Single-point Methods Research Articles

The comparison between single-point method and footprint-integrated validation method of the remote-sensing retrieval of evapotranspiration: a case study at Daman site

ABSTRACT In single-site validations, site-pixel, synthetic pixel and footprint-integrated methods are commonly used to validate evapotranspiration (ET) retrieval at the pixel scale. To reveal the reliability of these methods, this study analysed the performance of the three methods for ET retrieval validation based on eddy-covariance observations at the Daman site which is numerously used for validation in Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The analysis at different spatial scales in different seasons showed that the validation results of the site-pixel method were more similar that of the footprint-integrated method than the synthetic pixel method. However, the validation results of the site-pixel method showed significant discrepancies from those of the footprint-integrated method, especially in summer at the 480 M scale which is near to the spatial resolution of MODIS with a large difference of more than 125.65 W M−2. This error is due to aggregation and high spatial heterogeneity. The use of synthetic pixel can avoid this error. Normally, the error probably appears when the wind direction is 270°–360°, usually in summer, autumn and winter. In addition, the different aggregation methods had a slight effect on the ET estimation results. This study is helpful in determining the reliability of different single-point validation methods and provides a reliable way to select a validation method for single-site validation of remote sensing retrieval of ET.

Wearable network for multilevel physical fatigue prediction in manufacturing workers.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

Hybrid methods for solving structural geometric nonlinear dynamic equations: Implementation of fifth-order iterative procedures within composite time integration methods

This paper studies a class of hybrid methods that implement multi-point iterative procedures as the nonlinear solver within optimized composite implicit methods. These multi-point methods are employed to enhance convergence order and accuracy without requiring higher-order derivatives for solving structural geometric nonlinear dynamic equations. The promising approach provides an opportunity to delve deeper into its implications and explore the potential applications of multi-point techniques and composite methods across the analysis of dynamical systems. This study involves the utilization of two-point and five-point iterative methods with fifth-order convergence within three-step and four-step optimized composite implicit time integration methods. Moreover, a comprehensive hybrid scheme is introduced by integrating a family of optimized composite implicit methods with single-point and multi-point iterative methods. Then, the performance of the hybrid scheme is analyzed through structural examples, considering factors such as time step size, tolerance threshold, and the degrees of freedom of structures. The findings and numerical results illustrate that multi-point methods outperform the single-point method in terms of the number of iterations. Simultaneously, the four-step time integration method exhibits superior performance compared to the three-step time integration method, albeit with a marginal difference. It is observed that the hybrid method, consisting of the four-step time integration method and the five-point iterative method, performs better than other methods in terms of the number of iterations and errors for solving geometric nonlinear dynamic equations, especially in large structures. Thus, it stands out as a favorable choice for practical analyses and a possible candidate for generalization to other types of nonlinear problems in computational mechanics.

UV spectrophotometric determination of chlorthalidone in tablet dosage form by using single point standardization method

The current research endeavors to elucidate the creation of an uncomplicated, highly sensitive, swift, precise, and cost-effective UV-accepted spectrophotometric method for the quantitative assessment of Chlorthalidone. This is achieved through the utilization of a visible spectrophotometric approach employing single-point standardization and calibration plot methods, for pharmaceutical dosage forms. The equipment employed includes a double-beam UV-visible spectrophotometer, specifically the Shimadzu Model UV1800, with 1cm quartz cells and 0.2 M Sodium hydroxide serving as the solvent. Notably, an absorption maximum is identified at 219 nm. The developed method strictly adheres to Beer’s law. In the case of single-point standardization, the percentage of Chlorthalidone detected falls below the labeled claimed limit. Simultaneously, the tablet formulation is subjected to a percentage purity test using the calibration plot method, revealing that the observed quantity of Chlorthalidone is below the labeled content. This suggests a potential discrepancy in the marketed product of Chlorthalidone, indicating a probable deficiency in the therapeutic effect of the formulation due to the lower amount of Chlorthalidone present. The overall efficacy of the product hinges on the quality assurance of its constituents.

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited in vitro and in vivo data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.2 in the coefficient of determination. A significant aspect of this methodology is its resilience to the inclusion of extraneous chemical or experimental data. While additional data typically introduces a considerable level of noise and can hinder performance of single end point QSAR modeling, imputation models remain unaffected. This implies a reduction in the need for laborious manual preprocessing tasks such as feature selection, thereby making data preparation for ML analysis more efficient. This successful test, conducted on open-source data, validates the efficacy of imputation approaches in toxicity data analysis. This work opens the way for applying similar methods to other types of sparse toxicological data matrices, and so we discuss the development of regulatory authority guidelines to accept imputation models, a key aspect for the wider adoption of these methods.

Discussion: Development of a Single-Point Method to Determine Soil Plastic Limit Using Fall-Cone Data [Geotech Geol Eng 41:4473–4485, 2023

This discussion article presents a critical appraisal of three empirical correlations developed via multiple regression analysis and presented in the Kayabali et al. [Geotech Geol Eng 41:4473–4485, 2023. https://doi.org/10.1007/s10706-023-02527-0] (the Authors’) investigation for the determination of the soil consistency limits. Specifically, based solely on British Standard (BS) fall-cone (FC) test data, the Authors purport that the correlations given by Equations 2 and 3 of their paper can be used to predict the ASTM rolling-plate plastic limit (i.e., PLRP), while their Equation 4 can be used to predict the BS FC liquid limit (i.e., LLFC). The Authors demonstrated that these correlations gave good predictions of the measured LLFC and PLRP water contents (i.e., wL(FC) and wP(RP), respectively) for 87 fine-grained soils they sourced from different parts of Central Turkey. Employing newly compiled large and diverse consistency limits databases assembled from the published research literature, this discussion article confirms that the Authors’ Equations 2 and 3 generally produce poor wP(RP) predictions for the fine-grained soils comprising these databases, invariably overestimating (often seriously) their measured plastic limit values. Hence, the Discussers recommend that the Authors’ Equations 2 and 3 (being generally unreliable beyond the investigated Turkish soils) should not be used in geotechnical engineering practice. While the Authors’ single-point LLFC method given by their Equation 4 broadly appears as a good wL(FC) predictor for the newly compiled database soils, it is noted that there already exist well-established and standardised single-point LLFC methods.

A new raw signal fusion method using reweighted VMD for early crack fault diagnosis at spline tooth of clutch friction disc

Spline tooth root crack is a kind of critical faults for clutch friction disc. Most traditional crack diagnosis methods based on single-sensor or fusion may be degraded due to incomplete fault information or low signal-to-noise ratio (SNR). Aiming at enhancing the ratio of fault-related information in the fusion signal, a new reweighted variational mode decomposition (VMD) multi-point fusion method is proposed. With the proposed method, each measurement point vibration acceleration signal is decomposed by VMD firstly, and the intrinsic mode functions (IMFs) containing different sensitivities information are obtained. Then, correlation energy fluctuation (CEF) and confidence distance are utilized to evaluate the IMFs and raw signal respectively. A new dual-weight strategy is proposed to restructure the IMFs. Finally, convolutional neural network (CNN) is used to identify spline tooth early crack faults. Experimental results demonstrate that the proposed method has greater fault recognition rate than single point and other fusion methods.

Use of an Integrating Cavity Spectrometer to Easily Determine Beer SRM Color Without Filtration, Centrifugation or Numerical Correction

Quantification of beer color of craft unfiltered, hazy, seltzer, and fruited beer styles can be problematic due to turbidity and unconventional ingredients. A novel integrating cavity spectrometer (ICS), designed to eliminate the effect of light scatter by the sample was employed to determine if it was able to more accurately quantify beer color compared to a conventional scanning spectrophotometer (CSS). A CSS was employed to measure the absorbance and transmission of 15 filtered and unfiltered craft brewery beer samples to determine the SRM color value and tristimulus color values. The ICS measures absorbance with a 430 nm LED to determine the single-point SRM value for each beer. The SRM values of unfiltered or 0.2 µm filtered beer samples were compared using Bland-Altman analysis and regression fits. Filtered and very clear beers showed strong agreement between the methods and Bland-Altman analysis confirmed their equivalence, particularly for SRM <25. Using the ICS alone, filtered beer versus unfiltered beer showed highly correlated values and narrow limits of agreement, successfully negating the effects of turbidity. In conclusion, SRM determination by ICS is equivalent to standard spectrophotometer single-point methods and superior in accurately determining the SRM color of hazy beers without filtration or centrifugation.

Agreement Between Single- And Double-point Estimations Of Pulse Wave Velocity

Aortic pulse wave velocity (PWV) is a common measure of large artery stiffness, and an independent predictor of cardiovascular disease risk. Currently, the gold standard PWV measure, carotid-femoral pulse wave velocity (cfPWV), is taken using two arterial sites. A single-point method of PWV would be useful as it would decrease required operator training and patient burden. PURPOSE: To assess (1) overall, and (2) repeated measures (RM) agreement, between cfPWV and two test measures, which estimate aortic PWV from the brachial artery. METHODS: The criterion cfPWV measure was obtained with carotid and femoral artery oscillometric cuffs. Brachial-based PWV was obtained using the BP+ device (PWVBP) and the Mobil-o-graph device (PWVMOG). Measurements were made in the supine, then semi-recumbent, then seated position. Postural change was used as a hemodynamic perturbation for ascertainment of RM agreement. Multi-level correlation was used to calculate overall agreement (independent of posture), and RM correlation was used to determine whether change (i.e., with change in posture) in the test measure agrees with change in the criterion. Strength of agreement was interpreted as the intraclass correlation coefficient (ICC), with estimates of <0.2, 0.2, 0.4, 0.7, and 0.9 representing negligible, weak, moderate, strong, and very strong agreement, respectively. RESULTS: Complete data was collected for 30 subjects (age: 24 ± 5.5 years, 57% female). cfPWV increased approximately 1m/s from the supine to semi-recumbent, and from the semi-recumbent to seated posture. The overall and RM agreements between PWVMOG and cfPWV were weak, with the ICC: 0.35, 95%CI [0.15, 0.52] and ICC -0.28, 95% CI [-0.50, -0.02], respectively. The overall agreement between PWVBP and cfPWV was moderate (ICC 0.50, 95% CI [0.32, 0.64]) and the RM agreement was weak (ICC 0.30, 95% CI [0.05, 0.52]). Limits of agreement plots indicated bias, with both single-point methods producing lower PWV than the criterion. CONCLUSIONS: The results show PWVMOG weakly but significantly agreed with cfPWV, whereas PWVBP moderately and significantly agreed with cfPWV. RM agreement was weak, likely because both single point-measures changed minimally with postural change, whereas the criterion device recorded expected increases in PWV with more upright posture.

Comprehensive Quantum Chemical Characterization of the Astrochemically Relevant HCnH Chain Family: An Attempt to Aid Astronomical Observations

AbstractIn this paper, the results of a comprehensive theoretical investigation of neutral HCnH0 and charged HCnH± chains are reported using state‐of‐the‐art compound model chemistries (W1BD, G4, CBS‐QB3, CBS‐APNO, CBS‐4M) and single point methods (including “gold standard” CCSD(T)). The properties envisaged include electronic and chemical bonding structure data, enthalpies of formation, adiabatic and vertical ionization (IP), and electron attachment (EA) energies, gas phase acidities, C−H bond strengths, and chain “deprotecting” (HCnH → H + Cn + H) energies. The best theoretical estimates based on composite models reported here for the various properties investigated agree with the available experimental data and are better than all previous theoretical estimates. To exemplify, for the enthalpies of formation, the G4 composite model achieves a RSMD (root square mean deviation) of 0.28 kcal mol−1 and a MUD (maximum unsigned deviation) of 0.45 kcal mol−1. Most importantly from the astrochemical perspective, it is predicted that the stable HCnH− anions with are nonlinear. Having sufficiently large dipole moments, the anion HCnH− cis conformers are potential candidates for detection in space via rovibrational spectroscopy.

Benchmark Study of Redox Potential Calculations for Iron-Sulfur Clusters in Proteins.

Redox potentials have been calculated for 12 different iron–sulfur sites of 6 different types with 1–4 iron ions. Structures were optimized with combined quantum mechanical and molecular mechanical (QM/MM) methods, and the redox potentials were calculated using the QM/MM energies, single-point QM methods in a continuum solvent or by QM/MM thermodynamic cycle perturbations. We show that the best results are obtained with a large QM system (∼300 atoms, but a smaller QM system, ∼150 atoms, can be used for the QM/MM geometry optimization) and a large value of the dielectric constant (80). For absolute redox potentials, the B3LYP density functional method gives better results than TPSS, and the results are improved with a larger basis set. However, for relative redox potentials, the opposite is true. The results are insensitive to the force field (charges of the surroundings) used for the QM/MM calculations or whether the protein and solvent outside the QM system are relaxed or kept fixed at the crystal structure. With the best approach for relative potentials, mean absolute and maximum deviations of 0.17 and 0.44 V, respectively, are obtained after removing a systematic error of −0.55 V. Such an approach can be used to identify the correct oxidation states involved in a certain redox reaction.

Wind power time series simulation model based on typical daily output processes and Markov algorithm

The simulation of wind power time series is a key process in renewable power allocation planning, operation mode calculation, and safety assessment. Traditional single-point modeling methods discretely generate wind power at each moment; however, they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency. To resolve this problem, a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed. First, a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented. Second, considering the typical daily output processes as status variables, a wind power time series simulation model based on Markov algorithm is constructed. Finally, a case is analyzed based on the measured data of a wind farm in China. The proposed model is then compared with traditional methods to verify its effectiveness and applicability. The comparison results indicate that the statistical characteristics, probability distributions, and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods. Moreover, modeling efficiency considerably improves.

Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm

Wind speed prediction plays an essential role in wind energy utilization. However, most existing studies of wind speed forecasting used data from one location to build models and forecasts, which limited the accuracy of wind speed forecasting. Therefore, to improve the prediction accuracy at a target location, this study proposes a multiple-point model based on data from multiple locations for short-term wind speed prediction. The model, which utilizes wind speed measurements from neighboring locations and combines the extreme learning machine (ELM) with the AdaBoost algorithm, is named the multiple-point-AdaBoost-ELM model. Data from seventeen automatic meteorological stations in the Heihe River Basin are used, four stations at different positions are taken as target stations for multi-time-scale wind speed prediction, and six models and several metrics are involved for comparative analysis and comprehensive evaluation. The results show that: (1) the prediction performance of the proposed multiple-point-AdaBoost-ELM model is significantly superior to that of the compared single-point models; (2) the prediction accuracy of the multiple-point-AdaBoost-ELM model is relatively less affected by the prediction time-scale than that of the corresponding single-point model; and (3) the stations located at the center of multiple stations can obtain more accurate prediction results than those located near the edges of the region. Therefore, the proposed multiple-point-AdaBoost-ELM model is a more promising method than traditional single-point modeling methods. The proposed method fully uses historical wind speed at surrounding locations to enhance the wind speed predictions at target locations, makes up for the deficiency of the wind speed forecasting using data from one location, and expands a new way for wind speed prediction.

A Data-Driven Sequential Localization Framework for Big Telco Data

The proliferation of telco networks and mobile terminals brings the accumulation of tremendous amounts of measure report(MR) data at a rapid pace. The MR data is generated by mobile objects while connecting to data services and is stored in backend data centers. To geo-tag or localize such MR data is believed to have a profound effect on the analytics and optimizations of telco and traffic networks. However, MR records are of noisy and partial observations regarding to mobile objects' geo-locations and hence pose challenges to accurate telco data localization. There have been quite a few attempts. Single-point localization methods map a MR record to a location, but come out with limited accuracies due to the ignorance of spatiotemporal coherence of successive MR records. Recent efforts on sequential localization techniques alleviate this by mapping a sequence of MR records to a trajectory. However, existing solutions are often with assumptions on specific models, e.g., mobility and signal strength distributions, or priori knowledge on topology space, e.g., road networks, limiting the deployment in practice. To this end, we propose a data-driven framework to tackle the challenges in sequential telco localization. We solely use raw MR records and a public third-party GPS dataset for the learning of the correlations between mobile objects' locations and MR records, requiring no model assumptions and priori knowledge. To handle the data-intensive workloads during the learning process, we use materialized views for efficient online localization and light-weighted indexing techniques for periodical parameters tuning, in order to improve the efficiency and scalability. Results on real data show that our solution achieves 58.8 percent improvement in median localization errors compared with state-of-art sequential localization techniques that require hypothesis models and priori knowledge, making our solution superior in terms of effectiveness, efficiency, and employability.

Organic-Inorganic Hybrid Materials for Room Temperature Light-Activated Sub-ppm NO Detection.

Nitric oxide (NO) is one of the main environmental pollutants and one of the biomarkers noninvasive diagnosis of respiratory diseases. Organic-inorganic hybrids based on heterocyclic Ru (II) complex and nanocrystalline semiconductor oxides SnO2 and In2O3 were studied as sensitive materials for NO detection at room temperature under periodic blue light (λmax = 470 nm) illumination. The semiconductor matrixes were obtained by chemical precipitation with subsequent thermal annealing and characterized by XRD, Raman spectroscopy, and single-point BET methods. The heterocyclic Ru (II) complex was synthesized for the first time and characterized by 1H NMR, 13C NMR, MALDI-TOF mass spectrometry and elemental analysis. The HOMO and LUMO energies of the Ru (II) complex are calculated from cyclic voltammetry data. The thermal stability of hybrids was investigated by thermogravimetric analysis (TGA)-MS analysis. The optical properties of Ru (II) complex, nanocrystalline oxides and hybrids were studied by UV-Vis spectroscopy in transmission and diffuse reflectance modes. DRIFT spectroscopy was performed to investigate the interaction between NO and the surface of the synthesized materials. Sensor measurements demonstrate that hybrid materials are able to detect NO at room temperature in the concentration range of 0.25–4.0 ppm with the detection limit of 69–88 ppb.

Development of a microscopic adhesive evaluation method using a scanning haptic microscope

Single-point measurement methods have been proposed to study both microscopic to macroscopic adhesive properties; however, thus far, a simple and quantitative distribution observation method has not been established. A scanning haptic microscope (SHM) was developed to observe microscopic distribution of the elastic modulus of biological tissue surfaces. The SHM comprises a vibrating glass probe and measures the elastic modulus from the variation in vibration frequency during indentation by the probe. By focusing on the variation in frequency during the pulling of the probe, the adhesive properties between sample surface and probe tip could be evaluated. In this study, we examined the possibility of measuring adhesion properties, as a basis study of adhesive property mapping by SHM. The samples used were silicone gels with different adhesive properties by varying the mixing ratio of silicones with high or low adhesion. The sample was indented using a glass probe as in usual SHM measurements. The time difference during the variation in frequency between indentation and pulling (i.e., adhesion time) of each silicon sample was clearly distinguished. The adhesion time measured by the SHM was closely related to tack indices in rolling ball and probe tack tests; classical tests were conducted to quantitatively evaluate the tack. SHM is expected to be a powerful mapping tool of adhesive property with microscopic spatial resolution for centimeter-level measurement areas.

Quasi Single Point Calibration Method for High-Speed Measurements of Resistive Sensors.

Direct interface circuits are a simple, inexpensive alternative for the digital conversion of a sensor reading, and in some of these circuits only passive calibration elements are required in order to carry out this conversion. In the case of resistive sensors, the most accurate methods of calibration, namely two-point calibration method (TPCM) and fast calibration methods I and II (FCMs I and II), require two calibration resistors to estimate the value of a sensor. However, although FCMs I and II considerably reduce the time necessary to estimate the value of the sensor, this may still be excessive in certain applications, such as when making repetitive readings of a sensor or readings of a large series of sensors. For these situations, this paper proposes a series of calibration methods that decrease the mean estimation time. Some of the proposed methods (quasi single-point calibration methods) are based on the TPCM, while others (fast quasi single-point calibration methods) make the most of the advantages of FCM. In general, the proposed methods significantly reduce estimation times in exchange for a small increase in errors. To validate the proposal, a circuit with a Xilinx XC3S50AN-4TQG144C FPGA has been designed and resistors in the range (267.56 Ω, 7464.5 Ω) have been measured. For 20 repetitive measurements, the proposed methods achieve time reductions of up to 61% with a relative error increase of only 0.1%.

Photosensitive Organic-Inorganic Hybrid Materials for Room Temperature Gas Sensor Applications.

In this work, the hybrids based on nanocrystalline SnO2 or In2O3 semiconductor matrixes and heterocyclic Ru(II) complex are studied as materials for gas sensors operating at room temperature under photoactivation with visible light. Nanocrystalline semiconductor oxides are obtained by chemical precipitation with subsequent thermal annealing and characterized by XRD, SEM and single-point BET methods. The heterocyclic Ru(II) complex is synthesized for the first time and investigated by 1H NMR, 13C NMR APT, MALDI-MS analysis, and UV-Vis spectroscopy. The HOMO and LUMO energies of the Ru(II) complex are calculated from cyclic voltammetry data. The hybrid materials are characterized by TGA-MS analysis and EDX mapping. The optical properties of hybrids are studied by UV-Vis spectroscopy in the diffuse reflection mode. The investigation of spectral dependencies of photoconductivity of hybrid samples demonstrates that the role of organic dye consists in shifting the photosensitivity range towards longer wavelengths. Sensor measurements demonstrate that hybrid materials are able to detect NO2 in the concentration range of 0.25–2 ppm without the use of thermal heating under periodic illumination with even low-energy long-wavelength (red) light.

Inference statistical analysis of continuous data based on confidence bands-Traditional and new approaches.

In the analysis of continuous data, researchers are often faced with the problem that statistical methods developed for single-point data (e.g., t test, analysis of variance) are not always appropriate for their purposes. Either methodological adaptations of single-point methods will need to be made, or confidence bands are the method of choice. In this article, we compare three prominent techniques to analyze continuous data (single-point methods, Gaussian confidence bands, and function-based resampling methods to construct confidence bands) with regard to their testing principles, prerequisites, and outputs in the analysis of continuous data. In addition, we introduce a new technique that combines the advantages of the existing methods and can be applied to a wide range of data. Furthermore, we introduce a method enabling a priori and a posteriori power analyses for experiments with continuous data.

Effect of Osmotic Pressure on Cellular Stiffness as Evaluated Through Force Mapping Measurements

Atomic force microscopy (AFM) has been used to measure cellular stiffness at different osmolarities to investigate the effect of osmotic pressure on cells. However, substantial direct evidence is essential to clarify the phenomena derived from the experimental results. This study used both the single-point and force mapping methods to measure the effective Young's modulus of the cell by using temporal and spatial information. The single-point force measurements confirmed the positive correlation between cellular stiffness and osmolarity. The force mapping measurements provided local stiffness on the cellular surface and identified the cytoskeleton distribution underneath the plasma membrane. At hyper-osmolarity, the cytoskeleton was observed to cover most of the area underneath the plasma membrane, and the effective Young's modulus on the area with cytoskeleton support was determined to be higher than that at iso-osmolarity. The overall increase in cellular Young's modulus confirmed the occurrence of cytoskeleton compression at hyper-osmolarity. On the other hand, although the average Young's modulus at hypo-osmolarity was lower than that at iso-osmolarity, we observed that the local Young's modulus measured on the areas with cytoskeleton support remained similar from iso-osmolarity to hypo-osmolarity. The reduction of the average Young's modulus at hypo-osmolarity was attributed to reduced cytoskeleton coverage underneath the plasma membrane.