Variety Of Time Scales Research Articles

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity.

Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.

Scaling of the geomagnetic secular variation timescale

SUMMARY The ratio of the magnetic power spectrum and the secular variation spectrum measured at the Earth’s surface provides a timescale $\tau _{\rm sv}(l)$ as a function of spherical harmonic degree l. $\tau _{\rm sv}$ is often assumed to be representative of timescales related to the dynamo inside the outer core and its scaling with l is debated. To assess the validity of this surmise and to study the time variation of the geomagnetic field $\dot{\boldsymbol {B}}$ inside the outer core, we introduce a magnetic timescale spectrum $\tau (l,r)$ that is valid for all radius r above the inner core and reduces to the usual $\tau _{\rm sv}$ at and above the core–mantle boundary (CMB). We study $\tau$ in a numerical geodynamo model. At the CMB, we find that $\tau \sim l^{-1}$ is valid at both the large and small scales, in agreement with previous numerical studies on $\tau _{\rm sv}$. Just below the CMB, the scaling undergoes a sharp transition at small l. Consequently, in the interior of the outer core, $\tau$ exhibits different scaling at the large and small scales, specifically, the scaling of $\tau$ becomes shallower than $l^{-1}$ at small l. We find that this transition at the large scales stems from the fact that the horizontal components of the magnetic field evolve faster than the radial component in the interior. In contrast, the magnetic field at the CMB must match onto a potential field, hence the dynamics of the radial and horizontal magnetic fields are tied together. The upshot is $\tau _{\rm sv}$ becomes unreliable in estimating timescales inside the outer core. Another question concerning $\tau$ is whether an argument based on the frozen-flux hypothesis can be used to explain its scaling. To investigate this, we analyse the induction equation in the spectral space. We find that away from both boundaries, the magnetic diffusion term is negligible in the power spectrum of $\dot{\boldsymbol {B}}$. However, $\dot{\boldsymbol {B}}$ is controlled by the radial derivative in the induction term, thus invalidating the frozen-flux argument. Near the CMB, magnetic diffusion starts to affect $\dot{\boldsymbol {B}}$ rendering the frozen-flux hypothesis inapplicable. We also examine the effects of different velocity boundary conditions and find that the above results apply for both no-slip and stress-free conditions at the CMB.

Compressed intracellular motility via non-uniform temporal sampling in dynamic optical coherence tomography.

Optical coherence tomography has great utility for capturing dynamic processes, but such applications are particularly data-intensive. Samples such as biological tissues exhibit temporal features at varying time scales, which makes data reduction challenging. We propose a method for capturing short- and long-term correlations of a sample in a compressed way using non-uniform temporal sampling to reduce scan time and memory overhead. The proposed method separates the relative contributions of white noise, fluctuating features, and stationary features. The method is demonstrated on mammary epithelial cell spheroids in three-dimensional culture for capturing intracellular motility without loss of signal integrity. Results show that the spatial patterns of motility are preserved and that hypothesis tests of spheroids treated with blebbistatin, a motor protein inhibitor, are unchanged with up to eightfold compression. The ability to measure short- and long-term correlations compressively will enable new applications in (3+1)D imaging and high-throughput screening.

Radio transients from compact objects across the mass spectrum in the era of multi-messenger astronomy

Compact objects across the mass spectrum–from neutron stars to supermassive black holes–are progenitors and/or central engines for some of the most cataclysmic phenomena in the Universe. As such, they are associated with radio emission on a variety of timescales and represent key targets for multi-messenger astronomy. Observations of transients in the radio band can unveil the physics behind their central engines, ejecta, and the properties of their surroundings, crucially complementing information on their progenitors gathered from observations of other messengers (such as gravitational waves and neutrinos). In this contribution, we summarize observational opportunities and challenges ahead in the multi-messenger study of neutron stars and black holes using radio observations. We highlight the specific contribution of current U.S. national radio facilities and discuss expectations for the field focusing on the science that could be enabled by facilities recommended by the 2020 Decadal survey such as the next generation Very Large Array (ngVLA).

Discovery of dissipative microwave photonic soliton molecules in dual-bandpass optoelectronic oscillator

Optoelectronic oscillators (OEOs), which have attracted extensive studies in the past decades, are high quality-factor optoelectronic feedback loops for generating various ultra-pure microwave signals. In essence, OEOs are also dissipative nonlinear systems with multiple timescale characteristics and abundant nonlinearities, which open the possibilities for exploring localized dissipative solitary waves. In this paper, we demonstrate a new-class temporal dissipative soliton, i.e., dissipative microwave photonic soliton molecule (DMPSM), in a dual-bandpass OEO. Both the numerical simulation and experiment are conducted to reveal the physical mechanism of DMPSM generation and to evaluate the characteristics of the generated DMPSM sequences. Unlike optical soliton molecules in mode-locked lasers, the formation of DMPSMs arises from the combined action of multiple timescale coupling, nonlinear bistability, and time-delayed feedback in the OEO cavity, where the soliton interval and number in a DMPSM can be well-controlled through varying the multiple timescale variables in the OEO cavity, and the repetition frequency of the DMPSMs can be tuned through changing that of the initially injected perturbation signal. Meanwhile, the generated DMPSM sequence performs with high stability and excellent coherence, which shows enormous application potentials in pulse radar detection, dense microwave comb generation, and neuromorphology.

Site‐Specific Multiple Stressor Assessments Based on High Frequency Surface Observations and an Earth System Model

AbstractGlobal Earth system models are often enlisted to assess the impacts of climate variability and change on marine ecosystems. In this study, we compare high frequency (daily) outputs of potential ecosystem stressors, such as sea surface temperature and surface pH, and associated variables from an Earth system model (GFDL ESM4.1) with high frequency time series from a global network of moorings to directly assess the capacity of the model to resolve local biogeochemical variability on time scales from daily to interannual. Our analysis indicates variability in surface temperature is most consistent between ESM4.1 and observations, with a Pearson correlation coefficient of 0.93 and bias of 0.40°C, followed by variability in surface salinity. Physical variability is reproduced with greater accuracy than biogeochemical variability, and variability on seasonal and longer time scales is more consistent between the model and observations than higher frequency variability. At the same time, the well‐resolved seasonal and longer timescale variability is a reasonably good predictor, in many cases, of the likelihood of extreme events. Despite limited model representation of high frequency variability, model and observation‐based assessments of the fraction of days experiencing surface T‐pH and T‐Ωarag multistressor conditions show reasonable agreement, depending on the stressor combination and threshold definition. We also identify circumstances in which some errors could be reduced by accounting for model biases.

Characterizing Supply Reliability Through the Synergistic Integration of VRE towards Enhancing Electrification in Kenya

Decentralized electrical power systems, driven by variable renewable energy sources such as solar PV and wind, have the potential to provide accessible and sustainable energy, contributing to the realization of a zero-carbon transition. However, these sources are susceptible to extreme weather conditions, presenting a challenge to the reliability of the power system. With abundant resources and a significant rural population lacking access to electricity, Africa has emerged as a key area for research on variable renewable energy-based electricity generation. Despite this focus, there remains a substantial gap in understanding at regional-scale the potential and variability of solar and wind power across various time scales, as well asthe impact of available resource synergy. Thisstudy aims to bridge this knowledge gap by conducting comprehensive simulations of hybrid wind and solar energy systems, both on-grid and off-grid, across 20 geographically diverse locations in Kenya. Using high-resolution hourly time step data, we examine the effect of resource complementarity on system reliability at varying time scales: daily, monthly and annually. The study findings shows the available VRE resource exhibit moderate tendency for complementarity, and optimizing their deployment can reduce hourly variability by 20%, significantly enhancing supply reliability, especially in the northern and eastern regions.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Seminal electromagnetic fields from preinflation

We investigate the geometric dynamics of the primordial electric and magnetic fields during the early stages of the universe by extending a recently introduced quantum algebra (Bellini et al., 2023). We work on an extended model of gravity that considers the boundary terms from the Einstein–Hilbert action as geometric quantum fluctuations of the spacetime. We propose that the extended Riemann manifold is generated by a new connection δΓˆαβμ. This connection contains geometric information about the fluctuations of gravitational and electromagnetic fields in the vacuum, which could have been crucial during the primordial stages of the universe’s evolution. We revisit a preinflationary cosmological model (Bellini, 2023) with a variable time scale and negative spatial curvature, such that the universe begins with a null initial background energy density. We observed the emergence of large scale magnetic fields starting from small values during the early phases of the universe’s evolution. Subsequently, these fields decrease to reach present day values on the order of δBˆ≃10−12G on cosmological scales (between 1024 and 1026 meters). This significant deviation from inflationary models eliminates the need to impose excessively large initial values on these fields.

Evidence for rapid variability at high energies in GRBs

ABSTRACT Intrinsic variability was searched for in arrival times of six gamma-ray bursts (GRBs) at high energies – between 30 MeV and 2 GeV – detected by the Fermi satellite’s Large Area Telescope (LAT). The GRBs were selected from the Fermi LAT catalogue with preference for events with numerous photons, a strong initial pulse, and measured redshifts. Three long GRBs and three short GRBs were selected and tested. Two different variability-detection algorithms were deployed, one counting photons in pairs, and the other multiplying time gaps between photons. In both tests, a real GRB was compared to 1000 Monte Carlo versions of itself smoothed over a wide range of different time-scales. The minimum detected variability time-scales for long bursts (GRB 080916C, GRB 090926A, GRB 131108A) was found to be (0.005, 10.0, 10.0) s for the photon pair test and (2.0, 20.0, 10.0) s for the time-gap multiplication test. Additionally, the minimum detected variability time-scales for the short bursts (GRB 090510, GRB 140619B, GRB 160709A) was found to be (0.05, 0.01, 20.0) s for the photon pair test and (0.05, 0.01, 20.0) s for the gap multiplication test. Statistical uncertainties in these times are about a factor of 2. The durations of these variability time-scales may be used to constrain the geometry, dynamics, speed, cosmological dispersion, Lorentz-invariance violations, weak equivalence principle violations, and GRB models.

Machine learning predictive model for dynamic response of rising bubbles impacting on a horizontal wall

The integrated behavior of the fluid flow, encompassing variation of some specific response, has received more attention than mere examination of velocity and pressure distributions. A machine learning framework is introduced for the first time to elucidate and predict the complex fluid dynamic responses across varying time scales. For the same class of fluid processes, the dynamic response curves with varying time durations can be defined in terms of time scales and curve shapes. The former is predicted by a single-objective regression model, and the curves are normalized and recovered using time scaling and uniform interpolation. The latter are downscaled by Principal Component Analysis (PCA) and then predicted by a multi-objective regression model. As a novel application, this framework is employed to analyze small bubbles in a Newtonian fluid as they impact a horizontal wall, predicting the velocity, aspect ratio, and position dynamic response of the bubbles. Both Random Forest and Gaussian Process models, based on PCA-driven data processing, exhibit remarkable accuracy in describing and predicting complex dynamics of rising bubbles. Notably, PCA's principal components demonstrate independence and are set through rigorous cross-validation of the training set, rather than dependence on the Cumulative Variance Explained (CVE) values. Furthermore, our framework adeptly extends to predict the complex behavior of multiple bubble bounces by concatenating individual bounce predictions with high efficiency and precision. The proposed framework proves to be a catalyst for comprehending the dynamic response of fluids.

Exploring the Ecotoxicological Effects and Mitigation Strategies for Microplastics Pollution in Aquatic Ecosystems

<p>Microplastics pollution poses a significant threat to aquatic ecosystems globally, originating from various sources such as plastic debris breakdown and microbeads. This study investigates the ecotoxicological impacts of these pollutants on marine plants and evaluates mitigation strategies. Field surveys assess their distribution, sources, and fate in rivers, lakes, estuaries, and coastal waters. Sampling methods include sediment cores, water samples, and biological specimens to quantify abundance, size distribution, polymer types, and spatial variability. Source apportionment techniques aid in identifying major contributors to pollution. Laboratory experiments simulate environmental conditions to study their behavior, including sedimentation, resuspension, aggregation, and bioaccumulation. Mathematical models predict dispersal and deposition patterns over varying time scales. Ecotoxicological studies investigate impacts on organisms across trophic levels, including fish, invertebrates, and phytoplankton, considering acute and chronic effects. Mitigation strategies like filtration systems, policy interventions, and public awareness campaigns are evaluated for efficacy in reducing pollution and safeguarding ecosystems. This research contributes to developing sustainable solutions essential for environmental preservation and human health.</p>

Evaluating the time to detect biological effects of ocean acidification and warming: an example using simulations of purple sea urchin settlement

Ocean acidification (OA) and ocean warming driven by climate change are important stressors for marine species and systems, but documenting and detecting their long-term impacts on biological responses outside of laboratory settings are challenging due to natural variability caused by complex processes and interactions. We used settlement of purple sea urchins Strongylocentrotus purpuratus in the Southern California Bight (USA) over 6 yr as an example data set to parameterize a simulation model for exploring the time needed to detect environmental effects on a biological response. A generalized linear model was used to describe an index of urchin settlement as functions of pH, sea surface temperature (SST), sea surface salinity (SSS), and spatio-temporal factors, demonstrating that settlement was negatively associated with pH (i.e. lower settlement at higher pH) and positively associated with SST and SSS. Monte Carlo simulations were developed from this base model under a variety of alternative scenarios to estimate the time to detect: (1) annual trends in pH and SST time series, (2) pH and SST effects on urchin settlement, and (3) annual trends in urchin settlement. Time to detect pH and SST effects was predominantly influenced by the underlying strength of the relationships and the model uncertainty. Time to detect annual trends in settlement was more sensitive to the severity of long-term OA and warming trends, which had cumulative (at times opposing) effects. This study highlights the variable time scales (2-60+ yr) that may be necessary to detect biological responses to OA and ocean warming and the sensitivity to different assumptions of the study system.

A molecular and cellular perspective on human brain evolution and tempo.

The evolution of the modern human brain was accompanied by distinct molecular and cellular specializations, which underpin our diverse cognitive abilities but also increase our susceptibility to neurological diseases. These features, some specific to humans and others shared with related species, manifest during different stages of brain development. In this multi-stage process, neural stem cells proliferate to produce a large and diverse progenitor pool, giving rise to excitatory or inhibitory neurons that integrate into circuits during further maturation. This process unfolds over varying time scales across speciesand has progressivelybecome slower in the human lineage, with differences in tempo correlating with differences in brain size, cell number and diversity, and connectivity. Here we introduce the terms 'bradychrony' and 'tachycrony' to describe slowed and accelerated developmental tempos, respectively. Wereview how recent technical advances across disciplines, including advanced engineering of in vitro models, functional comparative genetics and high-throughput single-cell profiling, are leading to a deeper understanding of how specializationsofthe human brain arise during bradychronic neurodevelopment. Emerging insightspointto a central role for genetics, gene-regulatory networks, cellular innovations and developmental tempo, which together contribute to the establishment of human specializations during various stages of neurodevelopment and at different points in evolution.

Observed change and the extent of coherence in the Gulf Stream system

Abstract. By transporting warm and salty water poleward, the Gulf Stream system maintains a mild climate in northwestern Europe while also facilitating the dense water formation that feeds the deep ocean. The sensitivity of North Atlantic circulation to future greenhouse gas emissions seen in climate models has prompted an increasing effort to monitor the various ocean circulation components in recent decades. Here, we synthesize available ocean transport measurements from several observational programs in the North Atlantic and Nordic Seas, as well as an ocean state estimate (ECCOv4-r4), for an enhanced understanding of the Gulf Stream and its poleward extensions as an interconnected circulation system. We see limited coherent variability between the records on interannual timescales, highlighting the local oceanic response to atmospheric circulation patterns and variable recirculation timescales within the gyres. On decadal timescales, we find a weakening subtropical circulation between the mid-2000s and mid-2010s, while the inflow and circulation in the Nordic Seas remained stable. Differing decadal trends in the subtropics, subpolar North Atlantic, and Nordic Seas warrant caution in using observational records at a single latitude to infer large-scale circulation change.

Retention of p‐Chip microtransponders and posttagging survival of small‐bodied stream fishes

AbstractObjectiveObtaining demographic rates often requires complex open‐population capture–mark–recapture (CMR) study designs. Conducting such studies for small‐bodied fishes has been limited in part by excessive mortality after tagging procedures and poor tag retention. As new tag types emerge, information regarding fish survival and tag retention over varying time scales may benefit resource managers to effectively plan future CMR studies. The p‐Chip microtransponder is a 500‐ × 500‐ × 100‐μm tag that is inserted subcutaneously and is read with a handheld laser. Each tag contains a nine‐digit unique identification number. P‐Chip microtransponder tags have been used on a limited number of small‐bodied fishes, with relatively high rates observed for fish survival and tag retention. Information on posttagging survival and retention of p‐Chip microtransponder tags across a range of small‐bodied fish species and tagging locations is needed to inform their effectiveness in future CMR studies.MethodsWe quantified survival and tag retention after p‐Chip microtransponder implantation in Creek Chub Semotilus atromaculatus and Northern Pearl Dace Margariscus nachtriebi in a laboratory environment, and retention of p‐Chip microtransponder tags in Northern Pearl Dace was also quantified over a year‐long field study. We marked 56 Creek Chub and 9 Northern Pearl Dace in the laboratory with p‐Chip microtransponder tags and compared them to an equal number of unmarked individuals over 90 days. We marked 1990 Northern Pearl Dace and conducted recapture events through four seasons (June, September, and October 2022 and June 2023) in four headwater streams of Nebraska.ResultSurvival for Creek Chub was 85% (standard error [SE] = 5.9) and did not differ from control fish survival (95%; SE = 3.2) in the 90‐day laboratory experiment. Survival for Northern Pearl Dace was 89% (SE = 11.0) and did not differ from that of control fish (100%) in the laboratory experiment. Tag retention was 89% (SE = 4.6) for Creek Chub and 100% for Northern Pearl Dace in the laboratory. The p‐Chip microtransponder performed well during the CMR field study, with tag retention for Northern Pearl Dace at 94% across 374 days.ConclusionOur results suggest that the p‐Chip microtransponder minimally affected small‐bodied fish survival and had high tag retention in both the laboratory and field studies. Thus, the p‐Chip microtransponder tag may be appropriate for use in small‐bodied fishes when individual identification is needed in a CMR study.

Non-thermal radiation from dual jet interactions in supermassive black hole binaries

ABSTRACT Supermassive black hole binaries (SMBHBs) are natural by-products of galaxy mergers and are expected to be powerful multimessenger sources. They can be powered by the accretion of matter and then radiate across the electromagnetic spectrum, much like normal active galactic nuclei (AGNs). Current electromagnetic observatories have a good chance of detecting and identifying these systems in the near future. However, precise observational indicators are needed to distinguish individual AGNs from SMBHBs. In this paper, we propose a novel electromagnetic signature from SMBHBs: non-thermal emission produced by the interaction between the jets ejected by the black holes. We study close SMBHBs, which accrete matter from a circumbinary disc and the mini-discs formed around each hole. Each black hole ejects a magnetically dominated jet in the direction of its spin through the Blandford–Znajek mechanism. We argue that in such a situation, the interaction between the jets can trigger strong magnetic reconnection events, where particles are accelerated and emit non-thermal radiation. Depending on whether the jets are aligned or misaligned, this radiation can have different periodicities. We model the evolution of the particles accelerated during the dual jet interaction and calculate their radiative output, obtaining spectra and providing estimates for the variability time-scales. We finally discuss how this emission compares with that of normal AGNs.

The laser pump X-ray probe system at LISA P08 PETRA III.

Understanding and controlling the structure and function of liquid interfaces is a constant challenge in biology, nanoscience and nanotechnology, with applications ranging from molecular electronics to controlled drug release. X-ray reflectivity and grazing incidence diffraction provide invaluable probes for studying the atomic scale structure at liquid-air interfaces. The new time-resolved laser system at the LISA liquid diffractometer situated at beamline P08 at the PETRA III synchrotron radiation source in Hamburg provides a laser pump with X-ray probe. The femtosecond laser combined with the LISA diffractometer allows unique opportunities to investigate photo-induced structural changes at liquid interfaces on the pico- and nanosecond time scales with pump-probe techniques. A time resolution of 38 ps has been achieved and verified with Bi. First experiments include laser-induced effects on salt solutions and liquid mercury surfaces with static and varied time scales measurements showing the proof of concept for investigations at liquid surfaces.

A Comprehensive Review of Indentation of Gels and Soft Biological Materials

Abstract Indentation measurement has emerged as a widely adapted technique for elucidating the mechanical properties of soft hydrated materials. These materials, encompassing gels, cells, and biological tissues, possess pivotal mechanical characteristics crucial for a myriad of applications across engineering and biological realms. From engineering endeavors to biological processes linked to both normal physiological activity and pathological conditions, understanding the mechanical behavior of soft hydrated materials is paramount. The indentation method is particularly suitable for accessing the mechanical properties of these materials as it offers the ability to conduct assessments in liquid environment across diverse length and time scales with minimal sample preparation. Nonetheless, understanding the physical principles underpinning indentation testing and the corresponding contact mechanics theories, making judicious choices regarding indentation testing methods and associated experimental parameters, and accurately interpreting the experimental results are challenging tasks. In this review, we delve into the methodology and applications of indentation in assessing the mechanical properties of soft hydrated materials, spanning elastic, viscoelastic, poroelastic, coupled viscoporoelastic, and adhesion properties, as well as fracture toughness. Each category is accomplished by the theoretical models elucidating underlying physics, followed by ensuring discussions on experimental setup requirements. Furthermore, we consolidate recent advancements in indentation measurements for soft hydrated materials highlighting its multifaceted applications. Looking forward, we offer insights into the future trajectory of the indentation method on soft hydrated materials and the potential applications. This comprehensive review aims to furnish readers with a profound understanding of indentation techniques and a pragmatic roadmap of characterizing the mechanical properties of soft hydrated materials.