) during molecular beam epitaxy (MBE). This method allows the fabrication of strain-free QDs with sizes precisely controlled by the amount of material deposited for hole filling. Starting from the base temperature T = 3.2 K of the cryostat, single-dot PL measurements demonstrate a strong enhancement of the exciton emission up to a factor of five with increasing T. Both the maximum exciton intensity and the temperature Tx,max of the maximum intensity depend on excitation power and dot size. At an elevated excitation power, Tx,max becomes larger than 30 K. This allows an operation using an inexpensive and compact Stirling cryocooler. Above Tx,max, the exciton intensity decreases strongly until it disappears. The experimental data are quantitatively reproduced by a model which considers the competing processes of exciton generation, annihilation, and recombination. Exciton generation in the QDs is achieved by the sum of direct excitation in the dot, plus additional bulk excitons diffusing from the barrier layers into the dot. The thermally driven bulk-exciton diffusion from the barriers causes the temperature enhancement of the exciton emission. Above Tx,max, the intensity decreases due to exciton annihilation processes. In comparison to the exciton, the biexciton intensity shows only very weak enhancement, which is attributed to more efficient annihilation processes.

A Novel Predictor for the Analysis and Prediction of Enhancers and Their Strength via Multi-View Features and Deep Forest

Enhancers are short DNA segments (50–1500 bp) that effectively activate gene transcription when transcription factors (TFs) are present. There is a correlation between the genetic differences in enhancers and numerous human disorders including cancer and inflammatory bowel disease. In computational biology, the accurate categorization of enhancers can yield important information for drug discovery and development. High-throughput experimental approaches are thought to be vital tools for researching enhancers’ key characteristics; however, because these techniques require a lot of labor and time, it might be difficult for researchers to forecast enhancers and their powers. Therefore, computational techniques are considered an alternate strategy for handling this issue. Based on the types of algorithms that have been used to construct predictors, the current methodologies can be divided into three primary categories: ensemble-based methods, deep learning-based approaches, and traditional ML-based techniques. In this study, we developed a novel two-layer deep forest-based predictor for accurate enhancer and strength prediction, namely, NEPERS. Enhancers and non-enhancers are divided at the first level by NEPERS, whereas strong and weak enhancers are divided at the second level. To evaluate the effectiveness of feature fusion, block-wise deep forest and other algorithms were combined with multi-view features such as PSTNPss, PSTNPdss, CKSNAP, and NCP via 10-fold cross-validation and independent testing. Our proposed technique performs better than competing models across all parameters, with an ACC of 0.876, Sen of 0.864, Spe of 0.888, MCC of 0.753, and AUC of 0.940 for layer 1 and an ACC of 0.959, Sen of 0.960, Spe of 0.958, MCC of 0.918, and AUC of 0.990 for layer 2, respectively, for the benchmark dataset. Similarly, for the independent test, the ACC, Sen, Spe, MCC, and AUC were 0.863, 0.865, 0.860, 0.725, and 0.948 for layer 1 and 0.890, 0.940, 0.840, 0.784, and 0.951 for layer 2, respectively. This study provides conclusive insights for the accurate and effective detection and characterization of enhancers and their strengths.

Stochastic resonance induced weak signal enhancement in a second-order tri-stable system with single-parameter adjusting

Weak signal detection methods based on stochastic resonance (SR) have been extensively studied due to their capability to utilize noise energy for enhancing weak signals. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak-signal detection with better output performance compared to other SR models. To optimize the output performance of the second-order tri-stable systems, a variety of parameter optimization methods have been proposed to optimize the parameters of the system. However, these optimization methods often require to optimize multiple parameters, which leads to an increase in computational costs and reduces the real-time processing efficiency of signal processing. Such multi-parameter optimization methods cannot meet the demands for timely signal processing in the context of big data. To address this challenge, this paper proposes two single-parameter-adjusting SR models. The proposed models can attain an ideal output performance by adjusting a single parameter in the SR system. The spectral amplification as an indicator is derived to quantitatively analyze the effects of the proposed models on SR output. On this basis, the influences of the proposed models on the SR output under different potential well parameters, noise intensities, signal frequency, and damping ratio are fully investigated through numerical simulations. At last, the proposed models are employed to process an experimental signal with a weak fault feature, and the experimental results verify the feasibility of the proposed models in optimizing the SR output. The research results can guide the design of tri-stable SR models and support the application of the SR-based signal processing model in the context of big data.

Heterostructured organic/MoS2 nanowall with synergistic SERS enhancement enabling direct and sensitive detection of contaminants

In recent years, the π-conjugated organic molecular system has attracted considerable attention for its surface-enhanced Raman scattering (SERS) properties. However, the practical application of π-conjugated organic materials as SERS substrates is limited due to the inefficient dissociation of excitons, weak enhancement, and poor reproducibility. Herein, we proposed a novel nanowall SERS heterocomposite based on 3D D(C7CO)-BTBT nanowalls and 2D few-layer MoS2 flakes. A significant SERS activity of the D(C7CO)-BTBT/MoS2 nanowall heterocomposite can be achieved by the charge transfer (CT) resonance enabled by the π-conjugated interactions between D(C7CO)-BTBT and MoS2 and the electromagnetic field spatial distribution induced by the D(C7CO)-BTBT nanowalls. The concentration limit of detection (LOD) of the methylene blue (MB) molecules absorbed on the heterostructured D(C7CO)-BTBT/MoS2 nanowall substrate is as low as 8.2 × 10−11 M, which is excellent among the reported organic-based substrates. A practical SERS detection for the 100 nm polystyrene (PS) nanoparticles on the D(C7CO)-BTBT/MoS2 substrates can be down to 4.5 × 105 ng/L, which is exceptional for the real-time monitoring of emerging contaminants based on SERS technology. This research not only provides a new SERS enhancement strategy for organic-based materials, but also may be helpful in exploring a potential candidate for SERS detection of emerging contaminants.

A Gabor filter based weak crack detection method for glassivation passivation parts wafer

Crack defects on the surface of glassivation passivation parts (GPP) wafers are a significant factor leading to chip functional failure. Thus, efficient and stable surface defect detection is required to ensure product quality. However, existing detection methods do not handle weak crack defects on the wafer surface with an inhomogeneous texture background effectively. To overcome this challenge, we propose a weak crack defect detection method for GPP wafers with complex backgrounds. First, a weak crack feature enhancement method is proposed based on the Gabor filter with different orientations. Then, the crack candidates are generated using the least-squares method to transform the shape-based information into a line segment and direction independence judgment. Finally, a line segment clustering method is implemented for the final crack defect recognition. Experimental results demonstrate that the proposed defect detection method works effectively and robustly with inhomogeneous texture backgrounds and satisfies the requirement of a real-time detection system.

The native SOC increase in woodland and lawn soil amended with biochar surpassed greenhouse — A seven-year field trial

The effects of applying biochar with the same characteristics and at the same dose on the storage, composition, and underlying mechanisms of native organic carbon (n-SOC) dynamics in different ecosystems are still unclear. This study aimed to explore the effects of biochar amendment (7 years) on carbon sequestration and the n-SOC pools of woodland, lawn, and greenhouse soils. The ‘water floating method’ and improved ‘combustion loss method’ were used in this study to quantify residual biochar in soil. The results showed that after 7 years, the amount of biochar left in woodland, lawn, and greenhouse soils was 67.12 %, 87.50 %, and 88.13 % of the initial applied amount, respectively. And the n-SOC content increased approximately 2.07, 3.07, and 0.22 times, respectively, mainly due to increases in the native soil humin (n-HM) content of the soil. Biochar also increased the proportion of large aggregates in woodland and lawn soil and increased the t-SOC content of aggregates in each particle size fraction. Additionally, biochar increased the t-SOC of greenhouse soil aggregates but had no significant effect on the distribution of aggregates. The presence of biochar increased native soil easily oxidizable carbon (n-EOC) and microbial biomass carbon (n-MBC) in all three ecosystems. And increases in n-MBC in woodland and lawn soils occurred, which promoted the depletion of native soil hot water dissolvable organic carbon (n-HWOC) and increased CO2 emissions. Furthermore, the microbial respiration quotient (qCO2) of woodland and greenhouse soils was reduced by biochar, and that of lawn soil was unchanged. The carbon use efficiency (CUE) of lawn soils were reduced, possibly because biochar reduced the abundance of soil fungi/bacteria (F/B). In summary, the 7-year application of biochar significantly enhanced the n-SOC content in woodland and lawn soils, mainly due to an increase in humin, while a weaker enhancement was observed in greenhouse soil.

A successive sources deletion method for enhancing the trackside acoustic weak signal of axle bearing

The traditional beamforming method has been a common tool for extracting the trackside acoustic weak signal of axle bearing and been applied to fault diagnosis. Nevertheless, considering the severe impact interference and the strong background noise, such as the rolling noise, the effectiveness of beamforming method would be poor. In order to overcome the issue, a new successive sources deletion approach based on the kurtosis and energy estimators (noted SSD-KE) is proposed for enhancing the trackside acoustic weak signal of axle bearing. Firstly, the time-domain beamforming is performed using the acoustic signal acquired by the microphone array. Then, the strong impact interference is iteratively removed in terms of a kurtosis estimator. Likewise, the energy is employed as a subsequent statistic estimator to further remove the strong background noise. Finally, the enhanced signal could be used for the fault diagnosis. The validity of SSD-KE is first examined by numerical simulations. Different configuration parameters and noise conditions are also simulated to demonstrate the superiority and robustness of SSD-KE. Furthermore, experiments considering two different types of axle bearing faults are carried out, and the obtained results emphasize that SSD-KE has better performance on weak signal enhancement and could be used for fault diagnosis under strong background noise, even with strong impact interference.

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, the original disconnector current data are transformed into two different but related classes: strongly enhanced and weakly enhanced data, by using the strong and weak enhancement modules. Second, we propose the coarse-grained contrastive learning module to preliminarily judge the possibility of faults by learning the features of strongly/weakly enhanced data. Finally, in order to further judge the fault causes, we propose a fine-grained contrastive learning module. By comparing the differences in the data, the final fault type was judged. Our proposed MG-CL framework shows higher accuracy and speed compared with the previous model.

High resolution analysis of plankton distributions at the Middle Atlantic Bight shelf-break front

The Middle Atlantic Bight (MAB) is a highly productive ecosystem, supporting several economically important commercial fisheries. Chlorophyll enhancement at the MAB shelf-break front has been observed only intermittently, despite several studies suggesting persistent upwelling at the front. High resolution cross-frontal transects were conducted during three two-week cruises in April 2018, May 2019, and July 2019. Mesoplankton distributions at the front were measured with a Video Plankton Recorder equipped with hydrographic and bio-optical sensors. Zooplankton were also sampled with a Multiple Opening/Closing Net and Environment Sensing System. Each of the three cruises had distinctly different frontal characteristics, with lower variability in frontal position in April 2018 and higher variability in May and July 2019, primarily due to frontal eddies and a Gulf Stream warm core ring, respectively. Eulerian means of all transect crossings within each cruise did not show mean frontal chlorophyll enhancement in April 2018 or July 2019, despite individual crossings showing chlorophyll enhancement in April 2018. Transformation of the April 2018 data into a cross-frontal coordinate system revealed a weak enhancement of chlorophyll and copepods at the front. Mean frontal chlorophyll enhancement was observed in May and was associated with enhancement in the periphery of a frontal eddy rather than the front itself. None of the planktonic categories observed were enhanced at the front in the cross-shelf mean distribution, though diatom chains and copepods were more abundant inshore of the front, particularly in May and July 2019, as well as within the center of a frontal eddy in May. The high variability of the MAB frontal region obscured the impact of ephemeral frontal enhancement in mean observations of April 2018, while frontal eddies contributed to chlorophyll enhancement in mean observations of May 2019. The influence of both argues for the necessity for 3-D models rather than idealized 2-D models to explain frontal behavior and its effects on biological responses.

Study on the mechanism of structure modification of amylopectin co-crystalized by sodium chloride to promote disulfide bond formation of alkali-soluble glutenin

Strength and stiffness are the main characteristics of high-quality dough products. As one of the key components to endow those functions to noodles, formation of disulfide bonds in alkali-soluble glutenin (ASG) is very important. Interaction of ASG and co-crystalized wheat amylopectin with NaCl could prompt formation of disulfide bonds of ASG and its mechanism was deduced in the paper. The results showed that the disulfide bonds contents of ASG were increased significantly by interaction with co-crystalized wheat amylopectin. The co-crystalized wheat amylopectin with 2% NaCl for 24 h at 30 °C was distinguished by the Mn distribution of 0.55–0.95 × 106 g/mol, more chains with 18∼24 glucose residues being grafted from chains of 4∼7 glucose residues during the eutectic process, presence of the weak resonance enhancement at 96.5 ppm, molecular aggregate diameter less than 120 μm, and only one of X-diffraction angles 2θ at 20.2°. In the process of interaction, Qβ, Qδ, Yγ, Yε of ASG and C1, C6 of what amylopectin involved in the formation of disulfide bond, the contents of intermolecular β-sheet decreased and those of intra-molecular aggregation extended β-sheet and random coils increased. The peak melting temperature and melting enthalpy of the mixture decreased significantly after co-crystallized wheat amylopectin interacted with the ASG. Co-crystalized wheat amylopectin with NaCl can be used as a reinforcing agent of wheat flour.

An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Lasing in Zn-doped GaAs nanowires on an iron film

In this work, we demonstrate optically pumped lasing in highly Zn-doped GaAs nanowires (NWs) lying on an iron film. The conically shaped NWs are first covered with an 8 nm thick Al2O3 film to prevent atmospheric oxidation and mitigate band-bending effects. Multimode and single-mode lasing have been observed for NWs with a length greater or smaller than 2 μm, respectively. Finite difference time domain calculations reveal a weak electric field enhancement in the Al2O3 layer at the NW/iron film interface for the lasing modes. The high Zn acceptor concentration in the NWs provides enhanced radiative efficiency and enables lasing on the iron film despite plasmonic losses. Our results open avenues for integrating NW lasers on ferromagnetic substrates to achieve new functionalities, such as magnetic field-induced modulation.

Strong Evidence of Heterogeneous Processing on Stratospheric Sulfate Aerosol in the Extrapolar Southern Hemisphere Following the 2022 Hunga Tonga‐Hunga Ha'apai Eruption

AbstractThe January 2022 eruption of Hunga Tonga‐Hunga Ha'apai (HT‐HH) caused the largest enhancement in stratospheric aerosol loading in decades and produced an unprecedented enhancement in stratospheric water vapor, leading to strong stratospheric cooling that in turn induced changes in the large‐scale circulation. Here we use satellite measurements of gas‐phase constituents together with aerosol extinction to investigate the extent to which the thick aerosol, excess moisture, and strong cooling enabled heterogeneous chemical processing. In the southern tropics, unambiguous signatures of substantial chlorine and nitrogen repartitioning appear over a broad vertical domain almost immediately after the eruption, with depletion of N2O5, NOx, and HCl accompanied by enhancement of HNO3, ClO, and ClONO2. After initially rising steeply, HNO3 and ClO plateau, maintaining fairly constant abundances for several months. These patterns are consistent with the saturation of N2O5 hydrolysis, suggesting that this reaction is the primary mechanism for the observed composition changes. The southern midlatitudes and subtropics show similar but weaker enhancements in ClO and ClONO2. In those regions, however, effects of anomalous transport dominate the evolution of HNO3 and HCl, obscuring the signs of heterogeneous processing. Perturbations in chlorine species are considerably weaker than those measured in the southern midlatitude stratosphere in 2020 following the Australian New Year's fires. The moderate HT‐HH‐induced enhancements in reactive chlorine seen throughout the southern middle and low‐latitude stratosphere, far smaller than those in typical winter polar vortices, do not lead to appreciable chemical ozone loss; rather, extrapolar lower‐stratospheric ozone remains primarily controlled by dynamical processes.

Micro-seismic monitoring using sparse planar array and a weak signal enhancement method

Abstract Traditional ground micro-seismic monitoring is performed by laying long survey lines. This is expensive and difficult to implement in complex mountainous areas and deep marine shale gas reservoirs in China. To address these challenges, this study proposes a ground micro-seismic monitoring method using a sparse planar array that offers greater flexibility in implementation. This study presents a weak signal enhancement method based on a broadband-array adaptive beamforming algorithm to improve the signal-to-noise ratio (SNR) of micro-seismic data collected by sparse planar arrays and to suppress coherent noise. The proposed method involves establishing a signal model for a broadband planar array, estimating the direction of arrival (DOA) of broadband signals using a grid search method, and setting constraint conditions and objective functions based on the DOA results. The optimal weight vector is then calculated by solving the objective function to obtain the desired signal and suppress noise. This study demonstrates that the proposed method effectively improved the SNR and suppressed coherent noise in synthetic and real data. It also highlights the effectiveness of the sparse planar array as a ground micro-seismic monitoring method and the adaptive broadband beamforming method as a practical weak signal enhancement technology.

Hemispheric Albedo Asymmetries across Three Phases of CMIP

Abstract Earth’s planetary albedo shows a remarkable hemispheric symmetry. We assess to what extent CMIP models symmetrize the hemispheric clear-sky albedo asymmetry and what the role of clouds is for this. Following Voigt et al., we calculate a reference TOA reflected solar radiation considering the masking of clear-sky asymmetry by symmetric cloud contributions. We use the simple radiation model of Donohoe and Battisti to estimate this benchmark and to separate surface, aerosol, and cloud contributions to the compensation of this benchmark. In CERES, tropical clouds enhance the reference asymmetry while extratropical cloud asymmetries balance the reference asymmetry and the additional asymmetry introduced by tropical clouds. CMIP multimodel means show similar results as CERES. Clouds compensate reference asymmetries by 85% (CMIP3), 65% (CMIP5), and 78% (CMIP6) as compared with 98% for CERES. Spatial distributions of hemispheric differences indicate clear improvements across the CMIP phases. Remaining all-sky reflection asymmetries predominantly result from too-small, partly compensating cloud asymmetries: a too-weak enhancement of the reference asymmetry in the tropical Atlantic and eastern Pacific Oceans is accompanied by a too-weak compensation by extratropical clouds. Thus, tropical clouds and extratropical storm track regions are largely responsible for the compensation of hemispheric clear-sky asymmetries in CERES and CMIP, and for remaining biases in the GCMs. An unexpected result is the magnitude of model biases in the clear-sky asymmetries, which potentially condition systematic cloud biases. Experiments testing cloud-controlling factors influencing hemispheric asymmetry could help us to understand what drives hemispheric cloud differences.