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Analysis of contact stresses in tapered roller bearings (TRBs) is an important subject in mechanical systems that involve moving and rotating parts. This is because the damage resulting from contact stresses between races and rolling elements may lead to failure and long hours of down time. To make an accurate estimation of TRB performance, it is necessary to understand the stresses experienced at the contact surfaces. In this study stresses generated by combined radial and axial loads in TRB are investigated using photoelastic experimental hybrid method. A comparison between stress behaviour of TRBs and angular contact ball bearings (ACBBs) is then made. The critical areas of stress concentrations were identified. It was found that the stresses σx and σy on races of a TRB were much lower than those on ACBB. Analysis of shear stresses showed that τxy on the TRB were more than two times those on ACBB. This difference in magnitude was explained on the basis of the orientation of rolling elements. The study also showed that under combined axial and radial loading conditions, the critical areas of stress concentrations for TRB were mainly regions where the race thickness was smallest.

Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of the Yamuna River, India, and physiochemical parameters were examined for 5 years to simulate the DO using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system–grid partitioning (ANFIS–GP) and subtractive clustering (ANFIS–SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict DO. Results obtained from the models were evaluated using root mean square error and coefficient of determination (R2) to identify the appropriate combination of parameters to simulate the DO. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS–GP outperforms the ANFIS–SC. M4 generated R2 of 0.953 from ANFIS–GP compared to 0.911 from ANFIS–SC. HIGHLIGHTS ANFIS models were designed to predict the DO of urban steam.; Fuzzy logic allows the classification, data mining, interpretation and optimization of time series data.; Simulation was performed using ANFIS–grid partitioning (ANFIS–GP) and ANFIS–subtractive clustering (ANFIS–SC).; The extensive formulation of the rule base helps identify vital parameters and improves the accuracy of the model.

Multispectral satellite remote sensing imagery, together with appropriate modeling, have been proven to provide chlorophyll-a maps that are useful to evaluate the suitability of coastal areas for carrying out shellfish aquaculture. However, current approaches used for chlorophyll-a estimation in very shallow coastal areas often fail in their accuracy. To overcome this limitation, an algorithm that provides an accurate estimation of chlorophyll-a concentration in the coastal areas of the Ebro delta (North Western Mediterranean) using atmospherically corrected Sentinel 2 (S2) remote sensing reflectances (Rrs) has been calibrated and validated. The derived chlorophyll-a maps created have been used in a dynamic carrying capacity model that covers areas from very rich waters inside the embayment to the more oligotrophic waters in the open sea. The use of carrying capacity models is recommended to evaluate the potential of marine coastal areas for bivalve mollusk aquaculture. In this context, the depletion of chlorophyll-a is an indicator of negative environmental impact and thus a continuous monitoring of chlorophyll-a is key. The proposed methodology allows estimation of chlorophyll-a concentration from Sentinel-2 with an accuracy higher than 70% in most cases. The carrying capacity and the suitability of the external areas of the Ebro delta have been determined. The results show that these areas can hold a significant mussel production. The methodology presented in this study aims to provide a tool to the shellfish aquaculture industry. info:eu-repo/semantics/publishedVersion

AIM: The manufacturers' recommended methods to calculate delivered activities in liver radioembolization are simplistic and only slightly personalized. Activity planning could also be based on a 99mTc-macroaggregated albumin SPECT/CT (MAA) using the partition model but its accuracy is controversial. This study evaluates the dose parameters in the normal liver and in the tumor compartments using MAA SPECT/CT (pre-therapeutic imaging) and 90Y TOF-PET/CT (post-therapy imaging). Finally, we propose a prescription of the activity as a function of the normal liver MAA distribution. METHOD: 66 procedures of RE (with resin microspheres) corresponding to 171 lesions were analyzed. Tumor to normal targeted liver uptake (T/NTL), tumor absorbed dose (TD) and whole normal liver absorbed (WNLD) were assessed with MAA and 90Y imaging. Secondly, activities were recalculated using the MAA distribution in the normal liver compartment to reach the maximal tolerable liver dose. These Activities were compared to activities defined with the BSA method. RESULTS: Compared to 90Y imaging, our study demonstrated an accurate estimation of the WNLD using MAA imaging (Pearson's R = 0.97, p < 0.001). On the contrary, significant variations were found for TD (R = 0.65, p < 0.001). The MAA T/NTL ratio has a 85% positive predictive value in identifying patients who will get a 90Y T/NTL ratio above 1.5. Moreover, activities calculated using the MAA distribution in the normal liver compartment were significantly higher to activities defined with the BSA method. CONCLUSION: Whole normal liver absorbed doses are accurately predicted with MAA imaging and could be used to optimize the activity planning.

Accurate estimation of the preconsolidation stress and overconsolidation ratio, OCR, in uniform cohesive soils using the field vane test is influenced by the combined effects of soil type and plasticity, geologic history, soil structure, and the presence of organic matter. Published empirical correlations for cohesive soils consider the effects of soil type and plasticity, but significant variability can occur with changes in soil structure and the presence of organic matter content. The adaption of the “stress history and normalized soil engineering properties” (SHANSEP) format improves the characterization of overconsolidated soils using field vane tests by applying a proposed empirical approach to identify structured soils from unstructured soils and updating the SHANSEP-based approach to separately evaluate stress history in structured and unstructured soils. Validation of the correlation coefficients for individual projects is needed as the approach is applied to new geologic materials and with potentially different field vane equipment and laboratory testing procedures used to characterize the soils. This additional testing provides an opportunity to improve the correlations for specific conditions and reduce the variability in current OCR assessments.

The present paper aims to assess the existing shortfalls in the model used for the computation of the Global Entrepreneurship Index (GEI) especially for economies that have not participated in the Global Entrepreneurship Monitor (GEM) annual surveys. Due to the lack of participation of some countries in these annual surveys, the authors of the GEI try to estimate the individual factors of the GEM non-members by employing data from member countries called “nearby and similar countries.” Using some examples and several statistical robust methods, this paper demonstrated that the estimated individual factors of non-member countries, especially in underdeveloped countries, will lead to an unavoidable error. Likewise, it was showed that, due to the unpredictable nature of individual factors, the use of the previous data is not justifiable. Additionally, this study showed that the use of the arithmetic mean (AM) will overestimate the individual factors for non-member countries, while the geometric mean (GM) has the capability of accurate estimation. Finally, using statistical procedures such as cluster analysis, non-parametric methods, and model selection methods, it was shown that the GEI values are divided into two different clusters. It seems that the main reason for this distinction was a dissimilarity between the GEI amount in the GEM members and non-members, which may be due to employing incorrect estimators and the underlying methods of the GEI.

The infectiousness of COVID-19 is high among the susceptible population, making the calculation of the reproduction number (R) an essential step to implement preventive measures. We aim to estimate COVID-19 transmission to determine if the disease is successfully controlled or extra measured should be adopted to attain this goal. The daily incidence data of COVID-19 in Saudi Arabia from March 2nd, 2020, to April 4th, 2021, were obtained from the continuously updated Saudi Ministry of Health COVID-19 repository. To get accurate estimation of the situation over the last 4 months (from December 1st, 2020, to April 4th, 2021), we calculated the weekly (every 7 days) R starting from March 2nd, 2020, and till the last week of the available data. The calculated values of R were represented as median, first quantile (Q1), and third quantile (Q3). As early as the first week of December 2020, the median R was 0.81 (0.80–0.83) which means that each existing infected case would transmit infection to only one person. This was followed by fluctuations over the next few weeks around R value of 1, reaching its highest level of 1.45 (1.42–1.47) between December 31st, 2020, and January 6th, 2021. This was followed by a relatively steady decline over the following weeks, with some till mid-March where the R values started to slightly rise again. Social distancing, protective precautions, avoiding abuse of the partial lifting, expanding the screening process, and other Saudi measures sound to be successful and should be replicated in similar communities. This measure should be continued till the vaccination process is completed, to reduce the number of contacts and to avoid uncontrolled transmission of the disease.

Diversification rates vary across species as a response to various factors, including environmental conditions and species-specific features. Phylogenetic models that allow accounting for and quantifying this heterogeneity in diversification rates have proven particularly useful for understanding clades diversification. Recently, we introduced the cladogenetic diversification rate shift model, which allows inferring multiple rate changes of small magnitude across lineages. Here, we present a new inference technique for this model that considerably reduces computation time through the use of data augmentation and provide an implementation of this method in Julia. In addition to drastically reducing computation time, this new inference approach provides a posterior distribution of the augmented data, that is the tree with extinct and unsampled lineages as well as associated diversification rates. In particular, this allows extracting the distribution through time of both the mean rate and the number of lineages. We assess the statistical performances of our approach using simulations and illustrate its application on the entire bird radiation.[Birth–death model; data augmentation; diversification; macroevolution.]

Southern China is a major rice-producing region where mixed rice cropping systems (single cropping rice (SCR) and double-cropping rice (DCR)) are widely practiced. An accurate estimation of the spa...

We consider sums of the form $\sum \phi(\gamma)$, where $\phi$ is a given function, and $\gamma$ ranges over the ordinates of nontrivial zeros of the Riemann zeta-function in a given interval. We show how the numerical estimation of such sums can be accelerated by a simple device, and give examples involving both convergent and divergent infinite sums.

Background: The assessment and classification of burin severity and extent is very important for proper treatment. A computer system to assess burns seems to be crucial for the determination of fluid and nutritional requirements. The aim of the work: The objectives of this work are to design a computer system to assess and classify burns according to burn depth and extent and to implement the designed system for the assessment of fluid and nutritional needs using a database system for the automation of the entire process. Then test the computer system in reality through clinical application of the system on fifty patients with acute burn injury. Patients and Methods:This study was divided into two parts: Development of a burn management computer system [BMS]to facilitate the process of documentation, classification, and management of acute burn patients. The BMS contains three subsystems: Database, Classification, and Management subsystems. The second part was the clinical application of the system on fifty patients to test the efficacy, accuracy, and applicability of this system in comparison to the traditional method used at our unit. Results:Clinical application of the BMS has showed a reduction in the burn percentage calculation with 7.36%. It also decreased the amount of fluid calculated automatically by the system by about 7.28%, while the reduction in nutrition calculated automatically by the BMS was about 2.7%. Burn Management System has showed an overall shorter time in dealing with the cases. Conclusion: Clinical application of the BMS showed an accurate estimation of the burn percent, accurate and rapid calculation of resuscitation fluids and nutritional needs, and easy electronic documentation of patients’ data that required a minimal effort.

Finite-time stability (FTS) problem of nonlinear impulsive time-varying systems is studied in this paper. Some improved Lyapunov theorems on uniform FTS including settling-time estimation are obtained based on a time-varying differential inequality, in which the derivative of Lyapunov function is not necessarily negative definite. Especially, the stabilizing and destabilizing impulse effects on FTS and settling-time are investigated by developing a unified technique, such that the magnitude of impulses, the number of impulses and specific distribution of impulse times may be discussed in detail by introducing proper parameters. Furthermore, more accurate estimation of settling-time can be derived by using these parameters. Finally, two numerical examples are provided to validate the obtained results.

Purpose of the review Robotics is a rapidly advancing field, and its introduction in healthcare can have a multitude of benefits for clinical practice. Especially, applications depending on the radiologist’s accuracy and precision, such as percutaneous interventions, may profit. This paper provides an overview of recent robot-assisted percutaneous solutions. Recent findings Percutaneous interventions are relatively simple and the quality of the procedure increases a lot by introducing robotics due to the improved accuracy and precision. The success of the procedure is heavily dependent on the ability to merge pre- and intraoperative images, as an accurate estimation of the current target location allows to exploit the robot’s capabilities. Summary Despite much research, the application of robotics in some branches of healthcare is not commonplace yet. Recent advances in percutaneous robotic solutions and imaging are highlighted, as they will pave the way to more widespread implementation of robotics in clinical practice.

We are concerned with testing replicability hypotheses for many endpoints simultaneously. This constitutes a multiple test problem with composite null hypotheses. Traditional p -values, which are computed under least favorable parameter configurations (LFCs), are over-conservative in the case of composite null hypotheses. As demonstrated in prior work, this poses severe challenges in the multiple testing context, especially when one goal of the statistical analysis is to estimate the proportion π0 of true null hypotheses. Randomized p -values have been proposed to remedy this issue. In the present work, we discuss the application of randomized p -values in replicability analysis. In particular, we introduce a general class of statistical models for which valid, randomized p -values can be calculated easily. By means of computer simulations, we demonstrate that their usage typically leads to a much more accurate estimation of π0 than the LFC-based approach. Finally, we apply our proposed methodology to a real data example from genomics.

According to Fisher's principles, an experimental field is typically divided into multiple blocks for local control. Although homogeneity is supposed within a block, this assumption may not be practical for large blocks, such as those including hundreds of plots. In line evaluation trials, which are essential in plant breeding, field heterogeneity must be carefully treated, because it can cause bias in the estimation of genetic potential. To more accurately estimate genotypic values in a large field trial, we developed spatial kernel models incorporating genome-wide markers, which consider continuous heterogeneity within a block and over the field. In the simulation study, the spatial kernel models were robust under various conditions. Although heritability, spatial autocorrelation range, replication number, and missing plots directly affected the estimation accuracy of genotypic values, the spatial kernel models always showed superior performance over the classical block model. We also employed these spatial kernel models for quantitative trait locus mapping. Finally, using field experimental data of bioenergy sorghum lines, we validated the performance of the spatial kernel models. The results suggested that a spatial kernel model is effective for evaluating the genetic potential of lines in a heterogeneous field.

We propose herein a method for estimating the mixing state of the contents of a peristaltic continuous mixing conveyor simulating the intestine, developed for mixing and conveying powders and liquids. This study serves to improve a previously proposed method for estimating the mixing state using a logistic regression model with the pressure and flow rate sensors installed in the device as inputs. Moreover, the estimation accuracy of the proposed method is better than that of the previous method. The generalizability of the proposed method is evaluated for four conditions in which the feeding order of the contents, powder, and liquid are changed. The feeding order is as follows: powder first, liquid first, and powder and liquid alternately. As a result, a highly accurate estimation of mixing is achieved under the condition wherein the powder component is in the unit adjacent to the lid, but not under the condition wherein the liquid component is fed first. It is speculated that this is because the movement of the powder component inside the device is more easily reflected by the pressure and flow rate sensors installed in the device than in the liquid component.

During an epidemic, accurate estimation of the numbers of viral infections in different regions and groups is important for understanding transmission and guiding public health actions. This depends on effective testing strategies that identify a high proportion of infections (that is, provide high ascertainment rates). For the novel coronavirus SARS-CoV-2, ascertainment rates do not appear to be high in most jurisdictions, but quantitative analysis of testing has been limited. We provide statistical models for studying testing and ascertainment rates, and illustrate them on public data on testing and case counts in Ontario, Canada.

Accurate estimation of nontechnical losses (NTL) requires the real-time data acquisition of different smart meters to be accurately synchronized at a common time reference. However, real-time data reported by smart meters are usually not accurately synchronized. This article proposes a new quantity, to be measured by the smart meters that will address the problem of time synchronization. The proposed measurement quantity is also utilized to propose a modified coding-based algorithm to detect and correct NTL in distribution systems. It is worth mentioning that the measurement of the proposed quantity does not require any additional hardware, and commercially available smart meters can be reprogrammed to perform the new functionality. The proposed algorithm along with the proposed measurement quantity is tested and compared to a benchmark coding-based algorithm. The proposed algorithm is shown to outperform the benchmark algorithm in practical scenarios.

Background The accurate estimation of temporal patterns of influenza may help in utilizing hospital resources and guiding influenza surveillance. This paper proposes functional data analysis (FDA) to improve the prediction of temporal patterns of influenza. Methods We illustrate FDA methods using the weekly Influenza-like Illness (ILI) activity level data from the U.S. We propose to use the Fourier basis function for transforming discrete weekly data to the smoothed functional ILI activities. Functional analysis of variance (FANOVA) is used to examine the regional differences in temporal patterns and the impact of state's political orientation. Results The ILI activity has a very distinct peak at the beginning and end of the year. There are significant differences in average level of ILI activities among geographic regions. However, the temporal patterns in terms of the peak and flat time are quite consistent across regions. The geographic and temporal patterns of ILI activities also depend on the political make-up of states. The states affiliated with Republicans had higher ILI activities than those affiliated with Democrats across the whole year. The influence of political party affiliation on temporal pattern is quite different among geographic regions. Conclusions Functional data analysis can help us to reveal the temporal variability in average ILI levels, rate of change in ILI levels, and the effect of geographical regions. Consideration should be given to wider application of FDA to generate more accurate estimates in public health and biomedical research.

Nowadays a considerable percentage of trains mature delays due to nodes and stations congestion. They are normally a combination of effects of routes conflicts in stations on lines and propagation in stations of delays suffered along the lines. In order to prevent the conflicts and minimize delays, desirable timetables should be robust; accordingly, an accurate estimation during timetable design and implementation is necessary. Goal of the research is to compare analytical and simulation methods for the assessment of railway lines and nodes capacity to pinpoint their mutual effects, analyze stability or variability of the obtained results achieved by traditional and innovative approaches. The work is included in a research framework with the final goal of optimizing the use of railway network capacity. In order to tackle the purpose, the paper introduces synthetically the methods and applies them systematically to a complex network, including single and double track lines and various typologies of stations.

Being largely dependent on agriculture, the overall development and water resource management of Bangladesh are greatly influenced by the accurate estimation of precipitation. Different satellite derived precipitation products, covering a large area, are very useful for rainfall estimation. In this study, three different satellite precipitation datasets namely Climate Prediction Center MORPHING (CMORPH), the global Satellite Mapping of Precipitation (GSMaP), and the Integrated Multi-satellitE Retrievals for GPM (IMERG) daily data are spatially analyzed and compared with the observed rainfall data from 20th May 2020 to 21st May 2020. It is observed that the satellite products matched well with the observed data set but the amount varied. Also, the spatial distribution of CMORPH and GSMaP with the observed precipitation is represented for 02 May 2019.

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations is critical for improving patient outcomes in cancer cases. However, accurate estimation of mutational effects at the pathway level for such patients remains an open problem. While probabilistic pathway topology methods are gaining interest among the scientific community, the overwhelming majority do not account for network perturbation effects from multiple single-nucleotide alterations. Here we present an improvement of the mutational forks formalism to infer the patient-specific flow of signal transduction based on multiple single-nucleotide alterations, including non-synonymous and synonymous mutations. The lung adenocarcinoma and skin cutaneous melanoma datasets from TCGA Pan-Cancer Atlas have been employed to show the utility of the proposed method. We have comprehensively characterized six mutational forks. The number of mutated nodes ranged from one to four depending on the topological characteristics of a fork. Transitional confidences (TCs) have been computed for every possible combination of single-nucleotide alterations in the fork. The performed analysis demonstrated the capacity of the mutational forks formalism to follow a biologically explainable logic in the identification of high-likelihood signaling routes in lung adenocarcinoma and skin cutaneous melanoma patients. The findings have been largely supported by the evidence from the biomedical literature. We conclude that the formalism has a great chance to enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease.

I read with interest an article “Updated meta-analysis of pancreatic stent placement in preventing post-endoscopic retrograde cholangiopancreatography pancreatitis” by Fan and colleagues in World J Gastroenterol 2015; 21(24): 7577-7583. Although I appreciate their work, I have found problems with the data extracted and analyzed by the authors, and will give my comment in this letter. It would be valuable if the authors could provide an accurate estimation of their extracted data.

One of the key goals of microseismic processing is accurate estimation of the source location. Using full-waveform information in passive-source data sets can potentially delineate microseismic sources. The accuracy of the compressional-wave and shear-wave velocities has a strong influence on the estimation of source locations and hence the reliability of the fracture detection. We have adopted a methodology for passive source and velocity inversion, in which the conventional source term of the elastic wave equation is represented by an equivalent source. The equivalent source term is composed of source images and source functions because it is inspired by elastic reflection waveform inversion. Thus, we update the source locations, source functions, and velocities simultaneously by using a waveform inversion scheme. In the 2D isotropic case, the source terms are defined by two source image components and three source function components. They provide an alternative representation of the source mechanism, usually defined by the moment tensor. Waveform inversion of passive events has severe nonlinearity due to the unknown source locations in space and their functions in time. We have thus used a source-independent objective function, based on convolving reference traces with modeled and observed data, to avoid cycle skipping caused by the unknown sources. We first synthetically examined our method on a modified Marmousi model. Then, by applying a nested inversion for these variables, our method also produces good estimation of the source and background velocity for real microseismic monitoring data. We use a ball-drop event to test the accuracy because the inverted source location should match the ball-seat location. For the uncontrolled events, the estimated source distribution using waveform inversion agrees with the local stress potential information. Although our method has a higher computational cost than traveltime- or migration-based methods, the estimated event locations have significantly improved accuracy.

Process-based crop growth models have become indispensable tools for investigating the effects of genetic, management, and environmental factors on crop productivity. One source of uncertainty in crop model predictions is model parameterization, i.e. estimating the values of model input parameters, which is carried out very differently by crop modellers. One simple (SSM-iCrop) and one detailed (APSIM) maize (Zea mays L.) model were partially or fully parameterized using observed data from a 2-year field experiment conducted in 2016 and 2017 at the UFT (Universitäts- und Forschungszentrum Tulln, BOKU) in Austria. Model initialisation was identical for both models based on field measurements. Partial parameterization (ParLevel_1) was first performed by estimating only those parameters related to crop phenology. Full parameterization (ParLevel_2) was then conducted by estimating parameters related to phenology plus those affecting dry mass production and partitioning, nitrogen uptake, and grain yield formation. With ParLevel_1, both models failed to provide accurate estimation of LAI, dry mass accumulation, nitrogen uptake and grain yield, but the performance of APSIM was generally better than SSM-iCrop. Full parameterization greatly improved the performance of both crop models, but it was more effective for the simple model, so that SSM-iCrop was equally well or even better compared to APSIM. It was concluded that full parameterization is indispensable for improving the accuracy of crop model predictions regardless whether they are simple or detailed. Simple models seem to be more vulnerable to incomplete parameterization, but they better respond to full parameterization. This needs confirmation by further research.

The most challenging aspect of rebuilding the puzzle is finding out the right pair of pieces of the image. To do this, we need, an accurate estimation of the pairwise compatibility measure between local patches and the assembly strategy. In this paper, we propose a novel pairwise compatibility measure for the assembly of computational square jigsaw puzzles using Gist and color distance. This possible combination of gradient and color features improves the assembling pieces and can deal with the majority of the problems encountered in state-of-the-art. We also propose the rotation-based strategy to enable working on multiple parts and rebuild completed puzzles from local matching candidates. The experimental results on the frequently used data-sets sufficiently show that the suggested compatibility metric surpasses the latest approaches.

Various compositional parameters are derived using intensity ratios and integral area ratios of different spectral peaks and bands in the Raman spectrum of bone. The $$\nu $$ ν 1-, $$\nu $$ ν 2-,$$\nu $$ ν 3-, $$\nu $$ ν 4 PO43−, and $$\nu_{1} $$ ν 1 CO32− bands represent the inorganic phase while amide I, amide III, Proline, Hydroxyproline, Phenylalanine, δ(CH3), δ(CH2), and $$\nu $$ ν (C–H) represent the organic phase. Here, using high-resolution Raman spectroscopy, it is demonstrated that all PO43− bands of bone either partially overlap with or are positioned close to spectral contributions from the organic component. Assigned to the organic component, a shoulder at 393 cm−1 compromises accurate estimation of $$\nu $$ ν 2 PO43− integral area, i.e., phosphate/apatite content, with implications for apatite-to-collagen and carbonate-to-phosphate ratios. Another feature at 621 cm−1 may be inaccurately interpreted as $$\nu $$ ν 4 PO43− band broadening. In the 1020–1080 cm−1 range, the ~ 1047 cm−1$$\nu $$ ν 3 PO43− sub-component is obscured by the 1033 cm−1 Phenylalanine peak, while the ~ 1076 cm−1$$\nu $$ ν 3 PO43− sub-component is masked by the $$\nu $$ ν 1 CO32− band. With $$\nu $$ ν 1 PO43− peak broadening, $$\nu $$ ν 2 PO43− integral area increases exponentially and individual peaks comprising the $$\nu $$ ν 4 PO43− band merge together. Therefore, $$\nu $$ ν 2 PO43− and $$\nu $$ ν 4 PO43− band profiles are sensitive to changes in mineral crystallinity.

Hydraulic fracturing has been widely applied to stimulate the natural gas and oil production from unconventional reservoirs. To optimize the design of hydraulic fracturing in this application, an accurate estimation of the initiation and propagation of hydraulic fractures is indispensable. However, it still remains challenging as a result of the complex stress state and geological conditions. On account of their ability to complete control some significant factors and efficient observation of fracture geometry, laboratory-scale hydraulic fracturing experiments have received abundant research attention in recent years. This paper presents a review of the state of the art of laboratory-scale hydraulic fracturing experiments, focusing on the scaling analysis, experimental setup, fracturing fluids, and sample preparation. A discussion of the directions for future research is also provided with the intention of stimulating the development of the experimental technique for investigating hydraulic fracturing.

Accurate estimation of the partial atomic charges on metal centers is useful for understanding electronic and catalytic properties of materials. However, different methods of calculating these charges may give quite different results; this issue has been more widely studied for molecules than for solids. Here we study the charges on the metal centers of a test set of 18 solids containing transition metals by using density functional theory with several density functionals (PBE, PBE+U, TPSS, revTPSS, HLE17, revM06-L, B3LYP, B3LYP*, and other exchange-modified B3LYP functionals) and four charge models (Bader, Hirshfeld, CM5, and DDEC6). The test set contains 12 systems with nonmagnetic metal centers (eight metal oxides (MO

Timing group delay (TGD) is an important parameter that affects the positioning performance of global navigation satellite systems (GNSS). The BeiDou navigation satellite system (BDS) broadcasts TGD corrections from B3I frequency to B1I and B2I frequencies, namely TGD1 and TGD2. On July 21, 2017, BDS updated TGD values with a maximum change of more than 4 ns. In this contribution, we explain the motivation for the BDS TGD update, which is due to the systematic bias between narrowly correlated and widely correlated pseudo-ranges in BDS monitoring receivers. To investigate the impact of the updated TGD, BDS signal-in-space range error (SISRE) and user positioning performance regarding single point positioning (SPP) and precise point positioning (PPP) are analyzed. Results show that after the update of TGD, the difference between the new TGD and multi-GNSS experiment (MGEX) differential code bias (DCB) decreases from 1.38 ns to 0.29 ns on TGD1 and from 0.40 ns to 0.25 ns on TGD2. With the contribution of more accurate TGD, the systematic bias of BDS radial SISRE no longer exists, and the overall BDS SISRE also reduces from 1.33 m to 0.87 m on B1I/B2I frequency, from 1.05 m to 0.89 m on B1I frequency, from 0.92 m to 0.91 m on B2I frequency, respectively, which proves the similar precision of BDS TGD and MGEX DCB. One week of statistical results from 28 globally distributed MGEX stations shows that the SPP performance improves on non-B3I frequencies after the TGD update, with a maximum improvement of more than 22% for the B1I/B2I or B1I/B3I combination. The new TGD mainly reduces SPP positioning bias in the East component. The updated TGD also slightly improves the PPP convergence performance for the B1I/B3I combination, but mostly contributes to a more accurate estimation of the receiver clock and ambiguities.

Accurate estimation of evapotranspiration (ET) is an important factor in water management, especially in irrigated agriculture. Accurate irrigation scheduling requires accurate estimation of ET. The objective of this study was to estimate the actual evapotranspiration (ETa) by the pySEBAL model and to compare it with the actual evapotranspiration measured by the lysimeter method of soybean crop in Adana, Turkey. Five Landsat 5 Thematic Mapper (TM) images and weather data were used for this study to estimate actual evapotranspiration by the pySEBAL model. The results showed a good relationship between ETa estimated by the pySEBAL model and ETa measured by the lysimeter method, with an R2 of 0.73, an RMSE of 0.51 mm.day−1, an MBE of 0.04 mm.day−1 and a Willmott’s index of agreement (d) of 0.90. Based on this study, there is a good relationship between the actual evapotranspiration estimated by the pySEBAL model and the actual evapotranspiration measured by the lysimeter method. Consequently, ETa of soybean crop can be estimated with high accuracy by the pySEBAL model in Adana, Turkey.

In data assimilation for NWP, accurate estimation of error covariance matrices (ECMs) and their use are essential to improve NWP accuracy. The objective of this study is to estimate ECMs of all observations and background variables using sampling statistics, and improve global NWP accuracy by using them. This study presents the first results of such all ECM refinement. ECM diagnostics combining multiple methods, and analysis and forecast cycle experiments were performed on the JMA global NWP system, where diagonal components of all ECMs and off-diagonal components of radiance observations are refined. The ECM diagnostic results are as follows: 1) the diagnosed error standard deviations (SDs) are generally much smaller than those of the JMA operational system (CNTL); 2) interchannel correlations in humidity-sensitive radiance errors are much larger than 0.2; and 3) horizontal correlation distances of AMSU-A are ~50 km, excluding channel 4. The experimental results include the following: 1) the diagnosed ECMs generally improve forecast accuracy over CNTL even without additional tunings; 2) the supplemental tuning parameter, which is the deflation factor (0.6 in SD) applied for the estimated ECMs of nonsatellite conventional data and GPS radio occultation data, statistically significantly improves forecast accuracy; 3) this value 0.6 is set as the same value as the ratio of the estimated background error SD to that in CNTL; 4) high-density assimilation (10 times) of AMSU-A is better than CNTL, not better than that with 5 times; and 5) ECMs estimated using boreal summer data can improve forecast accuracy in winter, which indicates their robustness.

In order to evaluate the total shale gas-in-place (GIP) resources in deep formations, it is important to study the sorption of supercritical methane in shales. At present, the Dubinin–Astakhov model is used to describe sorption isotherms. However, it still has some shortcomings. The main objective of this study is to establish an optimized model for supercritical methane sorption in shales. A series of high-pressure methane sorption isotherms were measured at different temperatures (from 293 to 333 K) for shale samples collected in the Cengong block, Guizhou, China. The characteristics and causes of shale gas sorption capacity changes were analyzed. By comparing the fitting results of several conventional sorption models, the characteristics and applicable scope of these models are obtained. A four-parameter (V0, D, m, and ρa) modified supercritical D–A model was developed to accurately estimate the sorption of supercritical methane on shales based on Polanyi sorption potential theory. The results show that the sorption characteristic curve of methane on the shale surface under high pressure is obviously different from that under low pressure. The density of the sorption phase and the virtual saturated vapor pressure have a great influence on the fitting results of the sorption models. The density of the adsorption phase directly determines the ultimate sorption capacity of the shale sample. Also, the modified D–A model can improve the accuracy of the prediction of supercritical methane sorption on shales, and it can accurately describe the isothermal sorption law of gas in the supercritical state.

This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground, independent of theoretical hypotheses and engineering experience. A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground. Then, an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile. The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles, not only under the ultimate load but also under the working load. Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas.

BackgroundThree commonly used non-invasive protocols are implemented to estimate the timing at which PHV most likely occurs. Accurate estimation of circumpubertal years can aid in managing training load of adolescent athletes.AimThree protocols were compared against observed age at PHV: an estimate of 13.8 ± 1.0 years – generic age at PHV (from longitudinal measures); an estimate based on the maturity offset equation, predicted age at PHV ±1.0 year; a window of PHV based on 85–96% of predicted adult height at time of observation.Subjects and methodsA final sample of 23 (from 28) adolescent male participants were selected from the academy of an English Premier League club. Anthropometric measures were collected across five playing seasons; age at PHV was estimated with Super-Imposition by Translation and Rotation (SITAR). The three protocols were compared based on measures at 13.0 years.Results and Conclusions: An age window based on predicted maturity offset did not improve estimation of PHV compared to generic age method; however, the percentage of predicted adult height window showed improvement in performance shown by the following results. Predicted age at PHV correctly assigned 15 participants (65%) as experiencing PHV, while the percentage height correctly assigned 17 participants (74%). Generic age and predicted age at PHV correctly predicted observed age at PHV for 14 participants (61%), percentage of adult height window correctly predicted 22 participants (96%).