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Cement has been widely used in civil engineering, whose quality directly affects the safety of buildings. Cement compressive strength, as an important quality indicator, its accurate estimation has significant implications for quality inspections and the design of high-performance products. However, existing measurement technology remains traditional and destructive. Except for the waste of various resources and high time-consuming, it requires significant improvement since the unprofessional operations will give rise to large errors. In this paper, an efficient system is proposed to estimate the cement compressive strength based on the broad learning and internal factors, in which the index system describes the internal factors affecting the strength, and the broad learning system distills the potential correlation between the strength and those factors. It can nondestructively estimate the strength directly with the internal factors, which saves labor power and material, avoids large errors caused by unprofessional operations, and aids high-performance cement production.

Accurate estimation of forest biomass is essential to quantify the role forests play in balancing terrestrial carbon. Allometric equations based on tree size have been used for this purpose worldwide. There is little quantitative understanding on how environmental variation may affect tree allometries. Even less known is how to incorporate environmental factors into such equations to improve estimation. Here we tested the effects of climate on tree allometric equations and proposed to model forest biomass by explicitly incorporating climatic factors. For the five major Canadian timber species tested, the incorporation of climate was not found to improve the allometric models. For trembling aspen (Populus tremuloides Michx.) and tamarack (Larix laricina (Du Roi) K.Koch), the residuals of their conventional allometric models were found strongly related to frost-free period and mean annual temperature, respectively. The predictions of the two best climate-based models were significantly improved, which indicates that trembling aspen and tamarack store more aboveground biomass when growing in warmer than in colder regions. We showed that, under the RCP4.5 modest climate change scenario, there would be a 10% underestimation of aboveground biomass for these two species if the conventional non-climate models would still be in use in 2030. This study suggests the necessity to proactively develop climate-based allometric equations for more accurate and reliable forest biomass estimation.

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.

Emission control strategies, meteorological factors, and interregional atmospheric transport of pollutants are considered significant factors that affect PM2.5 concentrations in a city. In this research, two multiple linear regression models are proposed to achieve an accurate estimation of PM2.5 concentrations in Zibo City, Shandong Province of China. The influence of policy was considered as dummy variables in the models. First, a PM2.5–meteorological–policy–interregional model was constructed to explore the diversity of influences from all the parameters mentioned above. Second, for easy predictions of PM2.5 concentrations in the future, a PM2.5–meteorological–policy model was constructed considering meteorological factors from ground monitoring and pertinent air control policies. Data needed to construct the models were obtained from the monthly statistical data from Zibo and surrounding cities from January 2014 to September 2019. Then, the model predictions were extended to July 2021 and compared with the actual series. Model verification was revealed using goodness-of-fit and significant testing statistics. In specific, temperature, wind velocity, and the implementation of relative policies had clear effects on the PM2.5 concentrations in Zibo. Furthermore, Jinan, the provincial capital bordering Zibo to the west, made the largest contribution to fluctuations in PM2.5 concentrations in Zibo. Accordingly, the implementation of strict policies and regulations was undoubtedly a better measure to reduce PM2.5 concentrations in a city under certain topographic and climatic conditions.

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

SAR slow-moving target detection and velocity estimation in ultra-high speed platform and strong clutter are a serious problem. The existing slow-moving target velocity estimation methods including...

A smartphone-based dual-mode sensing system for accurate and reliable estimation of sulphate and chloride level in water is reported. The designed sensor is based on the detection of transmitted modulated signal (for spectrophotometric based study) and scattered signal intensities (for turbidimetric based study) from a sample. The ambient light sensor (ALS) of the phone has been used as a detector for receiving the modulated signal for both the sensing modes of studies. The applicability of the designed tool has been realized through monitoring of sulphate and chloride ions concentrations in water. A custom-designed application has been developed to convert the sensing system into a standalone tool that converts the sensing data into a readable format. The proposed sensing system could emerge as an alternative platform which can be utilized for monitoring of other parameters of water and other media as well.

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 occurrence of microplastics in many shellfish species is of concern because they may affect food security and human health. Previous studies have shown that microplastics ingested by shellfish species can cause adverse effects on the organisms themselves. This raises potential health risks to higher trophic levels of organisms, including humans. In this review, we discussed the findings in the last two years concerning microplastics in various shellfish species, especially focusing on microplastic translocation and accumulation within body tissues of organisms. Based on these findings, we discussed the potential health threats posed by microplastics to shellfish and humans. Microplastics are continuously increasing in various environments. Thus, it is urgent to conduct an accurate estimation of microplastics intake by humans through shellfish consumption and determine the risks of microplastics and their associated contaminants to human bodies.

Station-based one-way carsharing system brings transformation to public mobility and spurs the growth of sharing economy. The accurate estimation of rental and return demand of carsharing stations ...

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...

It is entirely apparent that an accurate estimation of global horizontal solar irradiance (GHI) does not guarantee an accurate estimation of its fundamental components. This basic perception motivates the present study, which evaluates the ability of the clear-sky solar irradiance models to accurately partition GHI into beam and diffuse components. Diverging from the traditional perspective, the diffuse fraction is assessed as an appropriate quantifier for the fractional part of GHI estimated by a clear-sky solar irradiance model as being diffuse. Acting as a quantifier, the diffuse fraction has the merit of isolating the uncertainty induced by aerosols in estimating the diffuse solar irradiance. The results of evaluating various clear-sky solar irradiance models show that many models consistently experience similar errors under high atmospheric turbidity. A strong influence of the atmospheric aerosol loading on the dissimilarities between the accuracies in estimating diffuse fraction and the corresponding diffuse solar irradiance is noticed. Our analysis of uncertainties in estimating the diffuse solar irradiance appears naturally as a sum of two terms: one encapsulating the ‘intrinsic’ error of the model, as quantified through errors in global solar irradiance, and the other encapsulating the aerosol modeling errors, as quantified through errors in diffuse fraction. For small atmospheric aerosol loading, the intrinsic errors of the model are dominant, while for high atmospheric aerosol loading the accurate modeling of the aerosols effect on GHI becomes critical.

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.

Hyperspectral images collected by unmanned aerial vehicles (UAVs) can provide fine, narrowband spectral information and help realize the accurate estimation of physiological and biochemical crop pa...

The International Commission on Radiological Protection (ICRP) recently reduced the dose limit for the eye lens for occupational exposure from 150 mSv yr−1 to 20 mSv yr−1, as averaged over defined periods of five years, with no annual dose in a single year exceeding 50 mSv, emphasizing the importance of the accurate estimation of lens dose. In the present study, for more accurate lens dosimetry, detailed eye models were developed for children and adolescents (newborns and 1, 5, 10, and 15 year olds), which were then incorporated into the pediatric mesh-type reference computational phantoms (MRCPs) and used to calculate lens dose coefficients (DCs) for photon and electron exposures. Finally, the calculated values were compared with those calculated with the adult MRCPs in order to determine the age dependence of the lens DCs. For photon exposures, the lens DCs of the pediatric MRCPs showed some sizable differences from those of the adult MRCPs at very low energies (10 and 15 keV), but the differences were all less than 35%, except for the posterior-anterior irradiation geometry, for which the lens dose is not of primary concern. For electron exposures, much larger differences were found. For the anterior-posterior (AP) and isotropic irradiation geometries, the largest differences between the lens DCs of the pediatric and adult phantoms were found in the energy range of 0.6–1 MeV, where the newborn lens DCs were larger by up to a factor of ∼5 than the adult. The lens DCs of the present study, which were calculated for the radiosensitive region of the lens, also were compared with those for the entire lens in the AP irradiation geometry. Our results showed that the DCs of the entire lens were similar to those of the radiosensitive region for 0.02–2 MeV photons and >2 MeV electrons, but that for the other energy ranges, significant differences were noticeable, i.e. 10%–40% for photons and up to a factor of ∼5 for electrons.

Size effect is a significant factor for accurate estimation of the thermoelastic damping (TED) in microplate/nanoplate resonators. The actual TED in microstructures/nanostructures is comprehensivel...

A rapid method based on quantitative 1H NMR spectroscopy for the accurate estimation of paraffins (P), olefins (O), naphthenes (N), and aromatics (A), i.e., PONA, in extremely light to middle cut n...

This paper provides a quantitative analysis of the resistance force of the locked-wheel for push-pull locomotion rovers using intentional sinkage. Our previous study has confirmed that push-pull locomotion using intentional subsidence at an initial position can contribute to improving the traveling performance. The key factor of this scheme is the resistance force of the locked-wheel. However, the resistance force at different sinkage conditions and wheel sizes (e.g., mass, width, and diameter) remains unclear, especially for the individual locked-wheel. The detailed investigation of this interaction can contribute to the accurate estimation of rover mobility. This paper, therefore, investigates the locked-wheel and soil interaction at different sinkage conditions experimentally, especially focusing on the intentional sinkage condition. Additionally, the resistance force is considered theoretically through the knowledge based on the soil flow patterns beneath the locked-wheel. The experimental results confirmed that the resistance force of the locked-wheel rose as the initial sinkage, wheel size, and weight increases. Furthermore, the theoretical calculation suggested the resistance force increased with a similar tendency of the experimental data.

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.

Reliability is widely used as a performance indicator of mining equipment to achieve a cost-effective maintenance plan. Reliability is a function of time as well as environmental and operational factors. Applying an adequate model by taking into account the mentioned factors is vital to ensure an accurate estimation of reliability characteristics. The aim of this study is to investigate the application of mixed frailty model to describe both observed and unobserved heterogeneity in reliability analysis of mining equipment. The capability of the model is assessed using field data from a fleet of dump trucks in an open-pit mine. The results indicate that the proposed model is superior to the traditional Cox model when data are heterogeneous. The results also show that the operator's skill and road conditions have a significant effect on the reliability of dump trucks.

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.

The prediction of the pressure drop of packed beds requires an accurate estimation of the packed-bed porosity as its error propagation multiplies the occurring errors by a factor of about four. For a better understanding of the packed bed's local porosity characteristics, a comprehensive x-ray tomography study was performed investigating and correlating the void structure of packed beds made of smooth, mono-sized spheres in cylindrical confining walls having tube-to-particle diameter ratios λ = 3.0 to 9.0 reinforced by numerically generated packed beds of λ = 1.1 to 9.0. An in-depth analysis of the obtained oscillating void profiles is performed, discussing the locations and heights of porosity extrema. Most importantly, a significant and in parts surprising extrema formation in the tube's center is described, especially present for λ

Due to the need for very high data rates in 5G, terrestrial radio links at millimeter-wave frequencies (particularly in the E band) are of paramount importance for use in the network front-/backhauling. An accurate estimation of the probability of occurrence of fade duration may help operators in the assessment of radio link time outages. Furthermore, experimental data for relatively long periods of time are needed for the development of a fade duration model.

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.

The DLPNO-CCSD(T1)/CBS and G4 methods combined with isogyric, isodesmic, and homodesmotic working reactions were applied to calculate gas-phase enthalpies of formation for thirty-three C–H–N–O compounds with well-established experimental values. Unlike the G4 method, the DLPNO calculations, even together with isogyric reactions with small reference species, reproduce the experimental enthalpies of formation within chemical accuracy (≈ 4 kJ/mol). For more than half of the compounds, a better accuracy, within 2 kJ/mol, was achieved. These results make the DLPNO-CCSD(T1)/CBS method a promising tool for accurate prediction of enthalpy of formation of medium-sized organic compounds. Being convinced of its reliability, the DLPNO-CCSD(T1)/CBS method was also used to estimate the enthalpies of formation for some challenging compounds with conflicting values reported in the literature (glycerol, nitromethane, nitrobenzene, 1-aminoadamantane, piperazine, and others).

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.