Single Model Parameter Research Articles

Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments.

Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering. Based on this redefined ATI, we developed a single-parameter model that outperforms the state-of-the-art reaction norm index model in both accuracy and stability, especially with limited training data. We identified 10 loci significantly associated with ATI variation, including two near known flowering time genes and four linked to ecotype differentiation. To enhance practical utility, we developed an efficient flowering time prediction kit using 28 functionally relevant markers, complemented by a user-friendly online tool (http://xielab.hzau.edu.cn/ATI). Our approach can be easily applied to other crops, as ATI is commonly used across various agricultural systems.

Electric fields imbue enzyme reactivity by aligning active site fragment orbitals

It is broadly recognized that intramolecular electric fields, produced by the protein scaffold and acting on the active site, facilitate enzymatic catalysis. This field effect can be described by several theoretical models, each of which is intuitive to varying degrees. In this contribution, we show that a fundamental effect of electric fields is to generate electrostatic potentials that facilitate the energetic alignment of reactant frontier orbitals. We apply this model to demystify the impact of electric fields on high-valent iron-oxo heme proteins: catalases, peroxidases, and peroxygenases/monooxygenases. Specifically, we show that this model easily accounts for the observed field-induced changes to the spin distribution within peroxidase active sites and explains the transition between epoxidation and hydroxylation pathways seen in Cytochrome P450 active site models. Thus, for the intuitive interpretation of the chemical effect of the field, the strategy involves analyzing the response of the orbitals of active site fragments, and their energetic alignment. We note that the energy difference between fragment orbitals involved in charge redistribution acts as a measure for the chemical hardness/softness of the reactive complex. This measure, and its sensitivity to electric fields, offers a single parameter model from which to quantitatively assess the effects of electric fields on reactivity and selectivity. Thus, the model provides an additional perspective to describe electrostatic preorganization and offers ways for its manipulation.

Optical properties of 4H-SiC and 6H-SiC from infrared to vacuum ultraviolet spectral range ellipsometry (0.05–8.5 eV)

Complex dielectric function (ɛ = ɛ1 + iɛ2) spectra are obtained from reflection mode spectroscopic ellipsometry and unpolarized transmittance measurements for 4H and 6H stacking sequence silicon carbide (SiC) nitrogen-doped single crystals from the infrared (IR) to vacuum ultraviolet (VUV) spectral range. A single parametric model describing ɛ predominately for the ordinary directions is developed over the 0.05–8.5 eV spectral range from analysis of (0001)-oriented back side roughened 4H-SiC and 6H-SiC single crystals with some contribution from the extraordinary direction of 6H-SiC in the IR region. Indirect bandgaps for 4H-SiC and 6H-SiC are found to be 3.30 and 3.03 eV, respectively, and the corresponding direct optical gaps are at 4.46 and 4.42 eV. A model describing the optical response in the IR spectral range is created using a Drude expression and either transverse optical (TO) and longitudinal optical (LO) (TOLO) or Lorentz oscillator models. Free carrier concentration (N) is optically measured to be 3.7 × 1018 and 3.3 × 1018 cm−3 using TOLO and Lorentz oscillator models, respectively, and the corresponding carrier mobility (μ) is 34 and 39 cm2/V s for 4H-SiC. Under the same assumption for 6H-SiC, N is measured to 8 × 1018 cm−3 using either TOLO or Lorentz oscillator models and μ is 9 and 10 cm2/V s using the TOLO and Lorentz oscillator models, respectively, in the ordinary direction and 5 cm2/V s in the extraordinary direction using either model. For 4H-SiC, using the TOLO oscillator model, TO and LO phonon modes are measured at 797.7 and 992.1 cm−1, respectively, and corresponding modes are found at same locations using the Lorentz oscillator model. In 6H-SiC, using the TOLO model, TO modes in ordinary and extraordinary directions are found at 797.7 and 789.7 cm−1, and corresponding modes are at 796.9 and 788.9 cm−1 using the Lorentz oscillator model. The LO modes using the TOLO model are found at 992 and 984 cm−1 in the ordinary and extraordinary directions, respectively, and the same modes in the corresponding direction using the Lorentz oscillator model are located at 975.9 and 967.9 cm−1.

Forecast of Modulation of Cosmic Rays with Rigidity of 10 GV in the 25th Solar Activity Cycle

Based on a forecast of solar activity parameters and the model developed by the authors for modulation of Galactic cosmic rays, we forecasted cosmic ray variations in the 25th solar activity cycle. The cosmic ray flux forecast is based on correlation with the number of sunspots (single-parameter model) or with a set of solar (mainly magnetic) parameters (multiparameter model). The forecast for the number of sunspots was taken from published data; the forecast for other solar parameters was done in the study. It is shown that variations in cosmic rays over three years of the current 25th cycle, in general, do not contradict the forecasts and indicate that the 25th solar activity cycle is expected to be slightly more active compared to the 24th.

Numerical modeling of beam plastic hinges in steel moment resisting frames including local buckling and stiffness/strength degradation

Plastic hinge formation in beams is the main energy dissipation mechanism in moment resisting frames, but its deformation capacity is limited by the strength deterioration after reaching the maximum moment. Such degradation is highly influenced by the onset of local buckling in the plastic hinge region once a significant portion of the cross-section has reached the yield stress. Numerical models developed to study this effect have shown good accuracy against experimental data, but with high computational costs and the need to calibrate several model parameters. This work proposes a numerical model of a beam plastic hinge that uses only one parameter to reproduce the hysteretic behavior under cyclic loading, degrading simultaneously stiffness and resistance with lower computational cost. The proposed model relies on the discretization of the beam cross-section using uniaxial bars with a prescribed geometric imperfection with buckling degrading strength capability spanning along an assumed plastic hinge length. The Euler-Bernoulli hypothesis is imposed at the ends of the plastic hinge region and elastic beam elements are used to model the beam outside this domain. The model is validated against experimental data from three cyclic loading connection tests reported in the literature. Results show that the model can accurately represent the response of the beam plastic hinge with a low computational cost by adjusting one single model parameter as well as the definition of the nominal information of the beam geometry and material properties, expected plastic hinge length, and standard fabrication tolerances.

Comprehensive modeling of far-infrared drying process for rough rice in a combined FIR dryer

This study introduces a novel single-parameter model for the far-infrared (FIR) drying process of rough rice in a combined FIR dryer, aiming to streamline the prediction process and enhance its efficiency. The experiments involved three levels of FIR intensity (0, 500, and 1000 W m−2) and three levels of inlet air temperature (30, 40, and 50 °C), with a comprehensive examination of various mathematical thin-layer models to predict the drying process. The study developed a comprehensive single-parameter model by investigating the interrelationship of the models’ coefficients under distinct drying conditions, demonstrating its capability to faithfully depict the drying characteristics of rough rice within the combined FIR dryer with an R2 exceeding 0.99. The findings also provide valuable insights into the impact of drying conditions on the drying time, percentage of cracked kernels (PCK), and amylose content of rough rice, revealing increasing trends in PCK and amylose content with higher drying temperature and FIR intensity, offering practical and efficient tools for optimizing the drying process and enhancing the quality of dried rice products. Overall, this research significantly advances rice drying technology and offers practical solutions for improving the efficiency and quality of rice drying processes.

External validation of models to estimate gestational age in the second and third trimester using ultrasound: A prospective multicentre observational study.

Accurate assessment of gestational age (GA) is important at both individual and population levels. The most accurate way to estimate GA in women who book late in pregnancy is unknown. The aim of this study was to externally validate the accuracy of equations for GA estimation in late pregnancy and to identify the best equation for estimating GA in women who do not receive an ultrasound scan until the second or third trimester. This was a prospective, observational cross-sectional study. 57 prenatal care centres, France. Women with a singleton pregnancy and a previous 11-14-week dating scan that gave the observed GA were recruited over an 8-week period. They underwent a standardised ultrasound examination at one time point during the pregnancy (15-43 weeks), measuring 12 foetal biometric parameters that have previously been identified as useful for GA estimation. A total of 189 equations that estimate GA based on foetal biometry were examined and compared with GA estimation based on foetal CRL. Comparisons between the observed GA and the estimated GA were made using R2, calibration slope and intercept. RMSE, mean difference and 95% range of error were also calculated. A total of 2741 pregnant women were examined. After exclusions, 2339 participants were included. In the 20 best performing equations, the intercept ranged from -0.22 to 0.30, the calibration slope from 0.96 to 1.03 and the RSME from 0.67 to 0.87. Overall, multiparameter models outperformed single-parameter models. Both the 95% range of error and mean difference increased with gestation. Commonly used models based on measurement of the head circumference alone were not amongst the best performing models and were associated with higher 95% error and mean difference. We provide strong evidence that GA-specific equations based on multiparameter models should be used to estimate GA in late pregnancy. However, as all methods of GA assessment in late pregnancy are associated with large prediction intervals, efforts to improve access to early antenatal ultrasound must remain a priority. The proposal for this study and the corresponding methodological review was registered on PROSPERO international register of systematic reviews (registration number: CRD4201913776).

Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer.

Unfortunately, the morphologic magnetic resonance imaging (MRI) is unable to determine perineural invasion (PNI) status. This study applied histogram analysis of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the assessment of PNI status of rectal cancer (RC). The retrospective analysis enrolled 175 patients with RC confirmed by postoperative pathology in The First Affiliated Hospital of Shandong First Medical University from January 2019 to December 2021. All patients underwent preoperative rectal MRI. Whole-tumor volume histogram features from IVIM-DWI were extracted using open-source software. Univariate analysis and multivariate logistic regression analysis were used to compare the differences in histogram parameters and clinical features between the PNI-positive group and PNI-negative group. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance, while the Delong test was used to compare the area under the curve of the models. The interobserver agreement of the histogram features derived from DWI, including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC) were good to excellent. A total of eight histogram features including DWI_maximum, DWI_skewness, D_kurtosis, D_minimum, D_skewness, D*_energy, D*_skewness, and f_minimum were significantly different between the PNI-positive and PNI-negative groups in the univariate analysis (P<0.05); among the clinicoradiologic factors, percentage of rectal wall circumference invasion (PCI) was significantly different between the two groups (P<0.05). Multivariate analysis demonstrated that the values of D*_energy, D*_skewness, and f_minimum differed significantly between the PNI-positive patients and PNI-negative patients (P<0.05), with the independent risk factors being D*_skewness [odds ratio (OR) =1.157; 95% confidence interval (CI): 1.050-1.276; P=0.003] and PCI (OR =11.108, 95% CI: 1.767-69.838; P=0.0002). The area under the curve of the model combining the three histogram features and PCI to assess PNI status in RC was 0.807 (95% CI: 0.741-0.863). The results of the Delong test showed that the combined model was significantly different from each single-parameter model (P<0.05). The combined model constructed on the basis of IVIM-DWI histogram features may help to assess the status of RC PNI.

Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.

Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linear-nonlinear model. However, it is challenging to analyze phase-invariant complex cells, which require more elaborate models having a combination of nonlinear subunits. Estimating parameters of these models is made additionally more difficult by cortical neurons' trial-to-trial response variability. We develop a simple convolutional neural network method to estimate receptive field models for both simple and complex visual cortex cells from their responses to natural images. The model consists of a spatiotemporal filter, a parameterized rectifier unit (PReLU), and a two-dimensional Gaussian "map" of the receptive field envelope. A single model parameter determines the simple vs. complex nature of the receptive field, capturing complex cell responses as a summation of homogeneous subunits, and collapsing to a linear-nonlinear model for simple type cells. The convolutional method predicts simple and complex cell responses to natural image stimuli as well as grating tuning curves. The fitted models yield a continuum of values for the PReLU parameter across the sampled neurons, showing that the simple/complex nature of cells can vary in a continuous manner. We demonstrate that complex-like cells respond less reliably than simple-like cells. However, compensation for this unreliability with noise ceiling analysis reveals predictive performance for complex cells proportionately closer to that for simple cells. Most spatial receptive field structures are well fit by Gabor functions, whose parameters confirm well-known properties of cat A17/18 receptive fields.

Correlation between acoustic velocity and physical parameters of sea floor sediments: a case study of the northern South China Sea

As the interface between seawater and the seabed, superficial sediments on the seabed are an important part of the marine acoustic field environment and are indispensable for marine resource investigations. Studying sediments several meters to hundreds of meters below the seafloor is highly valuable and important. This study processes and analyses the water depth, topography and bottom data and obtains the shallow bottom profile and topographic map of the northern continental slope of the South China Sea (SCS). The study analyzes the influence of physical parameter (including density, porosity, and grain size) on the acoustic velocity in sediments. Single-parameter and dual-parameter models are established to further examine this influence. The results show that porosity and density have greater influences on the acoustic velocity of sediments than does grain size. Finally, the acoustic properties of several typical stations with water depths are tested to analyze the variations in the acoustic properties of the shallow sediments in the northern SCS. The results show that the influence of each parameter on the prediction of the acoustic velocity of the sediment is in the following order: porosity&amp;gt;density&amp;gt;grain size. This study analyses and reveals the reason why the seafloor sediments in the local area cause the acoustic properties to change greatly. It may be caused by changes in the sediment type, lithology along with the depth. And the other reason is the development of interlayer in the land slope of the northern SCS.

The dynamics of neurobehavioral impairment and recovery sleep: improved biomathematical modeling for fatigue risk management in operational settings

Adequate sleep is essential for maintaining health, safety, and neurobehavioral functioning in 24/7 operational settings. Loss of sleep causes fatigue, which results in neurobehavioral impairment. Neurobehavioral impairment builds up disproportionately under conditions of total sleep deprivation compared to sustained sleep restriction; and recuperation due to recovery sleep is disproportionately fast after total sleep deprivation compared to sustained sleep restriction. These sleep schedule-dependent dynamics have been captured in a previously published, biomathematical model of fatigue, which includes positive feedback regulation of a relatively fast (hours to days) sleep/wake homeostatic process by a slower (days to weeks) allostatic process—a feature that suggests adenosinergic mechanisms are involved. However, the previously published model underestimates the rate of recuperation due to recovery sleep after acute total sleep deprivation as well as after consecutive days of sleep restriction. The objective of the present research is to modify the model to improve the accuracy of its predictions for recuperation due to recovery sleep. This can be accomplished by including in the model an additional, reciprocal feedback mechanism, presumed to be predominantly adenosinergic in nature, which provides feedback from the faster homeostatic process back onto the slower allostatic process. Adding a single new model parameter and refitting three existing model parameters significantly improves the predictions for recuperation due to recovery sleep after both acute total sleep deprivation and sustained sleep restriction. This model modification also improves the predictions of the build-up of neurobehavioral impairment across days of sustained sleep restriction, without adversely affecting the accuracy of the model in other scenarios including circadian misalignment and sleep inertia. The modified model preserves the previously developed capability to predict the differential dynamics of fatigue for objective performance impairment and subjective sleepiness. With the improved predictions for recuperation due to recovery sleep, the expanded model can be used to provide quantitative estimates for potentially impactful work scheduling decisions, such as the duration of time off needed before workers would be safe to return to the work floor. This enhances the usefulness of the model as a tool for predicting and managing neurobehavioral functioning and safety in 24/7 operational settings.

Correlation between the sound speed ratio and physical properties of seafloor sediments in the northwestern shelf of the south China sea

Based on a substantial number of seabed sediment samples and corresponding acoustic characteristic and physical property test data collected in the continental shelf area of the northwestern South China Sea, this study employed a sound speed ratio correction model to analyze the correlation between the sound speed ratio of seabed sediments and parameters such as porosity, density, mean grain size, and water content. This study resulted in the development of both single-parameter and double-parameter empirical formulas for the sound speed ratio of seabed sediments in this marine region. The determination coefficients (r2) for the single-parameter empirical formulas based on physical properties, excluding the mean grain size (r2 ≤ 0.75), were all above 0.78, indicating a strong correlation. Additionally, the fitting curve at 100 kHz was slightly greater than that at 50 kHz, consistent with the acoustic dispersion characteristics of sediments. To assess the applicability of the empirical formulas to different maritime areas, the formulas derived from the South Yellow Sea, East China Sea, and South China Sea were compared with those of the study area. The results indicated that the empirical formulas for the sound speed ratio established in this study have general applicability, especially for sediments with low porosity, high density, and low mean grain size. In addition, the determination coefficient of the dual-parameter empirical formula for the sound speed ratio–porosity–mean grain size is superior to that of the single-parameter empirical formula for the sound speed ratio–mean grain size. Through comparative studies with other marine areas, it was found that there are certain differences in the relationships between porosity and mean grain size established in different marine areas. Therefore, based on the rough sphere model, this study transforms the GS model into a single-parameter model built on either porosity or mean grain size. The error analysis results indicate that the single-parameter GS model has a smaller error than the measured data. However, the GS model controlled by a single parameter performs poorly compared to the predicted curves of the sound speed ratio–porosity and the sound speed ratio–mean grain size fitted in this study. This discrepancy may be attributed to the fact that the input parameters of the GS model are reference values, emphasizing the need for further research to determine suitable input parameters for the GS model in this marine area.

Association between accelerometer-derived physical activity and depression: a cross-sectional study using isotemporal substitution analysis

ObjectivesDepression is a significant public health concern, and physical activity has been identified as a non-pharmacological intervention. Understanding the dose–response relationship between physical activity and depression is crucial for designing...

Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma

ObjectiveTo investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016–October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter.ResultsStatistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915–0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967–0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant.ConclusionAmong all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model.

Soft-sphere continuum solvation models for nonaqueous solvents.

Solvation effects profoundly influence the characteristics and behavior of chemical systems in liquid solutions. The interaction between solute and solvent molecules intricately impacts solubility, reactivity, stability, and various chemical processes. Continuum solvation models gained prominence in quantum chemistry by implicitly capturing these interactions and enabling efficient investigations of diverse chemical systems in solution. In comparison, continuum solvation models in condensed matter simulation are very recent. Among these, the self-consistent continuum solvation (SCCS) and the soft-sphere continuum solvation models (SSCS) have been among the first to be successfully parameterized and extended to model periodic systems in aqueous solutions and electrolytes. As most continuum approaches, these models depend on a number of parameters that are linked to experimental or theoretical properties of the solvent, or that can be tuned based on reference data. Here, we present a systematic parameterization of the SSCS model for over 100 nonaqueous solvents. We validate the model's efficacy across diverse solvent environments by leveraging experimental solvation-free energies and partition coefficients from comprehensive databases. The average root means square error over all the solvents was calculated as 0.85 kcal/mol which is below the chemical accuracy (1 kcal/mol). Similarly to what has been reported by Hille et al. (J. Chem. Phys. 2019, 150, 041710.) for the SCCS model, a single-parameter model accurately reproduces experimental solvation energies, showcasing the transferability and predictive power of these continuum approaches. Our findings underscore the potential for a unified approach to predict solvation properties, paving the way for enhanced computational studies across various chemical environments.

Improved correlations of the fuel/cladding liquid penetration rate with the out-of-pile transient database

Utilizing data from the Out-of-Pile Transient Database (OPTD), we developed advanced correlations for the cladding liquid penetration rate in metallic fuels used in sodium-cooled fast reactors (SFRs). Based on the available OPTD data, our research highlights the key role of Pu concentration in influencing the liquid penetration behavior, showing different threshold fuel-cladding interaction temperatures, with burnup playing a substantial role, particularly in fuels with a 19% Pu concentration. The penetration rate exhibits a parabolic, diffusion-limited behavior, thereby facilitating predictions of maximum liquid penetration depth over time. These correlations are significantly more accurate, under a variety of conditions, compared to existing single-parameter models, and enhance our understanding of SFR metallic fuel behavior, promoting safer and more efficient reactor operations.

Risks to predict blood loss and cranial nerve injury in carotid body paraganglioma resection

Objective: To investigate clinical and imaging parameters to predict blood loss and cranial nerve injury (CNI) following carotid body paraganglioma (CBP) resection. Methods: A retrospective examination of clinical and imaging data was conducted on 63 patients who underwent CBP resection at Xiangya Hospital of Central South University from January 2016 to December 2022, including 23 males and 40 females, aged 26-87 years old. Three imaging parameters including tumor volume, the angle of contact with the internal carotid artery (ICA), and the distance to the base of skull (DTBOS) were gauged using the IMEDPACS software on CTA and MR imaging. The predictive efficacies of age, gender, Shamblin classification, and three imaging parameters for blood loss and CNI following surgery were analysed. Logistic composite parameter models were constructed and their predictive validity was assessed. Results: Multivariate logistic regression analysis underscored that only tumor volume (OR=1.381,95%CI:1.167-1.507,P=0.001) showed significant statistical correlations with blood loss following surgery. Area under curve (AUC) values of 0.910 for receiver operating characteristic (ROC) curves showed a sensitivity of 1.000 and a specificity of 0.694. Tumor volume (OR=1.126,95%CI:1.030-1.231, P=0.002) and DTBOS (OR=0.225,95%CI:0.081-0.630,P=0.005) were significantly associated with postoperative CNI. The analysis of logistic composite model showed AUC values for tumor volume, DTBOS and combination of the two parameters were 0.858, 0.788, and 0.872, respectively. The model for combination of tumor volume and DTBOS also proved superior in predicting postoperative CNI (Z=3.106, P<0.001), with a sensitivity of 0.833 and a specificity of 0.769. Conclusions: Tumor volume and DTBOS emerged as effective predictors for blood loss and/or CNI in patients with CBP resection. Moreover, the logistic composite parameter model outclassed single-parameter models in terms of their predictive clinical value.

Simulating large-diameter industrial ball mills from batch-grinding tests

Phenomenological models of ball milling are routinely used to optimize size reduction operations. A usual method adopted in the minerals industry relies on batch-grinding tests to calibrate models in a population balance model formulation describing the breakage behaviour and then scale-up the breakage parameters using an energy-based relationship. Despite the important advances and application of these methods, their application in describing grinding in large-diameter (>7.5 m) mills has not been widely demonstrated. The work demonstrates the calibration of industrial-scale ball mill model parameters from laboratory milling tests on Platinum Group Metals (PGM) and Itabirite iron ores. Minor fine-tunning of a single model parameter was required to simulate the large-diameter industrial mills, demonstrating the validity of the approach for ores as distinct as PGM and Itabirite iron ores. Estimation of the Arbiter Flow Number below 4.1 showed a regular slurry transport for all mills studied. This model can then be used in optimization of the ball mill performance.

Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves

Context or problemNitrogen is one of the important elements of crops, which plays a decisive role in crop growth and development and the formation of yields. Monitoring of rice organ-scale nitrogen concentration based on the unmanned aerial vehicle (UAV) images is of great significance for rice field management and yield prediction. Objective or research questionPrevious studies have focused on the use of traditional statistical methods and chlorophyll-related vegetation indices to construct plant nitrogen concentration, with models lacking generalizability. MethodsIn this study, rice field trials of two varieties (NJ9108, YD6) and nitrogen fertilizer treatments (N0-N3: 0, 105, 210 and 315 kg/ha) were conducted for 3 years with manual sampling and UAV digital and hyperspectral images during key fertility periods. Based on the data of the whole growth periods and combined with vegetation indices (VIs), color indices (CIs), hyperspectral parameters (HPs), texture indices (TIs) and machine-learning algorithms, monitoring models of nitrogen concentration at the organ scale of rice were constructed and used to estimate the N content of multiple organs (leaf and stem) of rice at different periods. Field experiments were used to collect the multi-organ nitrogen concentration of rice and the remote sensing (RS) data of UAV during the critical growth period of the two years (2021, 2022), and machine-learning algorithms were used to construct the estimation models. ResultsThe results showed that VIs had good correlations with leaf nitrogen concentration (LNC), stem nitrogen concentration (SNC) and plant nitrogen concentration (PNC), with correlation coefficients (r) of 0.86, 0.74 and 0.81, respectively. Machine learning estimation models combining multiple types of RS indices were more accurate than single parameter models constructed by traditional statistical methods, with the LNC optimal model (R2 = 0.8, RMSE = 3.83 mg/g), the SNC optimal model (R2 = 0.7, RMSE = 2.43 mg/g) and the PNC optimal model (R2 = 0.7, RMSE = 3.19 mg/g). Validated using data from 2020, the machine-learning models were far more accurate than traditional methods. ConclusionsThese results show that the use of multi-source remote sensing data based on machine-learning algorithms can effectively estimate the nitrogen concentration of organs in rice. ImplicationsThis study provides an accurate, stable and universal method for estimating rice nitrogen concentration in rice organs, which can be used as a reference for estimating rice nitrogen concentration in large fields using UAV RS technology.

Adjusting the range of cell–cell communication enables fine-tuning of cell fate patterns from checkerboard to engulfing

During development, spatio-temporal patterns ranging from checkerboard to engulfing occur with precise proportions of the respective cell fates. Key developmental regulators are intracellular transcriptional interactions and intercellular signaling. We present an analytically tractable mathematical model based on signaling that reliably generates different cell type patterns with specified proportions. Employing statistical mechanics, We derived a cell fate decision model for two cell types. A detailed steady state analysis on the resulting dynamical system yielded necessary conditions to generate spatially heterogeneous patterns. This allows the cell type proportions to be controlled by a single model parameter. Cell–cell communication is realized by local and global signaling mechanisms. These result in different cell type patterns. A nearest neighbor signal yields checkerboard patterns. Increasing the signal dispersion, cell fate clusters and an engulfing pattern can be generated. Altogether, the presented model allows us to reliably generate heterogeneous cell type patterns of different kinds as well as desired proportions.