Cumulative Distribution Curves Research Articles

Application of reverse cumulative distribution curve and scaled logit model in determining optimal immunogenic dose and prediction of protection of EV71 vaccines

ABSTRACT Background This study proposes the reverse cumulative distribution curve (RCDC) for optimal dose selection and a scaled logit model for estimating protection in EV71 vaccine development. Research design and methods Data were from a phase 2 trial involving infants and young children randomized to receive either 640 U with or without adjuvant, 320 U with adjuvant, 160 U with adjuvant EV71 vaccines, or placebo. RCDCs were constructed using neutralizing antibody titers 28 days post-vaccination. Robustness of RCDC parameters was assessed via coefficient of variation for the area under the curve (AUC), the relative optimal point, median on the curve, and antibody titer of the point of maximum curvature, with geometric mean titer (GMT) as control. The scaled logit model estimated protection against EV71-associated disease for the selected optimal dose. Results The AUC and relative optimal point demonstrated greater robustness than GMT. The 640 U with adjuvant dose had the highest AUC (0.64, 95% CI: 0.61-0.66), sum of coordinates of the relative optimal point (1.40, 95% CI: 1.34-1.43), and the highest estimated protection (93.36%, 95% CI: 79.91-97.86). Conclusions AUC and relative optimal point of RCDC are effective for early vaccine dose screening, with protection estimated by the scaled logit model.

Soil biological health assessment based on nematode communities under maize and peanut intercropping

BackgroundCereal/legume intercropping can enhance crop productivity and improve soil health in dryland farming. However, little is known about soil biological health under maize/peanut intercropping. The aim of this study was to assess soil biological health based on nematode communities in a maize/peanut intercropping system.MethodsWe conducted a field experiment with different planting patterns, including monoculture maize (M), monoculture peanut (P), and maize intercropped with peanut (IM, intercropped maize; IP, intercropped peanut) to determine the influence on soil biological health. We measured soil physicochemical properties and nematode communities, and employed exploratory factor analysis combined with cumulative normal distribution curve scoring to identify potential soil biological health traits.ResultsThe intercropped maize gave the highest plant parasitic nematode abundance, trophic diversity index, evenness index, and structure index. The monoculture peanut gave the highest enrichment index and least plant parasitic nematode abundance, trophic diversity index, Shannon diversity index, evenness index, structure index, and channel index. We identified four soil biological health traits, including basic nutrients and biodiversity, food web complexity, slow energy channel, and fast energy channel, mainly represented by soil ammonium nitrogen and Shannon diversity index, structural index and omnivore-predator nematode abundance, fungivorous nematode abundance and plant parasitic nematode abundance, microbial biomass carbon and bacterivorous nematode abundance, respectively. The intercropping systems improved the comprehensive score of soil biological health, especially maize intercropping soil. Intercropping maize and intercropping peanut significantly improved oil biological health traits representing the food web complexity compared with the corresponding monoculture soils.ConclusionsOur results indicate that soil nematode and physicochemical indicators reflect different soil biological health traits. Among those traits, the improvement of basic nutrients and biodiversity and the complexity of the food web were the main reasons for improving soil biological health through the intercropping system.

Koreksi Bias Data Hujan Satelit Persiann Sebagai Alternatif Pos Stasiun Hujan dengan Pendekatan Quantile Mapping di Sub Das Brantas Hulu

The rainfall data is usually obtained from meteorological stations, which many sill observed manually in Indonesia and can lead to an observation error. Remote sensing rainfall data observed by satellite offer interesting alternatives to overcome the limitation of existing meteorological stations in Indonesia. It offers accuracy, spatial coverage, timeliness and cost effectiveness. However it contains bias if compared to land based station. This research will evaluate rainfall data observed in PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) in Brantas Hulu Watershed. The rainfall data of 13 years from 12 stations is evaluated to obtain area averaged rainfall using Thiessen Polygon method. Three datasets from PERSIANN, PERSIANN CCS and PERSIANN CDR are compared statistically with the area averaged area for daily, monthly and yearly rainfall. Bias correction is performed using Quantile Mapping to match the cumulative distribution curve from the area averaged rainfall. Verification of bias correction is performed using 2 years data from 2019 to 2020. Finally, the extreme rainfall analysis is performed to test the corrected data for design rainfall calculations. The correlation of daily data shows poor agreement for all three datasets with correlation coefficient below 0,40. The monthly and yearly data gives better correlation with coefficient above 0,80. It shows that for time series event, the satellite data gives poor correlation with the area averaged rainfall. However, quantile mapping correction gives consistent cumulative distribution agreement for correction and model verification. The verification analysis shows an increase in coefficient correlation from 0.387 to 0.43 (daily) and 0.887 to 0.915 (monthly). In addition, there is a decrease in normalized root mean square error (NRMSE) from 31.03% to 16.91% (daily) and 46.03% to 14.03% (monthly). A decrease in normalized mean absolute error (NMAE) occurred from 16.12% to 9.74% (daily) and 31.67% to 10.07% (monthly). Beside that, the results of extreme rainfall analysis produce values that are very close to the rain station. So it is found that the PERSIANN CCS quantile mapping regression model is the best in improving data quality.

Comparison of the Efficacy of Omalizumab and Mepolizumab in Non-Steroidal Anti-Inflammatory Drug-Exacerbated Respiratory Disease

Introduction: Non-steroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD) is heterogeneous in both phenotypes and endotypes. Due to insufficient head-to-head comparison studies, it is hard to decide which biological to initiate. This study aimed to compare the efficacy of omalizumab and mepolizumab which can be used in the treatment of patients with severe eosinophilic asthma diagnosed with N-ERD. Methods: The population of this observational, cross-sectional study comprised of N-ERD patients who received omalizumab or mepolizumab for at least 6 months for severe asthma. Outcomes included the asthma control test (ACT), and sino-nasal outcome test scores (SNOT-22), blood eosinophil counts at initiation of biological treatment (T0, baseline) and at the end of 6th months (T6). Adverse effects related to biological treatment and changes of oral corticosteroids dose was recorded. Results: The study included a total of 22 patients, of whom 11 received mepolizumab and 11 received omalizumab. The change in ACT, SNOT-22, eosinophil counts, and adverse effects related to biologicals were similar at T6 (p = 0.606, p = 0.168, p = 0.05, p = 0.053, respectively). However, when examining the SNOT-22 and ACT based on the cumulative distribution curve (SUCRA), mepolizumab (SUCRA value: 0.61, 0.72, respectively) demonstrated greater efficacy compared to omalizumab (SUCRA value: 0.19, 0.35, respectively). The oral corticosteroids discontinuation rate was similar between the two groups (p = 0.05). Conclusion: We found both omalizumab and mepolizumab to be effective in treatment; however, we determined that mepolizumab may have a potential superiority in efficacy.

Association between GLIM-diagnosed malnutrition and quality of life in older patients with cancer

BackgroundOlder patients with cancer have a higher risk for malnutrition and impaired quality of life (QoL). The present study aimed to investigate the relationship between malnutrition diagnosed according to the Global Leadership Initiative Malnutrition (GLIM) criteria and QoL across various tumor types, and to evaluate the combined prognostic value of malnutrition and QoL in predicting survival among older patients with cancer. MethodsThis multicenter, observational cohort study included 5310 older patients with cancer and 2184 with malnutrition (moderate stage, n = 1023; severe stage, n = 1161). An empirical cumulative distribution curve was performed to illustrate the correlation between malnutrition and QoL. The primary objective was to investigate the association between malnutrition and QoL using logistic regression analysis. Survival analyses were performed to assess the combined prognostic value of malnutrition and QoL. ResultsThe median age of the patients (66.9% male, 33.1% female) was 70 years (interquartile range [IQR] 67–74 years) years. The median QoL score was highest in patients without malnutrition (91.88 [IQR 84.44–97.44]), followed by those with moderate (86.15 [IQR 76.18–93.85) and severe (82.31 [IQR 69.87–91.11]) malnutrition. Logistics regression revealed that the risk for developing impaired QoL increased 1.98 (95% confidence interval [CI] 1.64–2.38; P < 0.001) and 2.33 (95% CI 1.93–2.81; P < 0.001) times in patients with moderate and severe malnutrition, respectively. Kaplan–Meier curves showed that QoL in combination with GLIM criteria demonstrated a significant discriminative performance for survival and served as an independent prognostic factor among older patients with cancer, especially for lung and gastric cancers. ConclusionsMalnutrition diagnosed according to the GLIM criteria was a predictor of impaired QoL. Additionally, the combination of QoL and malnutrition demonstrated utility for predicting survival outcomes in older patients with cancer.

Optimal Planning of Energy Storage in Distribution Feeders Considering Economy and Reliability

The capacity of distributed photovoltaic impacts the safe and reliable operation of the distribution feeders. The energy storage is one solution for addressing that challenge. To balance the financial viability of investing in the energy storage projects in distribution feeders with grid reliability, an optimal planning method for energy storage considering economy and reliability is proposed. First, an economic model is established. Second, a grid reliability simulation is performed based on the economic optimization results of energy storage capacity in distribution feeders. Finally, considering the comprehensive indexes of economy and reliability, the technique for order preference by similarity to ideal solution method is used, and the final scheme is determined by the cumulative distribution curve. The simulation results show that it can effectively improve the distributed photovoltaic consumption capacity of the distribution feeders and better balance the financial viability of investing in energy storage considering reliability, which can be used as a reference for the reasonable determination of ratios of the energy storage for renewables.

A novel method for predicting coarse aggregate particle size distribution based on segment anything model and machine learning

Particle size distribution (PSD) is an important index property of granular materials. The conventional mechanical sieve analysis for PSD determination is labor-intensive, time-consuming, and inefficient. With the development of computer vision, image-based gradation prediction methods gradually emerge. However, previous studies mainly focused on images where individual aggregate particles are separated from each other without overlapping (i.e., not densely packed together), thus limiting their applicability. This study proposed a novel framework to predict the PSD of densely packed coarse aggregate particles, which is based on visual foundation model and machine learning with appropriate feature engineering. First, the segment anything model, which is one of the visual foundation models, was used for the instance segmentation of the densely packed aggregate particles. Then, the feature engineering was implemented with the segmentation result. The major quantitative indicators related to particle sizes were extracted from each mask, such as the area, the diameter of minimum enclosing circle, and the major axes of the equivalent ellipse of particle contours. Subsequently, the cumulative distribution curves of these indicators were plotted, followed by the data sampling operation on these curves to generate the feature vectors. The artificial neural network (ANN) model was then trained for gradation prediction with each of the feature vectors and the data points extracted from PSD curves used as the inputs and data labels, respectively. The model-training dataset consisted of 8160 different sets of digitally generated particle samples with known PSDs. Finally, verification test results of additional five different groups of digital particle samples and real coarse aggregate samples with known PSDs show that the root mean squared error (RMSE) of predicted aggregate sizes is 1.79 mm and 1.86 mm for digital and real aggregate particle samples, respectively. Therefore, the efficacy and feasibility of the proposed method was validated in terms of predicting the PSD of densely packed coarse aggregates from two-dimensional image information. As compared to the Mask RCNN combined with empirical formula of which the RMSE values were 5.12 mm and 5.84 mm for digital and real aggregate particle samples, respectively, the proposed framework demonstrates improved particle segmentation applicability and better PSD prediction accuracy.

NMR-based pore water distribution characteristics of silty clay during the soil compaction, saturation, and drying processes

The pore water distribution (PWD) of soil provides key information about soil shear strength, hydraulic conductivity, and water-retention ability. The PWD characteristics of the soil depend on the specimen preparation conditions (i.e., the variations in initial water content or compaction degree) and drying-wetting processes, which were investigated by using the nuclear magnetic resonance (NMR) technique. The NMR T2 spectrum is mathematically transformed into the PWD curve to more accurately quantify the variations in pore water content stored with pores of different radii. The pore water in silty clay can be categorized into strongly bound water, intra-aggregate pore water, and inter-aggregate pore water, which are identified by the pore radius of <0.05 μm, 0.05-1.0 μm and >1.0 μm, respectively. The effects of compaction degree and initial water content on the PWD of the soil exhibit distinctions. For unsaturated compacted specimens, the PWDs remain unaffected by changes in compaction degree, while an increase in initial water content promotes the formation of clay aggregates, thereby increasing the maximum water-holding pore radius and the intra-aggregate pore water content. For saturated compacted specimens, the inter-aggregate and intra-aggregate pore water content increases with a decrease in the compaction degree, while the changes in the PWD shape from unimodal to bimodal with an increase in the initial water content. During the soil drying process, inter-aggregate pore water is rapidly drained, the intra-aggregate dominant pore water content increases first and then decreases, and the strongly bound water content remains constant. The relationship between the cumulative pore water distribution curve and the soil-water characteristic curve (SWCC) can be established through the Young-Laplace theory. The SWCC can be well predicted by using the envelope of the cumulative pore water distribution curves during soil drying.

Enhancing parameter calibration for micro-simulation models: Investigating improvement methods

Calibrating microscopic traffic simulation models is a prerequisite for simulation applications. This study proposes three novel methods to improve the accuracy and interpretability of the calibration model. The proposed approach involves selecting the calibration parameter, refining the model parameter system, and optimizing the calibration results. The first method expands the single-point mean into a multi-point distribution. The cumulative distribution curve of delay was selected as the calibration parameter. The second method divides the parameter system into global and local parameters. Global parameters were calibrated using NGSIM measured data, and local parameters were calibrated through intelligent algorithms. The third method proposes a methodology of parameter clustering recursion based on the genetic algorithm results, with information entropy selected as the analysis index. To evaluate the effectiveness of the proposed optimization methods, this study used NGSIM trajectory data as a case study. Eight simulation schemes based on the three optimization methods were designed, and simulation experiments were conducted using the VISSIM platform. The results show that the accuracy of the multi-point distribution calibration and parameter value optimization method is significantly higher than the default method. Additionally, the optimization method with calibration of both global and local parameters was more consistent with actual driving characteristics. This study provides a theoretical foundation for improving the practical application of traffic simulation technology, which has significant implications for transportation planning and management.

Quantifying the Inverter-Interfaced Renewable Energy Critical Integration Capacity of a Power Grid Based on Short-Circuit Current Over-Limits Probability

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system’s short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined.

Estuarine environmental flow assessment based on the flow-ecological health index relation model: a case study in Yangtze River Estuary, China.

Environmental flow (e-flow) is the water demand of one given ecosystem, which can become the flow regulation target for protection and restoration of river or estuarine ecosystems. In this study, an e-flow assessment based on the flow-ecological health index (EHI) relation model was conducted to improve ecosystem health of the Yangtze River Estuary (YRE). Monitoring data of hydrology, biology, and water environment in the last decades were used for the model establishment. For the description of the YRE ecosystem, an EHI system was developed by cumulative frequency distribution curves and adaption of national standards. After preprocessing original flow values into proportional flow values, the generalized additive model and Monte Carlo random sampling were used for the establishment of the flow-EHI relation model. From the model calculation, the e-flow assessment results were that, in proportional flow values, the suitable flow range was 1.05-1.35, and the optimum flow range was 1.15-1.25 (flows in Yangtze River Datong Station). For flow regulation in two crucial periods, flows of 42,630-65,545 m3/s or over 14,675 m3/s are needed for the suitable flow of YRE in summer (June-August) or January, respectively. An adaptive management framework of ecological health-based estuarine e-flow assessment for YRE was contrived due to the limitation of current established model when facing the extreme drought in summer, 2022. The methodology and framework in this study are expected to provide valuable management and data support for the sustainable development of estuarine ecosystems and to bring inspiration for further studies at even continental or global levels.

Evaluation of succulent plants Echeveria elegans as a biomonitor of heavy metals and radionuclides

This work evaluates the use of Echeveria elegans as a biomonitor of metals and radionuclides, using semi-urban soils as a study area. The study area is exposed to various trace elements of concern for various social groups in nearby localities. The quantification of metals and radionuclides was performed by X-ray fluorescence spectrometry and gamma spectrometry, respectively. Cumulative frequency distribution curves, descriptive statistics, and multivariate analysis were used to estimate the local geochemical baseline and identify geochemical and anthropogenic patterns of metals and radionuclides from topsoil and E. elegans. The evaluation of contaminants and the contribution of possible exposure routes (topsoil and atmospheric deposition) was performed with the enrichment factor (EF) and the relative concentration factor (CFR). The results suggest that the plant does not present significant physical stress due to the environmental conditions to which it was exposed. Likewise, it can bioaccumulate heavy metals from natural and anthropogenic sources. The quantification of radionuclides in the plant is below the detection limits, indicating a low bioavailability and transfer factor. The CFR and EF results showed that the plant accumulates metals from the topsoil and atmospheric deposition. The bioaccumulation mechanism would be related to the functioning of Crassulaceae Acid Metabolism (CAM). In topsoil, the organic acids of the plant would modify the solubility of the metals present in an insoluble form in the soil, acting as ligands and, subsequently, following the transport route of these metabolites. In atmospheric deposition, the metals deposited in the leaves would be incorporated into the plant through the opening of the stomata because of the capture of CO2 (at night, day, or during environmental stress) by the CAM. Overall, the evidence showed that the succulent can be used as a biomonitor of heavy metals. However, additional studies are required to determine its usefulness as a radionuclide biomonitor.

Automatic detection and characterization of discontinuity traces and rock fragment size distribution using a digital image processing method

Rock materials naturally contain cracks and fractures, which may produce rock fragments or grains under geological and engineering conditions. Identification and characterization of the spatial extent of discontinuity traces and size distribution of fragments/grains are prerequisites for accurate geomechanical assessments of the stability of rocks containing natural or artificial discontinuities. This study introduces an advanced image processing methodology that leverages color gradient analysis and multi-threshold criteria to effectively detect and delineate fracture traces or boundary outlines. Furthermore, this approach facilitates the acquisition of precise fragment or grain size distribution statistics. To comprehensively characterize these discontinuity traces, the methodology employs the calculation of fractal dimensions alongside the use of an equivalent diameter conversion technique. These tools enable the quantification of the complexity and size distribution of the fragments or grains. The efficacy of this approach has been demonstrated through a series of case studies, encompassing a wide range of applications including digital images from numerical simulations, experimental tests, field observations, and micrographic analyses of rock structures. In these studies, the method proved capable of satisfactorily calculating the fractal dimensions of fractures, cracks, and contacts. Simultaneously, it accurately and efficiently determined the size distribution, as evidenced by frequency histograms and cumulative distribution curves obtained from the images. In essence, the proposed approach provides an automatic quantitative methodology to identify and characterize macroscopic and microscopic rock structures containing discontinuity traces or comprised of fragments/grains. This advancement holds significant potential for enhancing the precision and efficiency of geological and geomechanical assessments.

Evaluation of Subdaily Hydrological Regime Alteration Characteristics for Hydro–Photovoltaic Complementary Operation in the Upper Yellow River

The complementary operation of hydropower and photovoltaic power, aimed at meeting real-time demand, has led to frequent adjustments in power generation, causing significant fluctuations in hydrological systems and adversely affecting fish reproduction. The traditional hydrological regime alteration assessment index is based on index of hydrologic alternation (IHA) and mostly focuses on annual and daily runoff alterations. This study proposes a new set of indicators considering the characteristics of subdaily hydrological regime alterations, including magnitude, rate of change, duration, frequency, and timing. Using the hourly outflow from Longyangxia, an analysis of indicator redundancy was conducted. The alteration of the indicators before and after hydropower and photovoltaic operation was then analyzed using the cumulative probability distribution curve. Additionally, a concentration index was introduced to analyze the variations in hydrological impacts during different months. The results show that the hydro–photovoltaic complementary operation changed the subdaily natural flow regime, significantly increasing the rate of flow increase or decrease and the duration, with most indexes increasing by more than 100% compared with the natural flow regime. Furthermore, the concentration values of the indexes for the hydro–photovoltaic complementary operation were less than 10, indicating a more significant impact on the subdaily flow regime throughout the year. This research provides crucial data for mitigating ecological impacts under multi-source complementary scheduling.

Investigating paleoseismicity using high-resolution airborne LiDAR data - A case study of the Huangxianggou fault in the northeastern Tibetan plateau

Fault-offset landforms have long been recognized as holding important information about paleoseismic slip. Constructing an along-strike fault slip distribution could help reveal a fault's long-term rupture patterns and facilitate a more precise assessment of its future behavior. In this study, we documented the paleoseismic history of the Huangxianggou Fault, an oblique sinistral-thrust segment of the West Qingling Fault in the northeastern region of the Tibetan Plateau, using LiDAR-derived high-resolution DEM (0.1 m/pixel) and measurement of offset landforms. A total of 46 well-preserved and well-shaped landforms were chosen for horizontal and vertical displacement measurements, and the dense measurement data allow us to generate the cumulative offset probability distribution curves of horizontal and vertical displacements. Statistical analysis revealed at least seven surface-rupturing events, each with similar coseismic horizontal displacements of ∼7.0 m and vertical displacements of ∼0.7 m, in agreement with previous paleoseismic trench results. Using empirical relationships between moment magnitude and horizontal displacement, a plausible moment magnitude range of Mw7.3–7.7 was determined for these paleoearthquakes. Furthermore, by evaluating rupture parameters and fault slip rate, we determined a recurrence interval of 2.7–3.4 kyr for significant surface-rupturing events on this fault. The elapsed time since the last earthquake closely approaches this interval, suggesting a potential seismic risk for the Huangxianggou Fault. Our results emphasizes the importance of high-resolution topographic data in paleoseismic investigations, enabling the identification of numerous offset landforms and precise displacement measurements across faults. Paleoearthquake counts derived from horizontal and vertical displacement analyses were found to be consistent, highlighting the significance of vertical displacements in quantifying paleoearthquakes for strike-slip faults with a thrust component.

A crushing index for granular soils based on comminution energy consumption theory

Particle crushing is a common phenomenon in granular soils, which can affect the physical and mechanical properties of soils. The index which used to quantify the amount of particle crushing is crucial for the relevant research. Based on Kick's comminution energy consumption theory, size potential regarding as a property of soil and representing the energy of particles in a certain state was defined in this study, and a crushing index was proposed based on it. This index was determined according to a density distribution of particle size that voids the problem of sieving error propagation when using a cumulative grain size distribution curve. The validation of the proposed crushing index showed that it could quantitatively describe the amount of particle breakage, regardless of material, gradation, test type and particle size scale. In addition, the proposed crushing index was considered as a soil property index and could be divided into two categories: the ultimate crushing index and the mobilized crushing index, where the mobilized crushing index is obtained from the ultimate crushing index by replacing the ultimate breakage state with the final breakage state. The test results showed that the mobilized crushing index was equivalent to the input energy ratio between current and final breakage states. This relationship contributes to the understanding of the evolution of the grading curve caused by particle breakage.

Nuclear Magnetic Resonance and High-Speed Centrifugation for Gas-Water Relative Permeability Curve of Low-Permeability and Tight Sandstone

Gas-water relative permeability curve is the foundation of gas reservoir dynamic analysis and numerical simulation. It is generally obtained through gas displacing water experiment. Referring to the existing national standards, gas-water relative permeability test is carried out for low-permeability and tight sandstone. The flow is discontinuous and the curve smoothness is poor, which affects the application of gaswater relative permeability curve. Based on the comparative analysis of a large number of mercury injection and NMR experimental results of low-permeability and tight sandstone, a method to characterize the throat radius distribution of low-permeability tight sandstone based on NMR T2 spectrum is explored. Combined with high-speed centrifugal experiment, the calculation formulas of core water saturation, phase relative permeability of gas-water under different centrifugal force are established. A new method of “nuclear magnetic resonance + high-speed centrifugation” for gas-water relative permeability curve test of lowpermeability and tight sandstone is formed, and the test is carried out on Sulige low-permeability tight sandstone core. The results show that: (1) The shape of cumulative distribution curve of throat radius by mercury injection method is basically consistent with that of T2 relaxation time by NMR, and there is a good corresponding relationship between them. (2) The centrifugation process is similar to the gas displacing water process. With the decrease of water saturation, water centrifugation in rock core becomes more and more difficult until it become irreducible. (3) The core relative permeability curve obtained by the new method is consistent with the curve obtained by the standard method and has good repeatability. The new method can be used for gas-water relative permeability tests of low permeability tight sandstone. (4) The test process of the new method is unified and standard and up to 6 cores can be tested at the same time. The calculation results of water saturation and relative permeability curve are reliable and the integral curve is smooth, which greatly improves the test efficiency of gas-water relative permeability curve in lowpermeability and tight sandstone. The new method can be widely used to study the gas-water two-phase seepage law in low-permeability and tight sandstone gas reservoir.

Age estimation and boulder population analysis of the West crater at Apollo 11 landing site using Orbiter High Resolution Camera on board Chandrayaan-2 mission

Orbiter High Resolution Camera (OHRC) on-board Chandrayaan-2 had acquired a high-resolution image (∼0.26 m) covering Apollo 11 landing site, showing the Lander Module (LM) along with other small-scale features, such as boulders, etc. In the east of Apollo 11 LM lies the West crater, which has a rich population of boulders spread around it. We studied this boulder distribution around the West crater to estimate the age of the West crater and to understand the aspects related to the impact cratering and space weathering through the analysis of boulder distribution. For this, we identified and mapped >8500 boulders around the West crater using the OHRC image, out of which 6466 boulders (>∼1 m in size) lying within 1–5 crater radius were used for further analysis. For estimating the age of the West crater, we applied various methods based on crater morphology, boulder distributions and Diviner Rock Abundance. The age was estimated to be in the range of 80 Ma to 120 Ma with the most probable age close to 100 Ma, the same as that expected from the cosmic ray exposure dating. Boulder distribution around the West crater was found to be highly anisotropic with majority of boulders lying in the eastern to north-eastern direction, suggesting an impact at very low angle. Relationship between crater size and largest boulder size also pointed towards primary origin of the West crater. The Height (H) to Diameter (D) ratio is estimated to be ∼0.25 considering all the boulders with diameter ≤ 4m, going up to 0.38 with comparatively poor correlation for boulders with diameter > 4m. The H/D ratio from Apollo mission photos for boulders around Apollo 11 LM is found to be approximately double that of the corresponding measurements from the OHRC image. We also fit a Power law and the Weibull distribution to the cumulative Boulder Size Frequency Distribution (BSFD) curve with Maximum Likelihood (ML) method and Kolmogrov-Smirnoff (KS) statistics. Both fit well with R2 of 0.999, but varying minimum diameter (dKS) of 1.85 m and 1.64 m, respectively. The good fit by Power-law with high slope value (b = 4.61) describes the fragmentation due to the West crater forming impact as complex. However, the higher diameter tail was better explained by the Weibull fit implying sequential fragmentation of boulders with time.

A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links

AbstractCommercial microwave link (CML) has become one of the most widely used opportunistic sensors for rainfall monitoring. The high density and coverage of CMLs enable us to monitor near‐ground rainfall in high spatial resolution. The empirical power‐law (PL) model proposed in 1978 is the leading approach in relating CML attenuation with rain rate. However, while giving good rain rate estimates for CMLs longer than 1 km, in short CMLs, large errors are observed when the PL approach is applied. In this paper, we studied a statistical approach to convert CML attenuation measurement to rainfall intensity. The proposed approach calculates the exceedance probability, p, of rain‐induced attenuation and derives the corresponding rainfall intensity based on the rain rate cumulative distribution curve. Data from two cities in two countries were used to validate the approach. The results of the proposed approach were compared with that of the PL model. Results show that the models derived using our approach outperform PL for short CMLs, while showing similar performance for long ones. Since in the next generation mobile networks short CMLs will become more and more dominant, our work provides a way to derive retrieval models for the future generation CML networks.

Analysis of HepG2 cell response to a wide concentration range of mitomycin C using a multichannel quartz crystal microbalance system with a microscope

The morphological response of HepG2 cells to mitomycin C was analyzed using a multichannel quartz crystal microbalance system equipped with a home-built movable microscope that enables the simultaneous acquisition of cell images and measurements of eight-channel quartz crystal microbalance. After 24 h of cell seeding, mitomycin C was injected into the culture medium. During the attachment process, the resonant frequency decreased, and the curves fitted well with the first-order lag response. Analysis of the response to mitomycin C revealed that the resonant frequency response curves varied with mitomycin C concentration. When the mitomycin C concentration was <10 μmol L−1, the delay time was observed before the increase in resonant frequency. When the mitomycin C concentration was extremely low, an additional decrease in resonant frequency was observed in the middle of the delay time that fitted well with the cumulative log-normal distribution curve. The resonant frequency response curves after the delay time fitted well with the cumulative log-normal distribution curves. The delay time and mean cumulative log-normal distribution time for the increase in resonant frequency correlated with the mitomycin C concentration; however, the mean time for the additional decrease in the resonant frequency did not show a statistically significant difference as a function of mitomycin C concentration. For mitomycin C concentrations of >20 μmol L−1, the response to the change in resonant frequency was rapid, and the response curves fitted well with the first-order lag response. The first-order lag response indicates that the response occurred simultaneously for all cells. The results showed that the time constant was independent of the tested mitomycin C concentration between 20 and 100 μmol L−1. These results suggested that different cell death processes occurred by mitomycin C. The findings of this study suggest that the system can be used to investigate cell death in adherent cells.