Second-order Interaction Research Articles

Technical note: Effects of chemical, radiational, and thermal treatment of bone tissue on material stiffness and anisotropy.

We evaluated the effects of four treatments for pathogen inactivation in bone tissue (ethanol storage, formalin fixation, gamma irradiation, and heat treatment via autoclave) on stiffness and anisotropy values in bone samples. Cortical bone samples from the humerus of 14 bovine specimens were subjected to Knoop microindentation analysis in longitudinal and transverse planes of section and four indenter orientations within each section. From each specimen, individual samples were assigned to one of five treatment conditions: 50% ethanol-saline solution, formalin immersion, gamma irradiation, autoclave, and buffered saline (controls). Each sample was microindented 100 times and the resultant stiffness data were analyzed by a resampled factorial analysis of variance. First- and second-order interactions as well as main effects of indenter orientation, treatment, and specimen were significant for both transverse and longitudinal sections, with the sole exception that indenter orientation-treatment interaction was nonsignificant for longitudinal sections. Interaction plots reveal that thermal (autoclave) and irradiation treatments depress stiffness values the most, while patterns of indentation stiffness at different orientations are relatively unaffected. Patterns of anisotropy are relatively unaffected by preservative treatments, but elastic modulus changes are consistent and unambiguous. Formalin and ethanol treatments are most comparable to controls and represent the best preservative media for mechanical testing. These options, however, are likely to be the least effective for ensuring the inactivation or sterilization of potentially contaminated samples.

Risk Factors for Musculoskeletal Disorders in Korean Farmers: Survey on Occupational Diseases in 2020 and 2022.

This study investigated factors influencing the prevalence of musculoskeletal disorders (MSDs) resulting from agricultural work, utilizing the 2020 and 2022 occupational disease survey data collected by the Rural Development Administration. The combined data from these years indicated a 6.02% prevalence of MSDs, reflecting a significant class imbalance in the binary response variables. This imbalance could lead to classifiers overlooking rare events, potentially inflating accuracy assessments. We evaluated five distinct models to compare their performance using both original and synthetic data and assessing the models' performance based on synthetic data generation. In the multivariate logistic model, we focused on the main effects of the covariates as there were no statistically significant second-order interactions. Focusing on the random over-sampling examples (ROSE) method, gender, age, and pesticide use were particularly impactful. The odds of experiencing MSDs were 1.29 times higher for females than males. The odds increased with age: 2.66 times higher for those aged 50-59, 4.60 times higher for those aged 60-69, and 7.16 times higher for those aged 70 or older, compared to those under 50. Pesticide use was associated with 1.26 times higher odds of developing MSDs. Among body part usage variables, all except wrists and knees were significant. Farmers who frequently used their necks, arms, and waist showed 1.27, 1.11, and 1.23 times higher odds of developing MSDs, respectively. The accuracy of the raw method was high, but the ROSE method outperformed it for precision and F1 score, and both methods showed similar AUC.

Intrinsic process for upconversion photoluminescence via K -momentum–phonon coupling in carbon nanotubes

We investigate the intrinsic microscopic mechanism of photon upconversion in air-suspended single-walled carbon nanotubes through photoluminescence and upconversion photoluminescence spectroscopy. Nearly linear excitation power dependence of upconversion photoluminescence intensity is observed, indicating a one-photon process as the underlying mechanism. In addition, we find a strongly anisotropic response to the excitation polarization, which reflects the intrinsic nature of the upconversion process. In upconversion photoluminescence excitation spectra, three peaks are observed, which are similar to photoluminescence sidebands of the K-momentum dark singlet exciton. The features in the upconversion photoluminescence excitation spectra are well reproduced by our second-order exciton-phonon interaction model, enabling the determination of phonon energies and relative amplitudes. The analysis reveals that the upconversion photoluminescence can be described as a reverse process of the sideband emission linked to the K-momentum phonon modes. The validity of our model is further reinforced by temperature-dependent upconversion photoluminescence excitation measurements reflecting variations in the phonon population. Our findings underscore the pivotal role of the resonant exciton-phonon coupling in pristine carbon nanotubes and present potential for advanced optothermal technologies by engineering the excitation pathways. Published by the American Physical Society 2024

Clarification of apple juice using cold plasma treated mixed cellulose esters membrane: flux modeling, membrane fouling, and juice physicochemical properties evaluation

This study aimed to clarify apple juice using unmodified and low-pressure cold plasma (gas: air, power: 10 W, and exposure time: 180 s) surface-modified mixed cellulose esters (MCE) microfiltration membranes. The effect of feed pressure (20, 40, and 60 kPa), feed flow rate (10, 15, and 20 mL/s), and feed temperature (27 and 40°C) on the permeate flux was evaluated using response surface methodology (RSM). The binary interaction of feed pressure and temperature and the second-order (non-linear) interaction of feed pressure and flow rate had a significant effect on the permeate flux (P<0.05). Generally, the process at 60 kPa, 15 mL/s, and 40 °C resulted in the maximum permeate flux. The cold plasma surface modification of the MCE membrane increased the permeate flux by about 45% at the optimum process conditions. Evaluation of membrane fouling showed that the cake formation was the predominant membrane fouling mechanism during both membrane processing. The physicochemical properties evaluation revealed that the surface-modified membrane produced clarified apple juice with antioxidant activity, reducing sugars, total sugars, phenolic, and vitamin C contents about 18.87%, 12.9%, 15.49%, 24.53%, and 45.15% more than clarified apple juice produced with unmodified MCE membrane, respectively.

Real-time phase-resolved ocean wave prediction in directional wave fields: Second-order Lagrangian wave models

The Improved Choppy Wave Model (ICWM) was established to achieve a second-order Lagrangian expansion of surface waves. The second-order Lagrangian nonlinear interaction terms were found to be negligible and were therefore discarded. However, these interactions in directional wave fields remained unexplored. ICWM was successfully used, especially for floating offshore wind turbines, but it was noted that its accuracy in predicting directional sea states needed improvement. Hence, we formulated the Complementary ICWM (CICWM) with the nonlinear terms for free surface elevation in directional waves. Detailed formulations for data assimilation and wave propagation were provided, enabling real-time wave forecasting for directional seas using a simplified assimilation method. Comparing the model performances against tank-scale experiments showed that CICWM enhances the surface elevation description in directional seas. Ultimately, it was confirmed that the second-order Lagrangian model can reduce the prediction error of the linear wave model by 90% in directional seas for the experimental setups and sea states investigated in this study.

Higher-order interactions and zero-determinant strategies in the public goods game

Abstract Since the ingenious discovery of zero-determinant (ZD) strategies by Press and Dyson, many efforts have been devoted to the evolutionary performance of ZD strategies. Recently, the effects of higher-order interactions on evolutionary games have attracted widespread interests, whereas it remains unknown how higher-order interactions affect the evolutionary performance of ZD strategies. This paper focuses on the role of higher-order interactions on evolutionary ZD strategies in iterated public goods game (IPGG), where the baseline payoff is a key parameter to describe nodes' extent of reciprocity in both first-order and second-order interactions. Through the adaptive-like dynamics, we found that there is a critical value of each network, above which the networked game will converge to a consensus state where all the nodes obtain the same payoff. This critical value is significantly affected by the relative strength of higher-order interactions with a U-shaped trend. Numerical simulations are carried out to explore how the network structures affect the dynamics. The results in networks with different sizes indicate that networks with higher average degree are more easily to converge to the consensus state. The simulations on a real-world network further support the theoretical conclusions.

Three-wave mixing experiments in indium–tin–oxide thin-films with no phase matching

Abstract One challenge of using nonlinear optical phenomena for practical applications is the need to perform phase-matching. Recently, epsilon-near-zero materials have been shown to demonstrate strong optical nonlinearities, in addition to their other unique properties. As suggested by their name, the permittivity of the material is close to zero for a certain wavelength range. We demonstrate that this small permittivity allows for efficient three-wave mixing interactions to take place in indium–tin–oxide thin films without the need for phase matching the pump and signal beams. The efficiency of the second-order nonlinear interactions is characterized, and cascaded three-wave mixing is demonstrated.

Rogue and solitary waves of a system of coupled nonlinear Schrödinger equations in a left-handed transmission line with second-neighbors coupling

The effects of the second-order neighbor interaction have been effective on the coherent localized waves and modulation instability spectrum. A coupled nonlinear Schrödinger equation is derived in the nonlinear left-handed electrical lattice, and the linear stability is used to formulate the modulation instability spectrum expression. Unstable modes are displayed to show symmetric lobes and increasing bandwidths under the influence of the neighbor coupling. The Benjamin-Feir instability has prospered in the network, and for weak perturbed wave numbers, the unstable modes increase. The numerical simulation is used to develop localized waves, including rogue waves with one crest and two humps, Akhmediev breathers, and other modulated structures. We notice that, for strong values of the perturbed wave number, an exponential growth of the continuous wave arises to confirm the fact that the nonlinear left-handed electrical lattice with coupling strength is opened to coherent localized waves. The long-lived nature of the obtained structures has also been demonstrated for specific times of propagation.

Collaborative publication of related articles puts focus on emerging topics: editorial

The editors of Advances in Optics and Photonics and Optica introduce a collaborative publishing effort that highlights emerging fields in ways that will benefit both new and seasoned researchers. In this first example, a tutorial and a mini-review cover the physics of second-order nonlinear interactions in dispersion-engineered nonlinear photonic devices.

Collaborative publication of related articles puts focus on emerging topics: editorial

The editors of Advances in Optics and Photonics and Optica introduce a collaborative publishing effort that highlights emerging fields in ways that will benefit both new and seasoned researchers. In this first example, a tutorial and a mini-review cover the physics of second-order nonlinear interactions in dispersion-engineered nonlinear photonic devices.

Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles

The evolution of technology has raised concerns regarding cybersecurity for intelligent connected vehicles (ICVs). In-vehicle network in ICVs lacks robust protection mechanisms, making it vulnerable to cyber threats. In response, intrusion detection systems (IDSs) for ICVs have been developed to protect vehicles from malicious cyber attacks. However, current IDS methods solely rely on independent features, limiting their learning capabilities and increasing the number of false detections. Moreover, many IDSs require the knowledge of mapping between network messages and contents, which restricts their application. To address these limitations, we propose the Multi-order Feature Interaction-aware Intrusion Detection (MIFI) scheme for ICVs. Feature attention cross network is designed to address higher-order feature interactions, while factorization machine is used for second-order interactions. Then a discriminator is utilized to detect the attacks. MIFI expands the feature space through features interaction, thereby enhancing its ability to detect attacks. Moreover, it perceives the relationships of vehicle messages, facilitating intrusion detection without knowing the corresponding rules of vehicle messages. The performance of the proposed method is evaluated on two real-vehicle datasets, affirming its effectiveness and robustness. MIFI achieves an accuracy of over 99% in detecting different attacks. The proposed method can improve the accuracy of traditional IDS to a maximum of 99.99%, and increase the highest F1-score to 97.18%, demonstrating the model’s ability of achieving multi-order feature interactions. Ultimately, MIFI is suitable for intrusion detection in different types of ICV networks, significantly contributing to the cybersecurity of ICVs.

Identifying heterogeneity for increasing the prediction accuracy of machine learning models

In recent years, the significance of machine learning in agriculture has surged, particularly in post-harvest monitoring for sustainable aquaculture. Challenges like heterogeneity, irrelevant variables and multicollinearity hinder the implementation of smart monitoring systems. However, this study focuses on investigating heterogeneity among drying parameters that determine the moisture content removal during seaweed drying due to its limited attention, particularly within the field of agriculture. Additionally, a heterogeneity model within machine learning algorithms is proposed to enhance accuracy in predicting seaweed moisture content removal, both before and after the removal of heterogeneity parameters and also after the inclusion of single-eliminated heterogeneity parameters. The dataset consists of 1914 observations with 29 independent variables, but this study narrows down to five: Temperature (T1, T4, T7), Humidity (H5), and Solar Radiation (PY). These variables are interacted up to second-order interactions, resulting in 55 variables. Variance inflation factor and boxplots are employed to identify heterogeneity parameters. Two predictive machine learning models, namely random forest and elastic net are then utilized to identify the 15 and 20 highest important parameters for seaweed moisture content removal. Evaluation metrics (MSE, SSE, MAPE, and R-squared) are used to assess model performance. Results demonstrate that the random forest model outperforms the elastic net model in terms of higher accuracy and lower error, both before and after removing heterogeneity parameters, and even after reintroducing single-eliminated heterogeneity parameters. Notably, the random forest model exhibits higher accuracy before excluding heterogeneity parameters.

Cropping Flax for Grain and Fiber: A Case-Study from Italy

Flax (Linum usitatissimum L.) can be grown both as an oil crop and as a fiber crop, and this offers new opportunities when included in the framework of a whole-crop biorefinery, a system in which a range of products are made from portions of grain and straw and in which both of these should be satisfactorily produced. In the present experiment, the effect of flax genotypes (7 varieties), cultivation sites (two locations) and seasons (two years) were tested with a standard randomized complete block design, in search of a compromise for the production performance for both grain and straw, with the aim of reintroducing flax back into the northern Italian environment. Overall, grain yield reaches an average value of about 1.4 t ha−1 (dw), while straw yield reaches 2.77 t ha−1 (dw). The former is strictly dependent on the environmental effects of the growing site and season, while the effect of genotype was not significant. The straw yield also depends on the second-order interaction of the factors analyzed, although the performance of three varieties, Festival, Solal and Linoal, was noteworthy and seemed to respond well in both environments. Overall, it was found that flax can be conveniently grown for both grain and straw production.

Theoretical study on the structure, spectroscopic, and current–voltage behavior of 11-Cis and Trans retinal isomers in rhodopsin

In this study, the electronic transport properties of 11-Cis and Trans retinal, components of rhodopsin, were investigated as optical molecular switches using the nonequilibrium Green’s function (NEGF) formalism combined with first-principles density functional theory (DFT). These isomers, which can be reversibly converted into each other, were examined in detail. The structural and spectroscopic properties, including infrared (IR), Raman, nuclear magnetic resonance (NMR), and ultraviolet (UV) spectra, were analyzed using the hybrid B3LYP/6–311 + + G** level of theory. Complete vibrational assignments were performed for both forms utilizing the scaled quantum mechanical force field (SQMFF) methodology. To evaluate the conductivity of these molecules, we utilized current–voltage (I-V) characteristics, transmission spectra, molecular projected self-consistent Hamiltonian (MPSH), HOMO–LUMO gap, and second-order interaction energies (E2). The trendline extrapolation of the current–voltage plots confirmed our findings. We investigated the effect of different electrodes (Ag, Au, Pt) and various connection sites (hollow, top, bridge) on conductivity. The Ag electrode with the hollow site exhibited the highest efficiency. Our results indicate that the Cis form has higher conductivity than the Trans form.

G2D-Net: Efficient Dehazing with Second-Order Gated Units

Image dehazing aims to reconstruct potentially clear images from corresponding images corrupted by haze. With the rapid development of deep learning-related technologies, dehazing methods based on deep convolutional neural networks have gradually become mainstream. We note that existing dehazing methods often accompany an increase in computational overhead while improving the performance of dehazing. We propose a novel lightweight dehazing neural network to balance performance and efficiency: the g2D-Net. The g2D-Net borrows the design ideas of input-adaptive and long-range information interaction from Vision Transformers and introduces two kinds of convolutional blocks, i.e., the g2D Block and the FFT-g2D Block. Specifically, the g2D Block is a residual block with second-order gated units, which inherit the input-adaptive property of a gated unit and can realize the second-order interaction of spatial information. The FFT-g2D Block is a variant of the g2D Block, which efficiently extracts the global features of the feature maps through fast Fourier convolution and fuses them with local features. In addition, we employ the SK Fusion layer to improve the cascade fusion layer in a traditional U-Net, thus introducing the channel attention mechanism and dynamically fusing information from different paths. We conducted comparative experiments on five benchmark datasets, and the results demonstrate that the g2D-Net achieves impressive dehazing performance with relatively low complexity.

Abstract PO1-12-11: How do chemotherapy and age affect physical performance in breast cancer patients over 12 months of treatment? Results from the UPBEAT Study (UPBEAT WF-97415)

Abstract Introduction: Chemotherapy-associated functional decline is a concern for women with breast cancer. Objective: To assess effects of chemotherapy and age on objectively measured physical performance among women with breast cancer over time. Methods: Analyses utilized the multicenter, longitudinal Understanding and Predicting fatigue, cardiovascular decline, and events after BrEast cAncer sTudy (UPBEAT), a study conducted through the Wake Forest NCORP Research Base (5UG1CA189824). We examined physical performance at baseline and at 3 and 12 months and compared means across time by age group (&amp;lt; 65 years v. 65+ years at baseline) and by cancer/control group. For the latter variable, we had 3 groups of participants: 1) newly diagnosed breast cancer patients with Stage 1-3 breast cancer who received chemotherapy (CC; n= 201); 2) newly diagnosed breast cancer patients receiving no chemotherapy (CNC; n=57); and age-matched healthy controls (HC; n=145). The primary outcome was the Short Physical Performance Battery (SPPB) score (range 0-12, worst to best performance, including chair stands, gait speed, and balance testing). Effects of cancer/control group, age group, and time (treated categorically rather than ordinally) were estimated in a mixed model with a random subject effect and fixed effects of time (baseline, 3 month, 12 month) and cancer/control group. In the model we included all first-order interactions as well as the one second-order interaction between all three predictors. We also used linear contrasts to compare estimated means within groups. Analyses were conducted in SAS 9.4. A two-tailed alpha of 0.05 was used throughout to denote statistical significance. Results: Among 403 patients accrued through the Wake Forest Research Base of NCORP, 201 were in the CC group, and 18.9% of these were aged 65 and older; 57 were in the CNC group, and 33.3% were older; and 145 were in the HC group, and 15.9% were 65 or older. We observed a significant (p&amp;lt; 0.0001) main effect of older age group in our model: across all 3 groups, and all 3 time points, those who were 65 years of age or older had worse SPPB scores than those who were younger. Healthy controls tended to have better mean scores than the other 2 cancer/control groups, though the main effect of group did not achieve statistical significance (p=0.06). We observed no significant interaction terms; in other words, the effect of being older (in terms of association with worse SPPB scores) did not vary by time or group. Within the CC group, both the older and the younger groups declined significantly (p=0.04) in mean SPPB at 3 months compared to baseline, but by 12 months, the means had returned to their starting points. No other groups showed significant changes over time. Conclusions: Chemotherapy was associated with decline in SPPB score over the 12-month time period regardless of age. Older adults, regardless of chemotherapy status and time point, had lower SPPB scores. Citation Format: Shirley Bluethmann, Beverly Levine, Brianna Leitzelar, Katherine Ansley, Alexandra Thomas, Nancy Avis, Gregory Hundley, Heidi Klepin, Kathryn Weaver, Glenn Lesser. How do chemotherapy and age affect physical performance in breast cancer patients over 12 months of treatment? Results from the UPBEAT Study (UPBEAT WF-97415) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-12-11.

Severe perturbation and observation of forbidden transitions in asymmetrically deuterated methanol (CHD2OH) and atlas of very accurate torsional rotational spectrum obtained using Synchrotron radiation

High-resolution spectral studies have been continued for the doubly deuterated methanol species (CHD2OH) in the entire region from far infrared (FIR) to Infrared (IR). The spectrum has been obtained using a conventional Fourier transform spectrometer and the state-of-the-art Synchrotron radiation-based spectrometer with unprecedented resolution and very high signal-to-noise (S/N). During the investigation severe perturbation was encountered. The perturbation has been characterized as due to first and second-order Coriolis interaction. The interaction affected a wide range of states and caused the spectral study quite challenging. The perturbation caused a variety of mixing of states producing hybrid states. This hybridization resulted in producing a large number of transitions which should not be allowed by the dipole selection rules via the “intensity borrowing effect”. It was possible to identify a large number of such forbidden transitions that gain sufficient strength through the “Intensity borrowing” effect. The transitions connecting to these perturbed states have been analyzed using standard Coriolis interaction formulae. The possibility of “Fermi” interaction has been excluded as the interaction is highly dependent on the rotational quantum number J. In general, this kind of transition should be excluded from global analysis and should be treated in isolation. It is pertinent to note that some previously published works studied these states using un-substantiated assignments being perhaps unaware of the perturbation. Corrections have been suggested for the analysis and understanding of the properties of the energy levels which have subtle differences with the energy levels of normal asymmetric molecules. In all, available microwave (MW) millimeter wave (MMW) far infrared (FIR), and infrared (IR) assignments have been put together to prepare the most accurate and and complete atlas of about 12,000 spectral lines. This atlas (Appendix A) should be considered a work-in-progress supplement to this paper and supersedes all other lists known to date on this species can be had from one of us (IM) or via Researchgate.

D-optimal saturated designs for main effects and interactions in 2k-factorial experiments

In a 2 k -factorial experiment with limited resources, when practitioners can identify the non-negligible effects and interactions beforehand, it is common to run an experiment with a saturated design that ensures the unbiased estimation of the non-negligible parameters of interest. We propose a method for the construction of D-optimal saturated designs for the mean, the main effects, and the second-order interactions of one factor with the remaining factors. In the process, we show the problem is just as hard as the Hadamard determinant problem.

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

Sub-filter-scale shear stress analysis in hypersonic turbulent Couette flow

Direct numerical simulations of hypersonic turbulent Couette flows are performed for top-wall Mach numbers of 6, 7 and 8, inspired by non-reactive high-enthalpy wind tunnel free-stream conditions, with the goal of analysing the physical processes driving the sub-filter-scale (SFS) stresses to inform development of large-eddy simulation techniques for hypersonic wall-bounded flows. Semi-local scaling laws collapse mean profiles and second-order turbulent statistics well, in spite of the strong wall-normal gradients of temperature and density. On the other hand, the SFS shear stresses exhibit an unexpected profile characterized by a region of pronounced shear stress deficit, which becomes more pronounced for higher Mach numbers. Instantaneous visualizations suggest that the SFS shear stress deficit is induced by the counter-gradient resolved momentum transport driven by residual velocity motions at the interface between high-density low-speed streaks being ejected away from the wall, and low-density high-speed ones replacing the displaced fluid, qualifying this as a compressibility effect. It is shown that the SFS shear stresses are primarily driven by second-order interactions between residual velocities, in spite of their triply nonlinear nature. This, in turn, motivated a statistical quadrant analysis revealing the presence of a SFS shear stress deficit, that is, SFS processes driving momentum transport of the resolved field towards the top wall. Additionally, higher-Reynolds-number simulations reveal that there is an upper limit of spatial filter widths to resolve large-scale structures, and such deficit is observable at any Reynolds number when a reasonable spatial filter is applied.