Limited Set Research Articles

Double Marginalization, Market Foreclosure, and Vertical Integration

Abstract Double marginalization is a robust phenomenon in procurement under asymmetric information when sophisticated contracts can be implemented. In this context, vertical integration causes merger-specific elimination of double marginalization but biases the make-or-buy decision against independent suppliers. If the buyer has full bargaining power over prices and quantities, a vertical merger benefits final consumers even when it results in the exclusion of efficient suppliers. If on the contrary the buyer’s bargaining power is reduced after she has committed to deal exclusively with a limited set of suppliers, exclusion of efficient suppliers may harm final consumers.

A centrality analysis of the Lightning Network

Blockchain technology has a huge impact on our digital society by enabling a more decentralized economy and policy making. This decentralization is also pivotal in payment Payment channel networks (PCNs), including the Lightning Network, have emerged as a promising solution to the scalability challenges that many blockchain-based cryptocurrencies, like Bitcoin, grapple with. These PCNs, while innovative, also inherit the rigorous dependability demands of the blockchain. A pivotal aspect of this dependability is the need for a high degree of decentralization, essential for mitigating liquidity bottlenecks and on-path attacks.Driven by this imperative, our research embarks on an empirical centrality analysis of the Lightning Network, with a keen focus on the betweenness centrality distribution of its routing system. Utilizing an extensive dataset, sourced from several millions of broadcasted messages via the gossip protocol, we introduce the TimeMachine tool, an innovative method that allows for a temporal exploration of the network’s evolution.Our findings reveal that while the Lightning Network exhibits a commendable level of decentralization, there is a discernible skew: a limited set of nodes command a significant portion of the transactions. Alarmingly, over the past two years, the network’s centrality has surged, with the inequality, as gauged by the Gini index, rising by over 15 uptick of approximately 5 in. This research not only uncovers critical insights into the Lightning Network’s structural dynamics but also raises the question about strategies and policies that ensure its sustained decentralization in the face of evolving challenges such as security vulnerabilities, potential monopolistic tendencies, liquidity bottlenecks, the risk of transaction censorship and many more.

Constructivist and well-being based justifications of human rights. Rivals or allies?

ABSTRACT Scholars disagree about the proper justification of human rights and which rights qualify as human rights. While some argue for a very limited set of human rights, others defend more comprehensive accounts. In this paper I suggest that a defence of a comprehensive set of human rights can be strengthened by combining constructivist deontological and well-being based teleological justifications. To this end, I discuss two prominent proponents of constructivism and the well-being approach: Rainer Forst and Simon Caney. Forst criticises teleological justifications for grounding human rights in non-universalizable conceptions of the good, such as autonomy, that can reasonably be rejected by others. Constructivism, in contrast, does not rest on any particular idea of the good life and thus allows for greater ethical plurality. I reject this specific criticism, arguing that Caney’s approach ultimately allows for the same plurality. I also highlight that constructivism on its own cannot justify rights of people who are not (yet) able to reason. The well-being approach provides substantive arguments for the rights of these individuals. However, these arguments are made with the framework provided by constructivism. I hold, contra Caney, that Forst’s right to justification is primary in that it is the unavoidable basis from which justice issues can be dealt with in a reasonable way and that the main function of human rights is to protect the equal moral status of persons. I conclude that Forst’s constructivist and Caney’s well-being approach are complementary rather than rivalrous: when combined they provide a more robust justification for a comprehensive set of human rights than considered separately.

Public preference on sharing health data to inform research, health policy and clinical practice in Australia: A stated preference experiment.

To investigate public willingness to share sensitive health information for research, health policy and clinical practice. A total of 1,003 Australian respondents answered an online, attribute-driven, survey in which participants were asked to accept or reject hypothetical choice sets based on a willingness to share their health data for research and frontline-medical support as part of an integrated health system. The survey consisted of 5 attributes: Stakeholder access for analysis (Analysing group); Type of information collected; Purpose of data collection; Information governance; and Anticipated benefit; the results of which were analysed using logistic regression. When asked about their preference for sharing their health data, respondents had no preference between data collection for the purposes of clinical practice, health policy or research, with a slight preference for having government organisations manage, govern and curate the integrated datasets from which the analysis was being conducted. The least preferred option was for personal health records to be integrated with insurance records or for their data collected by privately owned corporate organisations. Individuals preferred their data to be analysed by a public healthcare provider or government staff and expressed a dislike for any private company involvement. The findings from this study suggest that Australian consumers prefer to share their health data when there is government oversight, and have concerns about sharing their anonymised health data for clinical practice, health policy or research purposes unless clarity is provided pertaining to its intended purpose, limitations of use and restrictions to access. Similar findings have been observed in the limited set of existing international studies utilising a stated preference approach. Evident from this study, and supported by national and international research, is that the establishment and preservation of a social license for data linkage in health research will require routine public engagement as a result of continuously evolving technological advancements and fluctuating risk tolerance. Without more work to understand and address stakeholder concerns, consumers risk being reluctant to participate in data-sharing and linkage programmes.

Cryptocurrency as an alternative inflation hedge?

AbstractWe examine the association of Bitcoin, and other cryptocurrency, returns with changes in inflation expectations, and form a comparison with gold, a traditional inflation hedge. We control for uncertainty in economic policy, cryptocurrency, and financial markets, and show that cryptocurrency returns are positively related to changes in US inflation expectations only for a limited set of circumstances. Unlike with gold, the identified relationship is only significant for short‐term inflation expectations, and when inflation or market‐implied inflation expectations are below 2% (the Fed's inflation target). Moreover, cryptocurrency returns tend to be lower on days with monthly consumer price index (CPI) announcements and respond negatively to CPI surprises. Our results suggest that cryptocurrencies do not currently offer investors a viable alternative to gold for hedging inflation.

Efficient denoising of cold atom images using the optimized eigenface recognition algorithm.

Absorption imaging is a widely employed technique for detecting cold atom clouds and Bose-Einstein condensates (BECs). There are situations where such images may suffer from unwanted interference fringes, resulting in uncertainties in determining crucial parameters such as the atom number, temperatures, or even dynamics in small timescales. Reducing the acoustic vibrations and recording image frames synchronized with the source of such vibrations can largely reduce these fringes; however, some residual fringes still need to be taken care of for precision measurements. In this study, we propose an efficient image post-processing technique for noise reduction that effectively mitigates such interference patterns. Our approach makes use of the well-known eigenface recognition algorithm, combined with an optimized masking strategy applied to the image of the atomic cloud using a small number of basis sets. The use of a limited basis set ensures minimal computational time, allowing this method to be readily incorporated into every experimental run. Through the application of our technique, we successfully reduce interference fringes and improve the accuracy of parameter estimation by 50% in the absorption imaging of cold atoms. The temperature uncertainties of cold 87 R b atoms are reduced by more than 50% after the algorithm is applied. This approach holds significant promise for enhancing the reliability and precision of experimental measurements in diverse research fields where absorption imaging is employed.

The 2022 world health organization reevaluation of human and mammalian toxic equivalency factors for polychlorinated dioxins, dibenzofurans and biphenyls

In October 2022, the World Health Organization (WHO) convened an expert panel in Lisbon, Portugal in which the 2005 WHO TEFs for chlorinated dioxin-like compounds were reevaluated. In contrast to earlier panels that employed expert judgement and consensus-based assignment of TEF values, the present effort employed an update to the 2006 REP database, a consensus-based weighting scheme, a Bayesian dose response modeling and meta-analysis to derive “Best-Estimate” TEFs. The updated database contains almost double the number of datasets from the earlier version and includes metadata that informs the weighting scheme. The Bayesian analysis of this dataset results in an unbiased quantitative assessment of the congener-specific potencies with uncertainty estimates. The “Best-Estimate” TEF derived from the model was used to assign 2022 WHO-TEFs for almost all congeners and these values were not rounded to half-logs as was done previously. The exception was for the mono-ortho PCBs, for which the panel agreed to retain their 2005 WHO-TEFs due to limited and heterogenous data available for these compounds. Applying these new TEFs to a limited set of dioxin-like chemical concentrations measured in human milk and seafood indicates that the total toxic equivalents will tend to be lower than when using the 2005 TEFs.

Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide

The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.

Femoral Occlusion during Neonatal Cardiopulmonary Resuscitation Improves Outcomes in an Ovine Model of Perinatal Cardiac Arrest.

The goal of chest compressions during neonatal resuscitation is to increase cerebral and coronary blood flow leading to the return of spontaneous circulation (ROSC). During chest compressions, bilateral femoral occlusion may increase afterload and promote carotid and coronary flow, an effect similar to epinephrine. Our objectives were to determine the impact of bilateral femoral occlusion during chest compressions on the incidence and timing of ROSC and hemodynamics. In this randomized study, 19 term fetal lambs in cardiac arrest were resuscitated based on the Neonatal Resuscitation Program guidelines and randomized into two groups: femoral occlusion or controls. Bilateral femoral arteries were occluded by applying pressure using two fingers during chest compressions. Seventy percent (7/10) of the lambs in the femoral occlusion group achieved ROSC in 5 ± 2 min and three lambs (30%) did not receive epinephrine. ROSC was achieved in 44% (4/9) of the controls in 13 ± 6 min and all lambs received epinephrine. The femoral occlusion group had higher diastolic blood pressures, carotid and coronary blood flow. Femoral occlusion resulted in faster and higher incidence of ROSC, most likely due to attaining increased diastolic pressures, coronary and carotid flow. This is a low-tech intervention that can be easily adapted in resource limited settings, with the potential to improve survival and neurodevelopmental outcomes.

Measures of Information Leakage for Incomplete Statistical Information: Application to a Binary Privacy Mechanism

Information leakage is usually defined as the logarithmic increment in the adversary’s probability of correctly guessing the legitimate user’s private data or some arbitrary function of the private data when presented with the legitimate user’s publicly disclosed information. However, this definition of information leakage implicitly assumes that both the privacy mechanism and the prior probability of the original data are entirely known to the attacker. In reality, the assumption of complete knowledge of the privacy mechanism for an attacker is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which they can access the corresponding un-distorted data. In this scenario, the conventional definition of leakage no longer has an operational meaning. To address this problem, in this article, we propose novel meaningful information-theoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism—we call them average subjective leakage , average confidence boost , and average objective leakage , respectively. For the simplest, binary scenario, we demonstrate how to find an optimized privacy mechanism that minimizes the worst-case value of either of these leakages.

Battery Voltage Prediction Technology Using Machine Learning Model with High Extrapolation Accuracy

Battery performance prediction techniques based on machine learning (ML) models and lithium-ion battery (LIB) data collected in the real world have received much attention recently. However, poor extrapolation accuracy is a major challenge for ML models using real-world data, as the data frequency distribution can be uneven. Here, we have investigated the extrapolation accuracy of the ML models by using artificial data generated with an electrochemical simulation model. Specifically, we set a lower open circuit voltage (OCV) limit for the training data and generated data limited to the higher state of charge (SOC) region to train the voltage prediction model. We have validated the root mean squared error (RMSE) of the voltage for the test data at several lower OCV limit settings and defined the average + 3 standard deviations of them as an evaluation metric. Eight representative ML models were evaluated, and it was found that the multilayer perceptron (MLP) showed an accuracy of 92.7 mV, which was the best extrapolation accuracy. We also evaluated models with published experimental data and found that the MLP had an accuracy of 102.4 mV, reconfirming that it had the best extrapolation accuracy. We also found that MLP was robust to changes in the data of interest since the accuracy degradation when changing from simulation to experimental data was as small as a factor of 1.1. This result shows that MLP can achieve higher voltage prediction accuracy even when collecting data for comprehensive SOC conditions is difficult.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort.

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Experimental validation and characterization of putative targets of Escargot and STAT, two master regulators of the intestinal stem cells in Drosophila melanogaster

Many organs contain adult stem cells (ASCs) to replace cells due to damage, disease, or normal tissue turnover. ASCs can divide asymmetrically, giving rise to a new copy of themselves (self-renewal) and a sister that commits to a specific cell type (differentiation). Decades of research have led to the identification of pleiotropic genes whose loss or gain of function affect diverse aspects of normal ASC biology. Genome-wide screens of these so-called genetic “master regulator” (MR) genes, have pointed to hundreds of putative targets that could serve as their downstream effectors. Here, we experimentally validate and characterize the regulation of several putative targets of Escargot (Esg) and the Signal Transducer and Activator of Transcription (Stat92E, a.k.a. STAT), two known MRs in Drosophila intestinal stem cells (ISCs). Our results indicate that regardless of bioinformatic predictions, most experimentally validated targets show a profile of gene expression that is consistent with co-regulation by both Esg and STAT, fitting a rather limited set of co-regulatory modalities. A bioinformatic analysis of proximal regulatory sequences in specific subsets of co-regulated targets identified additional transcription factors that might cooperate with Esg and STAT in modulating their transcription. Lastly, in vivo manipulations of validated targets rarely phenocopied the effects of manipulating Esg and STAT, suggesting the existence of complex genetic interactions among downstream targets of these two MR genes during ISC homeostasis.

On the quality of training artificial neural networks without initial data preprocessing under conditions of their limited set

The task of training artificial neural networks (ANNs) for the diagnosis of Alzheimer's disease based on the magnetic susceptibility of 10 brain veins under conditions of limited initial data (81 patients) is considered. In a recent study, fully-connected ANNs with one and two hidden layers were successfully trained in a recent study after reducing the number of veins to 4 and determining the number of synaptic connections comparable to a neural network based on a decision root. The known results gave rise to the hypothesis that training ANNs on the same data without preprocessing would also yield high-quality neural networks. To assess the quality of ANNs, the following indicators were used: mean square errors of training and generalization (testing), coefficients of determination (R2) on the sets of values used in training and generalization (testing), as well as the number of trained ANNs satisfying a certain value of the above indicators. For graphical representation of the results of repeated experiments on training and testing of ANNs, a new graph is proposed to visualize the quality of ANNs. Thus, the paper demonstrates the quality indicators of 800 ANNs trained without data preprocessing, among which there were 7 neural networks with one hidden layer and 9 neural networks with two hidden layers, for each of which 50 experiments were conducted. For comparison, we present the quality indices of 2400 ANNs trained with preprocessing.

Prevalence and associated factors of structural congenital anomalies in resource limited setting, 2023: a systematic review and meta-analysis.

Several studies have been conducted on structural congenital anomalies (CA). However, there is a paucity of studies that provide a comprehensive review of structural anomalies. We aimed to verify the available research articles to pool the possible risk factors of structural CA in resource-limited settings. The research articles were genuinely searched using PubMed, Scopus, Cochrane Library, Web of Science, free Google database search engines, Google Scholar, and ScienceDirect databases. Published studies were searched and screened for inclusion in the final analysis, and studies without sound methodologies and review and meta-analysis were not included in the analysis. This review analyzed data from 95,755 women who gave birth as reported by primary studies. Ten articles were included in this systematic review and meta-analysis. The articles that had incomplete information and case reports were excluded from the study. The overall pooled effect estimate (EI) of structural CA was 5.50 (4.88-6.12) per 100 births. In this systematic review and meta-analysis, maternal illness EI with odds ratio (OR) = 4.93 (95% CI: 1.02-8.85), unidentified drug use with OR = 2.83 (95% CI: 1.19-4.46), birth weight with OR = 4.20 (95% CI: 2.12-6.28), chewing chat with OR = 3.73 (95% CI: 1.20-6.30), chemical exposure with OR = 4.27 (95% CI: 1.19-8.44), and taking folic acid tablet during pregnancy with OR = 6.01 (95% CI: 2.87-14.89) were statistically significant in this meta-regression. The overall pooled effect estimate of structural CA in a resource-limited setting was high compared to that in countries with better resources. Maternal illness, unidentified drug use, birth weight, chewing chat, chemical exposure, and never using folic acid were found to be statistically significant variables in the meta-regression. Preconception care and adequate intake of folic acid before and during early pregnancy should be advised. https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022384838.

Exploring the three-dimensional space with modular concrete shells: Form-finding, design and structural analysis

Thin concrete shells can span large distances thanks to their structural efficiency, following the optimum force flow. Despite the efficient use of material of well-designed shells, their construction suffers from labor-intensive and time-consuming fabrication techniques. This research therefore proposes a design method for the facilitated manufacturing of freeform shell structures. The manufacturing process has been considered from the design phase on, by generating an extensive range of different shell configurations based on a limited set of form-found modules and thus formworks. The challenge was to define which curved module geometries, after assembly, lead to the largest variety of freeform surfaces. More than sixty configurations resulted from a set of four modules only. This paper will discuss the form-finding process as well as the geometrical arrangement using tessellation patterns for the achievement of those modular configurations, going from domed to undulating shell shapes. In order to evaluate the structural efficiency of the shapes, twenty modular configurations were selected and analyzed under self-weight, using finite element simulations. To conclude, a prototype of one modular base element has been built, using a reusable casting wax in combination with a grid shell box, as the main parts of the formwork. The results demonstrate the structural feasibility and limitations of the modular shell design strategy. The modular approach contributes to circularity by encouraging reuse and allowing various combinations with a repeating set of modules, striving as such towards resource-effective innovative shell structures.

Fuzzy-Based Human Health Risk Assessment for Shallow Groundwater Well Users in Arid Regions

The conventional point-estimate human health risk assessment (HHRA) primarily uses average concentrations of a limited number of samples due to the high monitoring costs of heavy metals in groundwater. The results can be erroneous when concentrations significantly deviate from the average across the collected samples in an investigation region. The present research developed a hierarchical fuzzy-based HHRA (F-HHRA) framework to handle variations in limited data sets and subjectively established a broader range of risks for various exposure groups. Groundwater samples from 80 to 120 m deep in shallow wells were collected from agricultural farms along Wadi Rumah in the Qassim Region of Saudi Arabia. Laboratory testing found total dissolved solids much higher than the promulgated drinking water quality standards. As the aftertaste issue eliminated the raw water potability, the study considered dermal exposure for HHRA. The collected samples were tested for thirteen potential heavy metals (HMs), including barium (Ba), boron (B), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), lithium (Li), manganese (Mn), silver (Ag), strontium (Sr), thallium (TI), and zinc (Zn). Cu, Fe, Pb, Ag, and TI were lower than the detectable limit of the inductively coupled plasma mass spectrometry device. Concentrations of the remaining HMs in wastewater outfalls that were much less than the groundwater eradicated the impact of anthropogenic activities and affirmed natural contamination. Apart from 10% of the samples for Mn and 90% of the samples for Sr, all the other HMs remained within the desired maximum allowable concentrations. Point-estimate and fuzzy-based approaches yielded ‘low’ dermal non-cancer risk and cancer risk for all groups other than adults, where dermal cancer risk of Cr remained in the ‘acceptable’ (1 × 10−6 and 1 × 10−5) risk zone. Although dermal risk does not require controls, scenario analysis established the rationality of F-HHRA for more contaminated samples. The proposed hierarchical F-HHRA framework will facilitate the decision-makers in concerned agencies to plan risk mitigation strategies (household level and decentralized systems) for shallow well consumers in Saudi Arabia and other arid regions.

Main belt asteroids taxonomical information from dark energy survey data

ABSTRACT While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.

Anosov representations with Lipschitz limit set

Anosov representations with Lipschitz limit set

The Validity of the ROX Index and APACHE II in Predicting Early, Late, and Non-Responses to Non-Invasive Ventilation in Patients with COVID-19 in a Low-Resource Setting.

The use of the Ratio of Oxygen Saturation (ROX) index to predict the success of high-flow nasal oxygenation (HFNO) is well established. The ROX can also predict the need for intubation, mortality, and is easier to calculate compared with APACHE II. In this prospective study, the primary aim is to compare the ROX (easily administered in resource limited setting) to APACHE II for clinically relevant outcomes such as mortality and the need for intubation. Our secondary aim was to identify thresholds for the ROX index in predicting outcomes such as the length of ICU stay and failure of non-invasive respiratory support therapies and to assess the effectiveness of using the ROX (day 1 at admission, day 2, and day 3) versus Acute physiology and chronic health evaluation (APACHE) II scores (at admission) in patients with Coronavirus Disease 2019 (COVID-19) pneumonia and Acute Respiratory Distress Syndrome (ARDS) to predict early, late, and non-responders. After screening 208 intensive care unit patients, a total of 118 COVID-19 patients were enrolled, who were categorized into early (n = 38), late (n = 34), and non-responders (n = 46). Multinomial logistic regression, receiver operating characteristic (ROC), Multivariate Cox regression, and Kaplan-Meier analysis were conducted. Multinomial logistic regressions between late and early responders and between non- and early responders were associated with reduced risk of treatment failures. ROC analysis for early vs. late responders showed that APACHE II on admission had the largest area under the curve (0.847), followed by the ROX index on admission (0.843). For responders vs. non-responders, we found that the ROX index on admission had a slightly better AUC than APACHE II on admission (0.759 vs. 0.751). A higher ROX index on admission [HR (95% CI): 0.29 (0.13-0.52)] and on day 2 [HR (95% CI): 0.55 (0.34-0.89)] were associated with a reduced risk of treatment failure. The ROX index can be used as an independent predictor of early response and mortality outcomes to HFNO and NIV in COVID-19 pneumonia, especially in low-resource settings, and is non-inferior to APACHE II.