Critical Slowing-down Research Articles

From Phonons to Domain Walls, the Central Peak and “Critical Slowing Down”

We investigate perovskite oxides from different perspectives, namely their pseudo-harmonic dynamical properties, their dynamical properties when strong anharmonicity exists, and the intriguing functionalities arising from domain walls. Taking these viewpoints together yields a rather complex picture of this material class, which has not been found in previous approaches. It opens pathways to novel applications and reveals the rich ground states beyond the fictitious belief in the ‘simplicity of perovskites and such structures’.

The Accelerating Loss of Resilience in Suburban Woodlands Can Largely Be Attributed to the Changes in Urban Precipitation Patterns.

Vegetation resilience holds significant importance for stabilizing ecosystem service functions in a changing climate. While global land surface vegetation resilience changes have been extensively studied, the impact of urbanization on the resilience of suburban woodlands remains inadequately understood. In this study, we utilized two critical slowing down (CSD) indicators, namely lag-one autocorrelation (LOA) and variance (VA), to assess the vegetation resilience, its long-term trends, and influencing factors in suburban woodlands across 1356 cities worldwide. The recovery rates estimated by LOA ( ) and VA ( ) showed close alignment in suburban woodlands with low suburban forest coverage (SFC) areas (correlation coefficient (r) = 0.95). However, a notable divergence was observed in areas with high SFC (r = 0.73). Suburban woodlands with high SFC typically exhibited lower recovery rate estimates, thus indicating greater vegetation resilience compared to areas with lower SFC. From 1986 to 2022, the recovery rates of suburban woodland areas in over 83% of the cities demonstrated a significant upward trend, with an average of 3.23 × 10-3 year-1 for both and , signifying a widespread decline in vegetation resilience. The accelerating pace of urbanization led to higher rising rates of and during 2010-2022 (5.11 × 10-3 year-1) compared to 1986-1999 (0.49 × 10-3 year-1). The notable decrease in resilience of forestland was primarily attributed to reduced precipitation in urban suburbs, which can be explained by urbanization-induced heat island and building barrier effects, causing a shift of precipitation center from urban suburbs to central cities. In summary, this study revealed that urbanization diminishes the vegetation resilience of urban suburban woodlands by altering urban precipitation patterns. These findings underscore the necessity of augmenting water availability in urban suburbs to restore resilience in these woodlands, thereby enhancing their ecosystem service value.

Ecosystem Resilience Trends and Its Influencing Factors in China’s Three-River Headwater Region: A Comprehensive Analysis Using CSD Indicators (1982–2023)

Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the “Water Tower of China”. We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world.

Early warning signals of complex critical transitions in deterministic dynamics

Early Warning Signals (EWS) have generated much excitement for their potential to anticipate transitions in various systems, ranging from climate change in ecology to disease staging in medicine. EWS hold particular promise for bifurcations, a transition mechanism in which a smooth, gradual change in a control parameter of the system results in a rapid change in system dynamics. The predominant reason to expect EWS is because many bifurcations are preceded by Critical Slowing Down (CSD): if assuming the system is subject to continuous, small, Gaussian noise, the system is slower to recover from perturbations closer to the transition. However, this focus on warning signs generated by stochasticity has overshadowed warning signs which may already be found in deterministic dynamics. This is especially true for higher-dimensional systems, where more complex attractors with intrinsic dynamics such as oscillations not only become possible—they are increasingly more likely. The present study focuses on univariate and multivariate EWS in deterministic dynamics to anticipate complex critical transitions, including the period-doubling cascade to chaos, chaos-chaos transitions, and the extinction of a chaotic attractor. In a four-dimensional continuous-time Lotka–Volterra model, EWS perform well for most bifurcations, even with lower data quality. The present study highlights three reasons why EWS may still work in the absence of CSD: changing attractor morphology (size, shape, and location in phase space), shifting power spectra (amplitude and frequency), and chaotic transitional characteristics (density across attractor). More complex attractors call for different warning detection methods to utilise warning signs already contained within purely deterministic dynamics.

Intelligent recognition of coal and gas outburst precursor and potential hazards using YOLOv8 neural network

Fluctuations in gas emission or concentration at working face represent primary indicators of impending coal and gas outbursts, making them essential for monitoring processes. However, the direct use of original signals for predictive purposes may cause false warnings due to the inability to recognize valuable potential information. This can result in decreased prediction accuracy. Additionally, gas emission or concentration is affected by potential hazards such as sensor calibration, borehole spraying, and air duct damage, leading to complexity of their behavior and recognition process. This paper proposes an intelligent recognition method leveraging YOLOv8 neural network to discern coal and gas outburst precursors and potential hazards. By employing continuous wavelet transform (CWT) on the gas concentration signal processed by critical slowing down (CSD) method, a two-dimensional time–frequency representation is generated. This representation is then fed into YOLOv8 model to recognize the outburst precursor characteristics and potential hazards. The research results show that YOLOv8-based intelligent recognition adeptly identifies the relevant precursor characteristics and potential hazards, enhancing both coal mine safety protocols and the accuracy of early outburst warning mechanisms.

Detection of Approaching Critical Transitions in Natural Systems Driven by Red Noise

Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past. Published by the American Physical Society 2024

Predicting the failure of rock using critical slowing down theory on acoustic emission characteristics

The joint surfaces and size affect the mechanical behavior and failure precursors of shale. Therefore, this paper studies the mechanical behavior, the response characteristics of acoustic emission (AE) and critical slowing down (CSD) characteristics of AE for shale with different joint surfaces and size. The results show that with an increase in the number of joint surfaces and the size of the sample, the crack initiation stress, uniaxial compressive strength (UCS), peak strain, and fractal dimension decrease. During the plastic stage, the occurrence of a sharp rise in AE ringing count, RA, a low-frequency, high-amplitude AE signal, and a rapid drop in AF can be characteristics of impending sample failure. Variance and autocorrelation coefficients can be used to characterize the CSD phenomenon. The optimal window length and lag step are determined to be 400 and 100, respectively. The CSD characteristics of the autocorrelation coefficient and the variance of multi-AE parameters are obtained. The variance-based early warning time provides short-term early warning, while the autocorrelation coefficient-based early warning time provides early warning. The research results can provide theoretical references for the mechanical design and failure prediction of underground rock engineering.

The effect of the gelation temperature on the structural, magnetic and magnetocaloric properties of perovskite nanoparticles manufactured using the sol-gel method.

This study presents a comprehensive investigation of the structural and magnetic properties of La0.8Sr0.2Mn0.8Co0.2O3 (LS1, LS2 and LS3) compounds synthesized via the sol-gel method at different gelation temperatures through X-ray diffraction and different magnetic measurement techniques. The Rietveld refinement demonstrated that all samples exhibit a rhombohedral perovskite structure with the R3̄C space group. Their magnetic behavior, characterized through magnetization measurements, hysteresis loops, and Arrot plots, demonstrates a ferromagnetic-paramagnetic transition with notable soft ferromagnetic characteristics. The samples also demonstrate second-order magnetic transitions, short-range magnetic order and the presence of both ferromagnetic and antiferromagnetic contributions. AC magnetic susceptibility measurements, allowing the investigation of the magnetic dynamics of the samples, shows that the Vogel-Fulcher and the Conventional Critical Slowing Down models are the most appropriate for describing the dynamic behavior, confirming the spin-glass nature of the compounds and the presence of medium to strong interaction between magnetic nanoparticles. The influence of gelation temperature in the magnetocaloric effect of the compounds was proven and LS1, synthesized at the lowest gelation temperature (70 °C), exhibits the higher magnetic entropy change (|ΔSmax| = 3.25 J kg-1 K-1 and RCP = 209.83 J kg-1 at 5 T). For a better evaluation of the magnetocaloric efficiency, the temperature average entropy change (TEC) parameter was calculated for all three samples and LS1 showed the highest value (TEC (LS1) ∼3.2 J kg-1 K-1 for ΔTH-C = 10 K and ΔH = 5 T).

Prediction of rock loading stages using average infrared radiation temperature under shear and uniaxial loading

Accurately predicting geologically rock loading phases under varying water levels is crucial to prevent rock engineering operations from severe geological hazards. Acoustic emission (AE) traditionally determines these phases, but machinery in rock projects can disrupt AE sensors, leading to imprecise data and potential accidents. To address this, our study tested a novel approaches using AE, Average Infrared Radiation Temperature (AIRT), Critical Slow Down Theory (CSDT), and Convolutional Neural Networks (CNNs) to predict rock loading phases accurately under different water conditions. The research findings are: (1) AE analysis divides the stress–strain curve into four phases: crack closure, elastic deformation, and stable and unstable crack propagations. Each phase exhibits distinct characteristics in terms of AE signal behavior. (2) AIRT analysis reveals specific patterns during different loading phases. The AIRT and cumulative AIRT have distinct trends in different phases. (3) The AIRT sequence's fractal dimension shows distinct trends during loading phases: it initially rises during the elastic phase and then declines. Fractal dimensions for crack closure, elastic deformation, and stable crack propagation increase, while that for unstable crack propagation decreases with water content. 4) The stress–strain curve phases were successfully predicted by using AIRT and CSDT, demonstrating a strong correlation with actual stress levels. Notably, this marks the first-time prediction of phases through non-destructive tests (IR and CSDT), where CSDT fluctuations at each stage can serve as early indicators of rock failure.5) The proposed CNNR model achieves high accuracy (98%) in predicting different phases of the stress curve using AIRT. It particularly performs well in the elastic deformation and stable crack propagation phases, with only a few instances of false negative predictions. The model shows promising potential for effective and safe geological projects, especially under varying water conditions. The findings contribute to the understanding of rock behavior and offer insights for the design of safer and more efficient rock engineering projects.

Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation

Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary missing-data distributions, biases and uncertainties. Here we show that, together with the pre-processing steps used to deal with them, these can bias the CSD analysis. We present an uncertainty quantification method to address such issues. We show how to propagate uncertainties provided with the datasets to the CSD analysis and develop conservative, surrogate-based significance tests on the CSD indicators. We apply our method to three observational sea-surface temperature and salinity datasets and to fingerprints of the Atlantic Meridional Overturning Circulation derived from them. We find that the properties of these datasets and especially the specific gap filling procedures can in some cases indeed cause false indication of CSD. However, CSD indicators in the North Atlantic are still present and significant when accounting for dataset uncertainties and non-stationary observational coverage.

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Many real-world systems of various origins are capable of self-organization to the edge of a phase transition, characterized by avalanche-like behavior. Therefore, it is important, by observing the behavior of early warning measures for dynamical series generated by systems, to timely see the early warning signals (precursors) of such self-organization and, if necessary, take preventive measures. To date, convincing evidence of self-organization to the edge of a phase transition has been obtained, but no effective precursors for this self-organization have been found. This research explores precursors for the Twitter self-organization based on the analysis of the behavior of measures directly related to the critical slowdown of the network and measures of the phase space reconstructed by the Takens method for the series of the number of network users creating avalanches of retweets in the network, corresponding to the three debates of the 2016 United States Presidential Election. We hydrated the relevant Tweet IDs, which were obtained from the Harvard Dataverse using the Social Feed Manager, to form this series. Preliminarily, we explore the potential of measures for early detection of self-organization of sandpile cellular automata as systems with Twitter-equivalent self-organization mechanisms. The equivalence is justified in the proposed discrete-time model for Twitter self-organization to the edge of a phase transition. It is found that there are more moments of the Twitter self-organization than the moments of time when debates started, and Twitter stays at the edge of a phase transition longer than the debate lasts. The effective measures, as the measures with the lowest number of false early warning signals, among all studied measures and for all studied systems, are dispersion and correlation dimension. Obtained results are practically important in the design and implementation of early warning systems for the systems with similar mechanisms for sandpile cellular automata self-organization to the edge of a phase transition.

Failure precursor characteristics of different types of rocks under load: insights from critical slowing down of acoustic emission

In this study, uniaxial compression tests and simultaneous acoustic emission (AE) monitoring were carried out on four rocks (yellow sandstone, white sandstone, marble and limestone). The mechanical properties and AE energy evolution characteristics of different rocks were analysed. With the help of critical slowing down (CSD) theory, the AE precursor characteristics of their failure were investigated. It is pointed out that the AE during rock loading has a CSD phenomenon. A sudden change in the variance of one of the CSD indicators can be regarded as a precursor to failure, and it has the advantage of being more accurate and sensitive to failure than the autocorrelation coefficient. The stress level of a rock's failure precursor is closely related to its brittleness characteristics. The higher the brittleness of the rock, the more backward the failure precursor is, and the more difficult the early warning is. The study aims to provide new indicators and references for the monitoring and early warning of rockbursts and other disasters induced by rock fracture in deep underground engineering.

Sweeny dynamics for the random-cluster model with small Q.

The Sweeny algorithm for the Q-state random-cluster model in two dimensions is shown to exhibit a rich mixture of critical dynamical scaling behaviors. As Q decreases, the so-called critical speeding-up for nonlocal quantities becomes more and more pronounced. However, for some quantity of a specific local pattern, e.g., the number of half faces on the square lattice, we observe that, as Q→0, the integrated autocorrelation time τ diverges as Q^{-ζ}, with ζ≃1/2, leading to the nonergodicity of the Sweeny method for Q→0. Such Q-dependent critical slowing-down, attributed to the peculiar form of the critical bond weight v=sqrt[Q], can be eliminated by a combination of the Sweeny and the Kawasaki algorithm. Moreover, by classifying the occupied bonds into bridge bonds and backbone bonds, and the empty bonds into internal-perimeter bonds and external-perimeter bonds, one can formulate an improved version of the Sweeny-Kawasaki method such that the autocorrelation time for any quantity is of order O(1).

An investigation of the potential clinical utility of critical slowing down as an early warning sign for recurrence of depression

Background and objectivesMuch of the burden of depressive illness is due to relapses that occur after treatment into remission. Prediction of an individual's imminent depressive relapse could lead to just-in-time interventions to prevent relapse, reducing depression's substantial burden of disability, costs, and suicide risk. Increasingly strong relationships in the form of autocorrelations between depressive symptoms, a signal of a phenomenon described as critical slowing down (CSD), have been proposed as a means of predicting relapse. MethodsIn the current study, four participants in remission from depression, one of whom relapsed, responded to daily smartphone surveys with depression symptoms. We used p-technique factor analysis to identify depression factors from over 100 survey responses. We then tested for the presence of CSD using time-varying vector autoregression and detrended fluctuation analysis. ResultsWe found evidence that CSD provided an early warning sign for depression in the participant who relapsed, but we also detected false positive indications of CSD in participants who did not relapse. Results from time-varying vector autoregression and detrended fluctuation analysis were not in agreement. LimitationsLimitations include use of secondary data and a small number of participants with daily responding to a subset of depression symptoms. ConclusionsCSD provides a compelling framework for predicting depressive relapse and future research should focus on improving detection of early warning signs reliably. Improving early detection methods for depression is clinically significant, as it would allow for the development of just-in-time interventions.

Strain-Controlled Critical Slowing Down in the Rheology of Disordered Networks.

Networks and dense suspensions frequently reside near a boundary between soft (or fluidlike) and rigid (or solidlike) regimes. Transitions between these regimes can be driven by changes in structure, density, or applied stress or strain. In general, near the onset or loss of rigidity in these systems, dissipation-limiting heterogeneous nonaffine rearrangements dominate the macroscopic viscoelastic response, giving rise to diverging relaxation times and power-law rheology. Here, we describe a simple quantitative relationship between nonaffinity and the excess viscosity. We test this nonaffinity-viscosity relationship computationally and demonstrate its rheological consequences in simulations of strained filament networks and dense suspensions. We also predict critical signatures in the rheology of semiflexible and stiff biopolymer networks near the strain stiffening transition.

Progressive damage process and destabilization precursor recognition of sulfate tailing-cemented paste backfill based on acoustic emission

Sulfate-rich tailings are commonly used as aggregate materials for mine filling, and the sulfate retained within them significantly affects the structural characteristics of cemented paste backfill (CPB). A series of microstructure tests, uniaxial compressive strength (UCS) tests, and acoustic emission (AE) monitoring were carried out on CPB and sulfate tailing-cemented paste backfill (ST-CPB) to investigate the progressive damage pattern and identify the destabilization precursor information of the backfill. The results showed that the UCS of the backfill increased and then decreased with the increase in sulfate content, and 1.5% was the optimal content. The AE counts, energy, amplitude, and peak frequency time series corresponded to each other, exhibited the stage characteristics of a stress curve and were significantly affected by sulfate content. CPB and 0.5% ST-CPB displayed more active AE counts and energy during the initial loading stage, with high amplitude and low-frequency signals. However, the 1.5% ST-CPB exhibited active AE events and medium-high amplitude and medium-low frequency signals only during the plastic yielding and damage stages. 2.5% ST-CPB exhibited more active AE signals during the entire loading process. RA-AF values indicate that CPB and 2.5% ST-CPB reached 93.12 and 96.62% of tensile cracks at final failure, respectively. Meanwhile, the percentages of tensile cracks at the final failure of 0.5 and 1.5% ST-CPB were 76.65 and 76.86%, respectively. The sudden increase in the variance curve and the rapid decrease in the b value of AE in the critical slowdown theory were identified as the precursor characteristics of the backfill instability. This investigation can provide technical and theoretical support for the progressive damage characteristic and stability assessment of ST-CPB.

Path Counting on Tree-like Graphs with a Single Entropic Trap: Critical Behavior and Finite Size Effects.

It is known that maximal entropy random walks and partition functions that count long paths on graphs tend to become localized near nodes with a high degree. Here, we revisit the simplest toy model of such a localization: a regular tree of degree p with one special node ("root") that has a degree different from all the others. We present an in-depth study of the path-counting problem precisely at the localization transition. We study paths that start from the root in both infinite trees and finite, locally tree-like regular random graphs (RRGs). For the infinite tree, we prove that the probability distribution function of the endpoints of the path is a step function. The position of the step moves away from the root at a constant velocity v=(p-2)/p. We find the width and asymptotic shape of the distribution in the vicinity of the shock. For a finite RRG, we show that a critical slowdown takes place, and the trajectory length needed to reach the equilibrium distribution is on the order of N instead of logp-1N away from the transition. We calculate the exact values of the equilibrium distribution and relaxation length, as well as the shapes of slowly relaxing modes.

Using the dynamics of productivity and precipitation-use efficiency to detect state transitions in Eurasian grasslands

In the face of accelerated global dryland expansion and grassland degradation, signaling grassland ecosystem state transitions is an ongoing challenge in ecology. However, there is still a lack of effective indicators and understanding of the mechanisms of grassland ecosystem state transitions at the continental scale. Here, we propose a framework that links ecosystem function-based indicators and critical slowing down (CSD) theory to reveal grassland state transitions. Across precipitation gradients, we quantified the statistical characteristics and spatial patterns in ANPP and PUE dynamics (variability, asymmetry, and sensitivity to precipitation and temperature) in Eurasian grasslands. We show that the CVANPP, CVPUE, AANPP, APUE, SPUE-P, and SANPP-P of temperate steppes were significantly higher than those of alpine steppes, while the SPUE-T and SANPP-T were the inverse. In temperate grasslands, AANPP, APUE, and SANPP-P indicated the transition of typical steppes, and CVANPP, APUE, and SPUE-T indicated the transition from meadow to typical steppes. In alpine grasslands, APUE indicated the transition between alpine deserts and alpine steppes, and AANPP and SANPP-P indicated the transition between alpine steppes and meadow steppes. The interannual variability of precipitation strongly affected xerophyte proportion and demographic processes, which control state transitions in low-resilience grasslands. Community structures and limiting factors (nutrient, light, and/or temperature) regulate state transitions in high-resilience grasslands. Our results demonstrate that function-based indicators are predictive of impending state transitions of temperate and alpine grasslands, highlighting the complementation of ANPP and PUE dynamics that have the potential for predicting grassland ecosystem regime shifts and their underlying mechanisms.

Acoustic Emission from Concrete: Critical Slowing Down Analysis and Compressive Strength Prediction

This work pursues two primary aims: identifying the precursory point by the CSD analysis of AE series and using acoustic emission parameters to predict the compressive strength of concrete utilizing the artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). The concrete specimens with a water-cement ratio of 0.45 and 0.55 were tested for compressive strength, and the failure process of concrete was monitored by acoustic emission. The results demonstrate that the advantage of variance was being intuitive, robust, and less affected by the window. The fluctuation and abrupt increase of the variance can be regarded as the critical point and the formation of the main failure surface, which provided warning information for preventing catastrophic failures. The precursory point determined by the variance was 80%–90% of the ultimate strength, consistent with the improved b value (Ib). Then, an approach to predict the compressive strength of concrete was proposed to predict the compressive strength of concrete utilizing the ANN, ELM, and SVM. The results of the ELM were more outstanding than those of the ANN and SVM. The results suggest that it was possible to predict the compressive strength of concrete with a small number of samples using the AE parameters.

Scrutinizing human resources for health availability and distribution in Mozambique between 2016 and 2020: a subnational descriptive longitudinal study

IntroductionOverall, resilient health systems build upon sufficient, qualified, well-distributed, and motivated health workers; however, this precious resource is limited in numbers to meet people’s demands, particularly in LMICs. Understanding the subnational distribution of health workers from different lens is critical to ensure quality healthcare and improving health outcomes.MethodsUsing data from Health Personnel Information System, facility-level Service Availability and Readiness Assessment, and other sources, we performed a district-level longitudinal analysis to assess health workforce density and the ratio of male to female health workers between January 2016 and June 2020 across all districts in Mozambique.Results22 011 health workers were sampled, of whom 10 405 (47.3%) were male. The average age was 35 years (SD: 9.4). Physicians (1025, 4.7%), maternal and child health nurses (4808, 21.8%), and nurses (6402, 29.1%) represented about 55% of the sample. In January 2016, the average district-level workforce density was 75.8 per 100 000 population (95% CI 65.9, 87.1), and was increasing at an annual rate of 8.0% (95% CI 6.00, 9.00) through January 2018. The annual growth rate declined to 3.0% (95% CI 2.00, 4.00) after January 2018. Two provinces, Maputo City and Maputo Province, with 268.3 (95% CI 186.10, 387.00) and 104.6 (95% CI 84.20, 130.00) health workers per 100 000 population, respectively, had the highest workforce density at baseline (2016). There were 3122 community health workers (CHW), of whom 72.8% were male, in January 2016. The average number of CHWs per 10 000 population was 1.33 (95% CI 1.11, 1.59) in 2016 and increased by 18% annually between January 2016 and January 2018. This trend reduced to 11% (95% CI 0.00, 13.00) after January 2018. The sex ratio was twice as high for all provinces in the central and northern regions relative to Maputo Province. Maputo City (OR: 0.34; 95% CI 0.32, 0.34) and Maputo Province (OR: 0.56; 95% CI 0.49, 0.65) reported the lowest sex ratio at the baseline. Encouragingly, important sex ratio improvements were observed after January 2018, particularly in the northern and central regions.ConclusionMozambique made substantial progress in health workers’ availability during the study period; however, with a critical slowdown after 2018. Despite the progress, meaningful shortages and distribution disparities persist.