Critical Benchmark Research Articles

Biomarker and adverse outcome pathway responses of Tubifex tubifex(sludge worm) exposed to environmentally-relevant levels of acenaphthene: insights from behavioral, physiological, and chemical structure-activity analyses.

Polycyclic aromatic hydrocarbons (PAHs), including acenaphthene, pose a significant threat to aquatic ecosystems by harming vital organisms such as benthic invertebrates. This study evaluated the impact of environmentally relevant concentrations of acenaphthene on Tubifex tubifex, focusing on sublethal acute toxicity and subchronic biomarker responses. Key biomarkers assessed included histopathological changes and the modulation of antioxidant enzymes: catalase (CAT), superoxide dismutase (SOD), glutathione S-transferase (GST), and malondialdehyde (MDA). Additionally, the study examined structure-activity relationships and species sensitivity distribution (SSD). Concentrations exceeding the solubility threshold of acenaphthene (3.9 mg/L) triggered distinct, concentration-dependent behavioral responses in Tubifex tubifex, such as clumping, mucus secretion, and body wrinkling. Prolonged exposure exacerbated these behavioral dysfunctions, while subchronic exposure resulted in significant histopathological alterations, including epithelial hyperplasia, inflammation, edema, fibrosis, and degenerative changes. The edematic appearance of the body wall suggested a potential immune response to exposure. Furthermore, increased activities of CAT, SOD, and GST indicated oxidative stress in the worms. The study found a 1.5-fold increase in CAT and GST activity, a fivefold increase in SOD, and a striking 100-fold increase in MDA levels compared to controls, signifying an overwhelmed antioxidant defense system and potential cellular disruption. The SSD curve revealed hazard concentrations (HC50 and HC90), indicating that Tubifex tubifex exhibited lower sensitivity to acenaphthene compared to other taxa. In silico analysis and read-across models confirmed the potential of acenaphthene to induce significant oxidative stress upon exposure. The correlation between biomarker responses and structure-activity relationship analysis highlighted the aromatic nature of acenaphthene as a key factor in generating reactive metabolites, inhibiting antioxidant enzymes, and promoting redox cycling, ultimately contributing to adverse outcomes. These findings, coupled with behavioral responses and SSD curve inferences, underscore the importance of the solubility threshold of acenaphthene as a critical benchmark for evaluating its ecological impact in aquatic environments.

An upper limit on the spins of merging binary black holes formed through isolated binary evolution

As the sensitivity of ground-based gravitational wave detectors progressively increases, observations of black hole mergers will provide us with the joint distribution of their masses and spins. This will be a critical benchmark to validate different formation scenarios. Merging binary black holes formed through the evolution of isolated binary systems require both components to be stripped of their hydrogen envelopes before core-collapse. The rotation rates of such stripped stars are constrained by the critical rotation limit at their surface, including its deviation from the Keplerian value owing to the outward force provided by radiation. This sets a restriction on their angular momentum content at core-collapse. We aim to determine if this restriction plays a role in the spins of binary black hole mergers. We used detailed calculations of stripped stars with the MESA code at low metallicities ($Z=Z_ $Z_ and $Z_ to determine the dimensionless spins of black holes produced by critically rotating stellar progenitors. To study how such progenitors can arise, we considered their formation through chemically homogeneous evolution (CHE) in binary stars. We used a semi-analytical model to study the physical processes that determine the final angular momentum of CHE binaries, and compared our results against available population synthesis models that rely on detailed binary evolution calculations. We find that above black hole masses of $ 25M_ the dimensionless spin parameter of critically rotating stripped stars ($a=Jc/(GM^2)$) is below unity. This results in an exclusion region at high chirp masses and effective spins that cannot be populated by isolated binary evolution. CHE can produce binaries where both black holes hit this limit, producing a pileup at the boundary of the excluded region. High-spin black holes arise from very low-metallicity CHE systems with short delay times, which merge at higher redshifts. On the other hand, the contribution of CHE to merging binary black holes detected in the third observing run of the LVK collaboration is expected to be dominated by systems with low spins ($ eff <0.5$) that merge near redshift zero. Owing to its higher projected sensitivity and runtime, the fourth observing run of the LVK collaboration can potentially place constraints on the high-spin population and the existence of a limit set by critical rotation.

Hydrodynamic and energy-harvesting performances of a compact-array OWC device: An experimental study

This study proposed and evaluated a compact-array oscillating water column device called “Rainbow”, which is expected to adapt to the nearshore wave climate characteristics in China, and satisfy the quick response requirements to coastal and offshore electricity. The proposed device employs a bent-duct buoy concept, with an open mouth in two opposite directions, and embraced a moon pool in a ring shape. Moreover, models under a scale ratio of 1:10 are designed and manufactured, which are fixed on the bottom and tested in regular and irregular wave scenarios in a wave tank. The time histories of critical performance parameters, hydrodynamic and aerodynamic performances, and capture width ratios in different modules were recorded and analyzed to reveal the operating mechanisms of the model. The various module arrangement topologies were found to cause significant differences in the value distribution characteristics of the capture width ratio in different modules, peaking at 0.26. The experimental results can be employed as critical benchmark cases for further designs and optimizations.

Enhancing thermal conductivity of AlN ceramics via vat photopolymerization through refractive index coupling and oxygen fixation

Despite the growing development of ceramic fabrication by vat photopolymerization (VP), major gaps remain in application. Particularly in the case of VP-printed aluminum nitride (AlN) ceramic, the thermal conductivity is still below 170 W·m−1·K−1, a critical benchmark for efficient heat dissipation. To address this challenge, here we prepared an AlN slurry with high curing thickness through RI coupling between liquid-solid phase, and took into account of the rheological property under high solid loading. Full dense AlN green bodies with solid loading up to 50 vol% were successfully printed. Aiming to to tackle the degradation of thermal conductivity issue caused by oxygen increment of AlN via VP, we systemically studied the form of oxygen in AlN preparation processes. Through the sintering optimization, the oxygen element from the hydrolysis of AlN surface was fixated in the sintering aid Y2O3. Eventually, without any special process control or additional treatment, the final sintered AlN ceramics prepared by the developed slurry present a full densification and the highest thermal conductivity (up to 187.9 W∙m−1∙K−1) of any known additive manufactured ceramics.

Benchmarking Reconstructive Spectrometer with Multiresonant Cavities.

Recent years have seen the rapid development of miniaturized reconstructive spectrometers (RSs), yet they still confront a range of technical challenges, such as bandwidth/resolution ratio, sensing speed, and/or power efficiency. Reported RS designs often suffer from insufficient decorrelation between sampling channels, which, in essence, is due to inadequate engineering of sampling responses. This in turn results in poor spectral-pixel-to-channel ratios (SPCRs), typically restricted at single digits. So far, there lacks a general guideline for manipulating RS sampling responses for the effectiveness of spectral information acquisition. In this study, we shed light on a fundamental parameter from the compressive sensing (CS) theory-the average mutual correlation coefficient ν-and provide insight into how it serves as a critical benchmark in RS design. To this end, we propose a novel RS design with multiresonant cavities, consisting of a series of partial reflective interfaces. Such multicavity configuration allows the superlative optimization of sampling matrices to achieve minimized ν. Experimentally, we implement a single-shot, dual-band RS on a SiN platform, realizing an overall operation bandwidth of 270 nm and a <0.5 nm resolution with only 15 sampling channels per band. This leads to a record high SPCR of 18.0. Moreover, the proposed multicavity design can be readily adapted to various photonic platforms, ranging from optical fibers to free-space optics. For instance, we showcase that by employing multilayer coatings, an ultrabroadband RS can be optimized to exhibit a 700 nm bandwidth with an SPCR of over 100.

Sustainable and Robust Cellulose‐Based Core–Shell Hydrogels Recycled from Waste Cotton Fabrics as High‐Performance Food Coolants

AbstractIdeal temperature condition is one of the essential determinants that critically impact the quality of food products. Conventional water‐based ice cubes present challenges from meltwater being breeding grounds for microorganisms and heightening the risk for cross‐contamination. Hereby, the presented cellulose‐based hydrogels crosslinked by epichlorohydrin are dip‐coated with alginate/calcium chloride to form a core–shell structure for achieving the critical benchmarks of an ideal food coolant with limited meltwater production, high‐water retention capacity, and high mechanical strength. The structures and properties of the hydrogels before and after freeze–thaw cycles are characterized by scanning electron microscopy, compressive test, water retention test, and differential scanning calorimetry. All formulated hydrogels demonstrate promising compressive strength, latent heat of fusion, and water retention properties. Notably, the C2A10Cl hydrogel exhibits a maximum compressive strength of 144.7 kPa and high latent heat of fusion of 272.5 J g–1, which is better than previously reported sustainable hydrogel coolants. Furthermore, comparison studies reveal that the cellulose‐based hydrogels demonstrate a similar thawing pattern to conventional ice cubes but without the generation of any meltwater. The temperature of blueberries can be cooled down from 22 to 3.9 °C in 32 min by the hydrogels and in 26 min by ice cubes, respectively.

Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications

The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing (DSP) schemes that are highly cost-effective with both high performance and low complexity. As benchmarks for nonlinear compensation methods, however, traditional DSP designed with block-by-block modules for linear compensations, could exhibit residual linear effects after compensation, limiting the nonlinear compensation performance. Here we propose a high-efficient design thought for DSP based on the learnable perspectivity, called learnable DSP (LDSP). LDSP reuses the traditional DSP modules, regarding the whole DSP as a deep learning framework and optimizing the DSP parameters adaptively based on backpropagation algorithm from a global scale. This method not only establishes new standards in linear DSP performance but also serves as a critical benchmark for nonlinear DSP designs. In comparison to traditional DSP with hyperparameter optimization, a notable enhancement of approximately 1.21 dB in the Q factor for 400 Gb/s signal after 1600 km fiber transmission is experimentally demonstrated by combining LDSP and perturbation-based nonlinear compensation algorithm. Benefiting from the learnable model, LDSP can learn the best configuration adaptively with low complexity, reducing dependence on initial parameters. The proposed approach implements a symbol-rate DSP with a small bit error rate (BER) cost in exchange for a 48% complexity reduction compared to the conventional 2 samples/symbol processing. We believe that LDSP represents a new and highly efficient paradigm for DSP design, which is poised to attract considerable attention across various domains of optical communications.

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Risk-based critical concentrations of enteric pathogens for recreational water criteria and recommended minimum sample volumes for routine water monitoring

Concerns are rising about the contamination of recreational waters from human and animal waste, along with associated risks to public health. However, existing guidelines for managing pathogens in these environments have not yet fully integrated risk-based pathogen-specific criteria, which, along with recent advancements in indicators and markers, are essential to improve the protection of public health. This study aimed to establish risk-based critical concentration benchmarks for significant enteric pathogens, i.e., norovirus, rotavirus, adenovirus, Cryptosporidium spp., Giardia lamblia, Campylobacter jejuni, Salmonella spp., and Escherichia coli O157:H7. Applying a 0.036 risk benchmark to both marine and freshwater environments, the study identified the lowest critical concentrations for children, who are the most susceptible group. Norovirus, C. jejuni, and Cryptosporidium presented lowest median critical concentrations for virus, bacteria, and protozoa, respectively: 0.74 GC, 1.73 CFU, and 0.39 viable oocysts per 100 mL in freshwater for children. These values were then used to determine minimum sample volumes corresponding to different recovery rates for culture method, digital polymerase chain reaction and quantitative PCR methods. The results indicate that for children, norovirus required the largest sample volumes of freshwater and marine water (52.08 to 178.57 L, based on the 5th percentile with a 10 % recovery rate), reflecting its low critical concentration and high potential for causing illness. In contrast, adenovirus and rotavirus required significantly smaller volumes (approximately 0.24 to 1.33 L). C. jejuni and Cryptosporidium, which required the highest sampling volumes for bacteria and protozoa, needed 1.72 to 11.09 L and 4.17 to 25.51 L, respectively. Additionally, the presented risk-based framework could provide a model for establishing pathogen thresholds, potentially guiding the creation of extensive risk-based criteria for various pathogens in recreational waters, thus aiding public health authorities in decision-making, strengthening pathogen monitoring, and improving water quality testing accuracy for enhanced health protection.

Challenges for novel antiretroviral development in an era of widespread TLD availability.

Over 80% of people living with HIV in low-and-middle-income countries (LMICs) take first-line TDF/XTC/DTG (TLD). Due to hard-fought activism, in >100 LMICs TLD now costs under $45pppy under Voluntary License. With final DTG patents expiring by 2029, generic TLD will soon be available globally. We identify seven critical benchmarks underpinning TLDs success which novel ART should now meet, and an eighth for which novel ART should aim. These are superior efficacy; a high genetic barrier to resistance; safety in hepatitis B coinfection; favourable drug-drug interaction profiles including with antimycobacterials; efficacy in HIV-2; safety in pregnancy, long-acting formulation availability and affordable pricing from the outset. We illustrate when generic TLD will become available worldwide and compare this with trial programmes and approval timelines for two case-study novel ART combinations: islatravir/doravirine and cabotegravir/rilpivirine. We demonstrate that currently these regimens and trial programmes will not meet key benchmarks required to compete with TLD.

Impact of Anti-Dumping on Chinese Exports to Countries in ASEAN Plus 6

ABSTRACT Concerns about China’s excessive production capacity prompt a closer examination of Anti-dumping Duties (ADs) as a tool for managing trade imbalances. Within the ASEAN region, ASEAN plus 6 countries, especially India, are emerging as alternatives to China in global value chains. These countries import a broad range of products from China, including intermediate goods, making it vital to analyze the effects of ADs on these imports. Using the Poisson Pseudo-Maximum Likelihood (PPML) method, which effectively handles zero trade flows, heteroskedasticity, and endogeneity, this study explores the use of ADs beyond traditional major economies like the US and EU, focusing on their adoption by less economically developed countries. The findings show that ADs significantly curtail Chinese exports, potentially hindering stronger cooperation within ASEAN plus 6 and slowing regional trade dynamics. This research serves as a critical benchmark for assessing China’s strategic role in global economic leadership, viewed with reservations by several countries, including the US. It emphasizes the need to further explore China’s interaction with ASEAN plus 6 partner countries, particularly concerning trade remedy such as ADs and their impact on regional integration.

Time history response prediction of stochastic SDOF structures through Kriging-NARX modeling and adaptive real-time hybrid simulation

Emulating time history response is a critical benchmark for evaluating the seismic resilience of structures and assessing their reliability in the face of uncertainties. Traditional computational simulations, while commonly favored, often fall short in accurately describing the behavior of intricate structural components. Real-time hybrid simulation (RTHS) offers an effective and efficient experimental technique for replicating the responses of deterministic systems in a cyber-physical manner but encounters challenges when dealing with stochastic systems. This study proposes an innovative application of RTHS for time history response prediction of nonlinear stochastic structures. A cross-validation (CV)-Voronoi sampling strategy is applied to sequentially select uncertain parameter samples for RTHS. Nonlinear autoregressive with exogenous input (NARX) model is identified and updated from RTHS results in a sequential manner. Coefficients of final NARX model are surrogated using the Kriging meta-models to account for given uncertainties. Laboratory tests on single-degree-of-freedom (SDOF) structures with self-centering viscous dampers (SC-VD) were conducted to demonstrate the effectiveness of the proposed approach. Predicted time history responses from the final Kriging-NARX model are further compared and validated. It is demonstrated that the proposed approach provides an effective and efficient method for time history response prediction of stochastic systems especially when accurate numerical models are not available.

Compound database for gaseous metal hydroxides and oxyhydroxides

In this study computational methods are used to derive thermochemical data for Sc, Fe, Co, Ni, and Hf hydroxides and oxyhydroxides. As done previously, molecular geometries and vibrational modes were derived with DFT methods; for the enthalpies of formation more computationally intensive coupled cluster methods were necessary. For each species ΔfHo(298), So(298), and Cp in the form A + BT + CT2 + D/T + E/T2 with A, B, C, D, and E fitted constants are presented. These are combined with previously reported calculations for Al, Cr, Si, Ta, Al, Zr, Y, Yb, Gd, and Mn to build a compound database for metal hydroxides and oxyhydroxides. Sample calculations for applications where high temperature water vapor is encountered are shown. The majority of the database was generated from ab initio calculations; however, experiments were critical benchmarks for many of the species.

Assessing the role of dental insurance in oral health care disparities in Canadian adults.

This study examines the association of dental insurance with oral health care access and utilization in Canada while accounting for income and sociodemographic factors. It contributes to a baseline of oral health care disparities before the implementation of the Canadian Dental Care Plan (CDCP). This retrospective study of Canadians aged 18 to 64 years is based on data from the 2022 Canadian Community Health Survey. Multivariable logistic regression was employed to evaluate the association of dental insurance with the recency and frequency of dental visits, as well as avoidance of dental care because of cost. Overall, 65.7% of Canadians reported visiting a dental professional in the previous year: 74.6% of those with private insurance, 62.8% with public insurance, and 49.8% uninsured. Cost-related avoidance of dental care was 16.0%, 20.9%, and 47.4% for the privately insured, publicly insured, and uninsured, respectively. After adjustment, adults with private (odds ratio [OR]=2.54; 95% confidence interval [CI]: 2.32 to 2.78) and public (OR=2.17; 95% CI: 1.75 to 2.68) insurance were more likely to have visited a dental professional in the last year compared with those without insurance. Similarly, both private (OR=0.22; 95% CI: 0.20 to 0.25) and public (OR=0.22; 95% CI: 0.17 to 0.29) insurance holders showed a significantly lower likelihood of avoiding dental visits because of cost when compared with uninsured individuals. This study showed the significant association of dental insurance with access to oral health care in Canada, contributing to setting a critical benchmark for assessments of the CDCP's effectiveness in addressing oral health disparities.

Uncertainty quantification based on similarity analysis of reactor physics benchmark experiments for SFR using TRU metallic fuel

One of the issues in the development of the sodium-cooled fast reactor (SFR) using transuranic (TRU) metallic fuel is the absence of criticality benchmark experiment that faithfully mocks up the nuclear characteristics of the target design for validation of the reactor core design code and its uncertainty quantification (UQ). This study aims to quantify the criticality uncertainty of a typical TRU burner with metallic fuel by using the standard upper safety limit (USL) estimation framework based on the similarity analysis of existing benchmark experiments but elaborated in two aspects:1) application of two-sided rather than one-sided tolerance interval and 2) inclusion of additional uncertainty to account for fission products and minor actinides not included in the benchmark experiments. To conduct the similarity analysis and evaluate the nuclear-data induced uncertainty, existing, well-verified computing codes were integrated, including the nuclear data sampling code SANDY, the nuclear data processing code NJOY, and the continuous-energy Monte Carlo code McCARD. Finally, using the SFR benchmark database comprising both publicly available and proprietary benchmark experiments, the criticality uncertainty of the TRU core model with metallic fuel was evaluated.

Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications

AbstractThe growing demand for applications based on Generative Adversarial Networks (GANs) has prompted substantial study and analysis in a variety of fields. GAN models have applications in NLP, architectural design, text-to-image, image-to-image, 3D object production, audio-to-image, and prediction. This technique is an important tool for both production and prediction, notably in identifying falsely created pictures, particularly in the context of face forgeries, to ensure visual integrity and security. GANs are critical in determining visual credibility in social media by identifying and assessing forgeries. As the field progresses, a variety of GAN variations arise, along with the development of diverse assessment techniques for assessing model efficacy and scope. The article provides a complete and exhaustive overview of the most recent advances in GAN model designs, the efficacy and breadth of GAN variations, GAN limits and potential solutions, and the blooming ecosystem of upcoming GAN tool domains. Additionally, it investigates key measures like as Inception Score (IS) and Fréchet Inception Distance (FID) as critical benchmarks for improving GAN performance in contrast to existing approaches.

Resolved resonance region evaluations of n+ 206,207,208Pb for fast spectrum applications

Resolved resonance region evaluations of the major isotopes of natural lead, 206Pb, 207Pb, 208Pb, have been performed to support the development of Generation IV reactors. Validation of nuclear data for lead fast reactors was performed with simulations of shielding benchmarks, integral critical benchmarks, and quasi-differential scattering measurements. Sensitivity analyses of these systems showed that elastic scattering reactions above 100 keV were the dominant reactions driving system performance. The resolved resonance regions (RRRs) of the lead isotopes extend past 100 keV, making the RRR an ideal starting point to evaluate lead cross sections. Since the R-matrix requires knowledge of bound, distant, and observed resonances, it was necessary to evaluate from 10−5eV up to the respective limit of the RRR. The 208Pb RRR evaluation was extended to 1.5 MeV in order to obtain resonance parameters used to calculate new elastic scattering angular distributions up to 1.5 MeV. Resonance parameter uncertainties and covariance were generated using the R-matrix code SAMMY. The new RRR parameters show a direct improvement to the scattering kernel below 1.5 MeV which in turn greatly improves fast critical experiments over ENDF/B-VIII.0.

The Super-Critical Operational Modes in Robotic Systems

Amidst the swift advancements in robotics, the emergence of super-critical operational modes stands distinct, marking conditions that transcend conventional critical benchmarks. Such modes subject robotic systems to unparalleled strains, necessitating unmatched reliability, flexibility, and robustness. This paper probes into the intricacies of super-critical operations, underscoring the imperative for state-of-the-art sensors, heightened adaptability, resilience post-mishaps, intrinsic redundancy, unceasing surveillance, exhaustive pre-launch examinations, and all-encompassing operator education. Tending to these elements is indispensable for preserving the safety and efficacy of robotic systems under intense conditions, thereby fortifying both the apparatus and their associated industries.

The effect of school size and class size on school preparedness.

The purpose of the present study was to understand students' school readiness as a function of student and teacher behaviors but also school size and class size using both linear and non-linear analytical approaches. Data came from 21,903 schools distributed across 80 countries as per the 2018 cohort of the PISA database. Results pointed to a preference for the Cusp model in that the relationship between school and class sizes with achievement proved to be best described by the non-linearity of the Cusp catastrophe model. The critical benchmarks were a school size of 801 students and a class size of 27 students for which increases beyond those thresholds were linked to nonlinearity and unpredictability in school readiness. For this reason, we suggest using the cusp catastrophe model from Nonlinear Dynamical Systems Theory (NDST) to understand more fully such complex phenomena.

Neutron spectra from photonuclear reactions: Performance testing of Monte-Carlo particle transport simulation codes

The production of neutrons in photon-induced nuclear reactions in the giant-dipole-resonance energy domain remains a topic of high interest for various applications, including the activation and decommissioning of electron accelerator facilities, the detection of illicit materials for homeland security, and the evaluation of neutron dose received by patients during radiotherapy treatments. General-purpose Monte-Carlo (MC) simulation codes for particle transport are intensively used to account for photoneutron production in these applications. However, due to the current scarcity of measured photoneutron energy spectra in the literature, experimental validation of MC-simulated photoneutron energy distributions is not always feasible. Therefore, a critical benchmark among simulation results from various MC codes presently appears as the only option to systematically assess their capabilities in accurately simulating photoneutron production for nuclear reactions of interest. In this work, neutron energy spectra from several targets under irradiation by 20 MeV photons are simulated, employing various state-of-the-art MC codes (FLUKA, Geant4, MCNP6, and PHITS) in their default or generally employed settings. A detailed analysis of the simulated neutron spectra allows one to not only assess the performance of various MC codes in applications such as those mentioned above, but also to partially gauge the incurred systematic uncertainty, and to highlight the present need for more comprehensive evaluated nuclear data in this domain. Ultimately, this work suggests that more prudence is required when using MC codes for applications where photonuclear reactions play a dominant role and where not only the production rate but also the energy spectrum of the emitted neutrons matters.