Nonlinear Change Research Articles

The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth

BackgroundRisk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical (NTP) population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic (AUT) people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms.MethodWe ran a game-like risk-sensitive reinforcement learning task on 75 participants aged 6–30 years (AUT group, n = 31; NTP group, n = 44). Focusing on choices between alternatives with the same objective value but different risks, we calculated the risk preference and stay probability of a risky choice after a rewarding or non-rewarding outcome. Analyses using t-tests and multiple regression analyses were conducted. Using the choice-related data of each participant, we fit four reinforcement learning models and compared the fit of each model to the data. Furthermore, we validated the results of model fitting with multiple methods, model recovery, parameter recovery, and posterior predictive check.ResultsWe found a significant difference in nonlinear developmental changes in risk preference between the AUT and NTP groups. The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences.ConclusionsThese findings indicate that for NTP people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for AUT people, who show opposite developmental change of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference.

General Equation for Expressing the Physicochemical Properties of Aliphatic Alcohols.

In this work, two general equations were proposed to express the nonlinear and linear changes in physicochemical properties of aliphatic alcohols, involving boiling point, refractive index, critical temperature, critical volume, and so on. The two general equations all are expressed with the same six molecular descriptors. The results show that the linear and nonlinear change properties of aliphatic alcohols have good correlations with the same six molecular descriptors. Using the obtained correlation equations, various properties of aliphatic alcohols without experimental values were predicted, involving 15 normal boiling points, 96 refractive indexes, 105 critical temperatures, 109 critical pressures, 100 liquid densities, 136 heat capacities, 107 critical volumes, and 130 enthalpies of the formation of liquid, a total of 798 values. The relationship between the nonlinear and linear change properties can be deduced by using the obtained general equations, which connect different properties of aliphatic alcohols. In addition, this paper combined the general estimation models used in the properties of aliphatic alcohols and aliphatic primary amines, and then, by taking the boiling point and critical temperature as examples, a general estimation model with good correlation and high prediction accuracy was obtained via adding another two molecular structure characteristic parameters. It is a meaningful exploration for establishing general models for monosubstituted alkane RX compounds with different substituents in the future.

Spatiotemporal Nonlinear Characteristics and Threshold Effects of China's Water Resources

China faces shortage of water resources, particularly in the context of rapid population growth and accelerating urbanization, making the changes in its water resources among the most pronounced on Earth. Additionally, the complex interplay between climate change and human activities leads to nonlinear and non-stationary patterns in China's water resources. This study utilizes high-resolution water storage monitoring data to comprehensively analyze the nonlinear changes in water storage and its relationships with human footprint, precipitation, and temperature, revealing the complex dynamics of water storage changes across China. We used advanced data analysis techniques to identify critical points where water storage patterns shifted significantly, with categories of nonlinear changes that first decrease then increase and those that first increase then decrease together accounting for 55.62% of the observations. The northeastern and western border areas of China are hotspots for these nonlinear changes, and the analysis identifies 2019 as a year with a high frequency of turning points. The piecewise linear regression analysis found that when the human footprint exceeds a specific threshold, its negative impact on water storage significantly intensifies; when precipitation and temperature exceed certain thresholds, their impact on water storage shifts from negative to positive. These findings not only reveal the complex spatiotemporal distribution characteristics of water storage change turning points across China but also emphasize that water resource management strategies in China and globally need to adopt more comprehensive and dynamic approaches in the context of global climate change. To meet future challenges, it is essential to integrate multiple variables and develop flexible, adaptive management strategies to ensure the sustainable use and effective management of water resources.

Inventory costing: a multiplicative-based average for crude oil and petroleum products

Crude oil prices have been fluctuating nonlinearly in recent times due to various factors, such as market expectations, supply disruptions, and geopolitical conflicts like the Russia-Ukraine and Israel-Palestine crises. However, conventional methods like weighted averages and moving averages are inadequate for accurately capturing these nonlinear changes especially in crude oil prices. This study, therefore, aims to combine multiplicative calculus with weighted and moving average methods to enable accurate calculation of crude oil inventory costing, particularly during times of financial crisis. A comparative study is conducted to highlight the differences between conventional and multiplicative-based average methods. The findings show that the multiplicative-based average yield more accurate results for cost of goods (crude oil or petroleum products) sold, cost of goods inventoried, and gross margins, especially when the change is nonlinear. This paper, which is the first study to combine weighted and moving averages with a new calculus, is unique in the sense that it contributes to the literature by providing an accurate costing framework to respond to the global financial crises associated with the unprecedented increase in the prices of metals, crude oil, and petroleum products.

Dynamic Changes in Gene Expression Through Aging in Drosophila melanogaster Heads.

Work in many systems has shown large-scale changes in gene expression during aging. However, many studies employ just two, arbitrarily-chosen timepoints at which to measure expression, and can only observe an increase or a decrease in expression between "young" and "old" animals, failing to capture any dynamic, non-linear changes that occur throughout the aging process. We used RNA sequencing to measure expression in male head tissue at 15 timepoints through the lifespan of an inbred Drosophila melanogaster strain. We detected >6,000 significant, age-related genes, nearly all of which have been seen in previous fly aging expression studies, and which include several known to harbor lifespan-altering mutations. We grouped our gene set into 28 clusters via their temporal expression change, observing a diversity of trajectories; some clusters show a linear change over time, while others show more complex, non-linear patterns. Notably, re-analysis of our dataset comparing the earliest and latest timepoints - mimicking a two-timepoint design - revealed fewer differentially-expressed genes (around 4,500). Additionally, those genes exhibiting complex expression trajectories in our multi-timepoint analysis were most impacted in this re-analysis; Their identification, and the inferred change in gene expression with age, was often dependent on the timepoints chosen. Informed by our trajectory-based clusters, we executed a series of gene enrichment analyses, identifying enriched functions/pathways in all clusters, including the commonly seen increase in stress- and immune-related gene expression with age. Finally, we developed a pair of accessible shiny apps to enable exploration of our differential expression and gene enrichment results.

Experimental Evaluation of the Effect of Degradation on the Mechanical Behavior and Morphometric Characteristics of Functionally Graded Polymer Scaffolds.

Bone transplantation ranks second worldwide among tissue prosthesis surgeries. Currently, one of the most promising approaches is regenerative medicine, which involves tissue engineering based on polymer scaffolds with biodegradable properties. Once implanted, scaffolds interact directly with the surrounding tissues and in a fairly aggressive environment, which causes biodegradation of the scaffold material. The aim of this work is to experimentally investigate the changes in the effective mechanical properties of polylactide scaffolds manufactured using additive technologies. The mechanism and the rate of the degradation process depend on the chosen material, contact area, microstructural features, and overall architecture of sample. To assess the influence of each of these factors, solid samples with different dimensions and layers orientation as well as prototypes of functionally graded scaffolds were studied. The research methodology includes the assessment of changes in the mechanical properties of the samples, as well as their structural characteristics. Changes in the mechanical properties were measured in compression tests. Microcomputed tomography (micro-CT) studies were conducted to evaluate changes in the microstructure of scaffold prototypes. Changes caused by surface erosion and their impact on degradation were assessed using morphometric analysis. Nonlinear changes in mechanical properties were observed for both solid samples and lattice graded scaffold prototypes depending on the duration of immersion in NaCl solution and exposure to different temperatures. At the temperature of 37 °C, the decrease in the elastic modulus of solid specimens was no more than 16%, while for the lattice scaffolds, it was only 4%. For expedited degradation during a higher temperature of 45 °C, these ratios were 47% and 16%, respectively. The decrease in compressive strength was no more than 32% for solid specimens and 17% for scaffolds. The results of this study may be useful for the development of optimal scaffolds considering the impact of the degradation process on their structural integrity.

Design and Simulation of a Fuzzy PID Control System for Building Climate

This article discusses that climate control systems are important to maintain comfortable temperatures and clean, and efficient structures in the modern world. An effective system for climate control of buildings including responses in real time is highly beneficial for human comfort and the reliability of equipment. Nevertheless, conventional proportional-integral-derivative (PID) control systems may experience difficulties in addressing the non-linear processes and changing conditions of properties, as a result of which they deliver quite a low level of performance. Thus, this research work presents an adaptation of the conventional control scheme known as the PID model as the Fuzzy PID. The Fuzzy PID system was developed and analyzed through simulation to determine its performance solution in comparison to conventional PID control. The results further reveal that the proposed Fuzzy PID controller enhances the response time, stability, and accuracy by bringing down by about 15% the overshoot and by about 20% the settling time as compared to the basic PID controller. To the existing field of control methodologies in building climate systems, this research helps extend the progress towards better control systems that can bring about improvements in energy consumption and occupant comfort in multiple uses.

Variable Kerr nonlinearity and optical bistability of a four-level lambda atomic medium

Using perturbation theory we obtain expressions for the third-order nonlinear susceptibility and the Kerr nonlinear coefficient of a four-level lambda-type atomic system in the presence of Doppler broadening. The model is applied to the Rb atomic gas medium at room temperature; the investigations demonstrate the ability to enhance and control the Kerr nonlinear coefficient according to laser parameters in single-window and double-window EIT regimes. The amplitude and the sign of the Kerr nonlinear coefficient change sensitively to the driving laser parameters as well as ambient temperature. As a representative application, this Kerr nonlinear medium is applied to optical bistability (OB) and shows that the threshold intensity and the width of OB are also easily changed according to the laser parameters. In particular, in double-window EIT regime the emergence of nonlinear coefficient at different frequency domains that leads to the appearance of OB at multiple frequency domains simultaneously. This study is a good preparation for experimental observations of Kerr nonlinearity and optical bistability.

Improving the performance of echo state networks through state feedback.

Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time series modeling, and system identification. Echo state networks (ESNs), a type of reservoir computer, mirror neural networks but simplify training. They apply fixed, random linear transformations to the internal state, followed by nonlinear changes. This process, guided by input signals and linear regression, adapts the system to match target characteristics, reducing computational demands. A potential drawback of ESNs is that the fixed reservoir may not offer the complexity needed for specific problems. While directly altering (training) the internal ESN would reintroduce the computational burden, an indirect modification can be achieved by redirecting some output as input. This feedback can influence the internal reservoir state, yielding ESNs with enhanced complexity suitable for broader challenges. In this paper, we demonstrate that by feeding some component of the reservoir state back into the network through the input, we can drastically improve upon the performance of a given ESN. We rigorously prove that, for any given ESN, feedback will almost always improve the accuracy of the output. For a set of three tasks, each representing different problem classes, we find that with feedback the average error measures are reduced by 30%-60%. Remarkably, feedback provides at least an equivalent performance boost to doubling the initial number of computational nodes, a computationally expensive and technologically challenging alternative. These results demonstrate the broad applicability and substantial usefulness of this feedback scheme.

A strain rate-dependent distortional hardening model for nonlinear strain paths

In this paper, a strain rate-dependent distortional hardening model is firstly proposed to describe strain rate-dependent material behaviors under linear and nonlinear strain paths changes in 0≤θpathchange≤180∘. The proposed model is formulated based on the simplified strain rate-independent distortional hardening model (Choi and Yoon, 2023). Any yield function could be used for the strain rate-dependent isotropic and anisotropic yielding. For the linear strain path, the strain rate-dependent isotropic hardening behavior could be explained by two state variables representing rate-dependent yielding and convergence rate of flow stress under monotonically increasing loading condition, respectively. For the nonlinear strain paths, the strain rate-dependent material behaviors such as Bauschinger effect, yield surface contraction, permanent softening, and nonlinear transient behavior could be described by modifying the evolution equations of the simplified strain rate-independent distortional hardening model with a logarithmic term of strain rate. For the verification purpose, it was used the strain-rate dependent tension-compression experiments of TRIP980 and TWIP980 (Joo et al., 2019). In addition, a high speed U-draw bending test was conducted with original and pre-strained specimens. The springback prediction in high speed U-draw bending test was performed by using strain rate-independent isotropic, strain rate-dependent isotropic-kinematic and distortional hardening models. It is identified that the proposed model showed the most accurate prediction for the pre-strained specimen where the possible bilinear and trilinear path change in 0≤θpathchange≤180∘ is observed while it showed the same accuracy for the original specimen where main strain path change occur in forward-reverse manner (θpathchange=180∘).

A hybrid K-means and KNN approach for enhanced short-term load forecasting incorporating holiday effects

Accurate short-term load forecasting (STLF) is essential for efficient power system operations, particularly during holidays when demand patterns vary significantly. Traditional methods often struggle with these non-linear changes, leading to increased operational costs. This research introduces a hybrid model combining K-means clustering and K-nearest neighbors (KNN) classification to enhance forecast accuracy by accounting for holiday effects. Using historical electricity consumption data, the model applies K-means to classify load patterns and KNN to predict load clusters based on the day of the week, month, and holiday status. Advanced forecasting algorithms for each cluster, including linear regression, neural networks with Bayesian optimization, Support Vector Regression with Bayesian Optimization, and long short-term memory with Bayesian optimization, were evaluated using mean absolute percentage error (MAPE), ANOVA, and Tukey's HSD test. Results show significant improvements in forecast precision, especially during holidays, with the LSTM-BO model achieving the lowest MAPE values. The proposed hybrid model achieved an average 56.1 % improvement in forecasting accuracy across various scenarios, providing a reliable tool for power system planners and operators to optimize performance and reduce costs.

A novel soft sensor approach for industrial quality prediction based TCN with spatial and temporal attention

The complex industrial process is often characterized by strong multivariate coupling and nonlinear dynamic changes, which pose great challenges to modeling and prediction. Traditional deep learning methods are difficult to effectively capture spatiotemporal characteristics of industrial processes, resulting in poor prediction accuracy. To tackle this issue, we propose a novel end-to-end method named STA-TCN, which utilizes a temporal convolutional network (TCN) with both spatial and temporal attention mechanisms. The TCN uses causal and dilated convolutions to capture long temporal patterns in time series data. The spatial attention identifies the significance of different features, while the temporal attention focuses on crucial time steps. This design assigns adaptive weights to different features and emphasizes key moments to improve the accuracy of dynamic processes. We conduct experiments on two industrial datasets and show that the proposed STA-TCN method achieves significantly improved predictive performance compared to TCN for quality prediction of industrial processes. The results validate the effectiveness and robustness of the proposed method.

Porous Electrode Theory-Based Rapid Simulation of Deformation of Li-Ion Batteries

Lithium-ion batteries change their geometric dimensions during cycling as a macroscopic result of a series of microscale mechanisms, including but not limited to diffusion-induced expansion/shrinkage, gas evolution, growth of solid-electrolyte interphase, and particle cracking. Predicting the nonlinear dimensional changes with mathematical models is critical to the lifetime prediction, health management, and non-destructive assessment of batteries. In this study, we present an approach to implement an elastoplasticity model for powder materials into the porous electrode theory (PET). By decomposing the overall deformation into elastic, plastic, and diffusion-induced portions and using the powder plasticity model to describe the plastic portion, the model can capture the reversible thickness change caused by Li-ion (de-)intercalation as well as the irreversible thickness change due to the rearrangement and consolidation of particles. For real-world applications of the model to predict battery health and safety, the key lies in solving the mathematical equations rapidly. Here, we implemented the coupled model into the open-source software PETLION for millisecond-scale simulation. The computational model is parameterized using values gathered from literature, tested under varying conditions, briefly compared to real-world observations, and qualitatively analyzed to find parameter-output relations.

The association between salivary oxytocin, age, and puberty in children with and without OCD

The oxytocin system has been thought to contribute to obsessive-compulsive disorder (OCD). Few studies, only involving adults, have investigated this hypothesis and have found inconsistent results regarding oxytocin system activity and OCD. We investigated whether salivary oxytocin concentrations differed between children and adolescents with and without OCD and qualified our comparative analysis by investigating the possible covariates age, pubertal stage, and sex. Participants included 113 children and adolescents (8–17 years) with OCD and 88 children and adolescents without any previous or current psychiatric disorder and their parents (254 parents included). Salivary oxytocin concentrations were measured in children and parents with enzyme-linked immunosorbent assay (ELISA). Statistical analyses were performed using frequentist and Bayesian approaches. We found no evidence of a difference in mean salivary oxytocin concentrations between children and adolescents with and without OCD. Bayesian analysis indicated anecdotal to moderate support for the null hypothesis. We found an association between oxytocin and age and between oxytocin and pubertal stage, which by visual inspection of plots and post-hoc tests indicated nonlinear relationships. We found no association between oxytocin concentration and sex. Our findings do not suggest elevated oxytocin concentrations in pediatric OCD. Nonlinear changes in oxytocin across development show the importance of accounting for hormonal and behavioral changes during puberty.

The chaos theory of careers in career education

Approaches to career education in schools continue to be dominated by a focus on school to work or further or higher education transition planning. It is argued that as a consequence of this, the emphasis is on identifying relatively stable and singular vocational goals or outcomes. Furthermore the theories, techniques and models that support this focus characterise the world as largely stable and predictable. It is argued that these assumptions about the world and careers are increasingly questionable and this calls into question the theories and models used to support the short-term vision of transition. The Chaos Theory of Careers is introduced as a dynamical systems theory alternative and contemporary model of career development that emphasises continual, uncertain and non-linear change, complexity of influences, and emergent fractal patterns in career. The application of this approach to career education is adumbrated challenging traditional notions of career planning and goal setting, and highlighting the importance of creativity, reinvention and resilience as important outcomes of contemporary career education.

Improved long-term prediction of chaos using reservoir computing based on stochastic spin–orbit torque devices

Predicting chaotic systems is crucial for understanding complex behaviors, yet challenging due to their sensitivity to initial conditions and inherent unpredictability. Probabilistic reservoir computing (RC) is well suited for long-term chaotic predictions by handling complex dynamic systems. Spin–orbit torque (SOT) devices in spintronics, with their nonlinear and probabilistic operations, can enhance performance in these tasks. This study proposes an RC system utilizing SOT devices for predicting chaotic dynamics. By simulating the reservoir in an RC network with SOT devices that achieve nonlinear resistance changes with random distribution, we enhance the robustness for the predictive capability of the model. The RC network predicted the behaviors of the Mackey–Glass and Lorenz chaotic systems, demonstrating that stochastic SOT devices significantly improve long-term prediction accuracy.

Scheme evaluation method of coal gangue sorting robot system with time-varying multi-scenario based on deep learning

The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

The thrust balance model during the dragonfly hovering flight

In recent years, the micro air vehicle (MAV) oscillations caused by thrust imbalances have received more attention. This paper proposes a dual-wing thrust balance model (DTBM) that can solve the above problem by iterating the modified rotation angle formula. The core control parameter of the DTBM model is the au angle, which refers to the angle between the wing surface and the stroke plane at the mid-stroke position during the upstroke. For each degree change in the au angle, the range of variation in the dimensionless average thrust coefficient is between 0.0225-0.0268. A thrust coefficient of 0.0225 causes the dragonfly to move forward by 9.037 cm in one second, which is equivalent to 1.29 times its body length. By using DTBM, the average thrust coefficient can be reduced to below 0.001 in just a few iterations. No matter how complex the motion pattern is, the DTBM can achieve thrust balance within 0.278 s. Through our research, when selecting the deviation angle motion of real dragonflies, the dual-wing au angles exhibit a highly linear correlation with wing spacing, called linear motion. In contrast, the nonlinear variation of the au angle appears in the hindwing of the no-deviation motion and the forewing of the elliptical deviation motion. All of the nonlinear changes are referred to as nonlinear motion. Nonlinear variation of the au angle arises from larger disturbances of the lateral force during the upstroke. The stronger lateral force is closely related to the flapping trajectory. When the flapping trajectory causes the dual-wing to closely approach each other in the mid-stroke, a continuous positive pressure zone forms between the dual-wing. The collision of the leading-edge vortex and the shedding of the trailing-edge vortex is the special flow field structure in the nonlinear motion. Guided by the DTBM, future designs of MAVs will be able to better achieve thrust balance during hovering flight, requiring only the embedding of the iteration algorithm and prediction function of the DTBM in the internal chip.

Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales.

Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD;ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free-ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation with Ophidiomyces ophidiicola. Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural-level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genus Pseudomonas was characteristic of disease-negative microbiomes, whereas, positive snakes were characterized by the pathobionts Chryseobacterium, Paracoccus, and Sphingobacterium. Geographic regions suitable for O. ophidiicola had high pathogen loads (>0.66 maximum sensitivity+specificity). We found that pathogen-induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen.

Coastal vertical land motion across Southeast Asia derived from combining tide gauge and satellite altimetry observations

Vertical land motion (VLM) is complex in Southeast Asia because this region is subject to a range of natural processes (e.g., earthquakes) and anthropogenic activities (e.g., groundwater withdrawal) that can change land heights. To aid in coastal management, long-term observations of VLM are as crucial as observations for climate-induced sea surface height changes; however, such long-term observations are sparse for Southeast Asian coasts. To fill this observational gap, here we derive monthly VLM time series from 1993 to 2020 at 50 coastal sites across Southeast Asia by combining tide-gauge records and newly generated satellite altimetry observations. These altimetry observations are reproduced sea-level products using new altimetry standards and more accurate geophysical corrections. Our 27-year-long VLM dataset shows high spatial variability and non-linear temporal changes in VLM across Southeast Asia. We identify several major sources that dominate the regional land-height changes, which include large subsidence due to groundwater extraction in Manila and Bangkok, land uplift in Indonesia and subsidence in Thailand from postseismic deformation resulting from the sequence of large Sumatran earthquakes since 2004, and land subsidence as a result of sediment compaction in Malaysia. Those signals are quantitatively or qualitatively consistent with observations from other sources. This VLM dataset can be used to advance our understanding of the physical mechanisms behind land-height changes and to improve sea level projections in the region.