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This paper studies the implications of agents signaling their moral type in a lying game. In the theoretical analysis, a signaling motive emerges where agents dislike being suspected of lying and where some lies are more stigmatized than others. The equilibrium prediction of the model can explain experimental data from previous studies, particularly on partial lying, where individuals lie to gain a non-payoff maximizing amount. I discuss the relationship with theoretical models of lying that conceptualize the image concern as an aversion to being suspected of lying and provide applications to narratives, learning, the disclosure of lies, and the selection into lying opportunities. [ABSTRACT FROM AUTHOR]

It is important to understand the flow behaviour inside wedge-shaped hoppers and accurately predict the discharge rate for the processing and handling of granular materials, regardless of whether the wedge hopper is symmetric or asymmetric. In this paper, the discrete element method (DEM) was used to reveal the flow behaviour of pellet feed in an asymmetric wedge-shaped hopper from the discharge rate, flow pattern, velocity distribution, normal contact force between particles and free-fall arch. The results showed that, with the hopper angle decreasing, the area of the active region of the particles increased, the stagnation zone decreased, and the free-fall arch became unstable. When the unilateral hopper angle was less than 45°, the discharge rate of the wedge-shaped hopper increased, and the average discharge rate reached the maximum value of 1.070 kg s−1 when the left and right hopper angles were equal to 15°. In addition, using the discharging mass proportion coefficient to represent the size of the region in which the unilateral hopper angle affected the flow of particles in the hopper, the hopper angle term in the Brown and Sellers model was corrected. The predictive errors of the corrected discharge rate model were less than 7.6% and 3.3% in the simulated and actual discharging tests respectively, which was better than that of the Brown and Sellers model. The results of the study could provide a theoretical basis for the intelligent upgrading of feed accurate handling equipment, and provide a reference basis for the design of asymmetric wedge-shaped hopper. • Simplified modeling of pellet feeds applied. • Discharge characteristics of an asymmetric wedge-shaped hopper revealed. • Discharge rate prediction model for an asymmetric wedge-shaped hopper developed. [ABSTRACT FROM AUTHOR]

Fingerling counting is a basic operation in fish farming and provides an important guideline for many aspects of aquaculture. However, most of the current counting methods are inefficient or computationally cumbersome. This study proposed a high-speed, non-intrusive fingerling counting method based on multiple detectors and a motion prediction model, which achieved high-accuracy counting under the condition of low-frame rate. Firstly, to effectively detect and locate the adherent fingerlings, the detector was accomplished by combining the mixture of Gaussian-based (MOG) segmentation algorithm and the local extremum-based blob detection algorithm. Secondly, three different functions were used to construct a motion prediction model to predict the approximate probability of the fingerlings at each position in the previous frame. Thirdly, the cost matrix was constructed with probability as the feature to associate the fingerlings in the consecutive frames, and the newly appeared fingerlings were counted in real-time, realising the continuous fingerling counting with high precision. Through testing and analysis on 52 collected datasets under low-frame-rate (10 fps) acquisition conditions using largemouth bass (Micropterus salmoides , 3–5 cm) and crucian carp (Carassius auratus , 2–6 cm), results indicated that the best motion prediction model with segmentation function reached over 99% average counting accuracy for both species, with a standard deviation of accuracy less than 0.7%. This method provides a low-cost, high-speed, and stable application solution for computer vision-based fingerling counting. • Combined Mixture of Gaussian segmentation and blob detection for fingerling counting. • Integration of a motion prediction model achieved over 99% average counting accuracy. • Open access to a dataset containing annotation files for fingerlings counting. [ABSTRACT FROM AUTHOR]

• LGB-IPs is a new LGB-based optimal feature fusion model for the accurate prediction of STY phosphorylation sites. • LGB-IPs explores ten different feature descriptors and assesses its discriminative capability using five different classifiers. • Extensive cross-validation and independent assessment shows that LGB-IPs outperformed the single feature models consistently. SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers. [ABSTRACT FROM AUTHOR]

Black persons bear a disproportionate burden of peripheral artery disease (PAD) and experience higher rates of endovascular revascularization failure (ERF) when compared with non-Hispanic White persons. We aimed to identify predictors of ERF in Black persons using predictive modeling. This retrospective study included all persons identifying as Black who underwent an initial endovascular revascularization procedure for PAD between 2011 and 2018 at a midwestern tertiary care center. Three predictive models were developed using (1) logistic regression, (2) penalized logistic regression (least absolute shrinkage and selection operator [LASSO]), and (3) random forest (RF). Predictive performance was evaluated under repeated cross-validation. Of the 163 individuals included in the study, 113 (63.1%) experienced ERF at 1 y. Those with ERF had significant differences in symptom status (P < 0.001), lesion location (P < 0.001), diabetes status (P = 0.037), and annual procedural volume of the attending surgeon (P < 0.001). Logistic regression and LASSO models identified tissue loss, smoking, femoro-popliteal lesion location, and diabetes control as risk factors for ERF. The RF model identified annual procedural volume, age, PAD symptoms, number of comorbidities, and lesion location as most predictive variables. LASSO and RF models were more sensitive than logistic regression but less specific, although all three methods had an overall accuracy of ≥75%. Black persons undergoing endovascular revascularization for PAD are at high risk of ERF, necessitating need for targeted intervention. Predictive models may be clinically useful for identifying high-risk patients, although individual predictors of ERF varied by model. Further exploration into these models may improve limb salvage for this population. [ABSTRACT FROM AUTHOR]

• MicroRNA-mediated gene regulation enables plant adaptation and survival under stressors like drought, salinity, heat, and heavy metals. • Addressing this biological importance a computational model has developed for identification of MiRNA and Pre-MiRNA based on RNA sequences. • The relative positioning based feature vector has been computed with ensemble modeling for classification purpose. • The proposed study overcome the existing studies by enhancing the accuracy of computational model. • The webservers for identification of MiRNA and Pre-MiRNA has been developed. This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-silico prediction. Addressing this gap, the study seeks to develop an efficient computational model for plant stress response prediction. The two benchmark datasets for MiRNA and Pre-MiRNA dataset have been acquired in this study. Four ensemble approaches such as bagging, boosting, stacking, and blending have been employed. Classifiers such as Random Forest (RF), Extra Trees (ET), Ada Boost (ADB), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). Stacking and Blending employed all stated classifiers as base learners and Logistic Regression (LR) as Meta Classifier. There have been a total of four types of testing used, including independent set, self-consistency, cross-validation with 5 and 10 folds, and jackknife. This study has utilized evaluation metrics such as accuracy score, specificity, sensitivity, Mathew's correlation coefficient (MCC), and AUC. Our proposed methodology has outperformed existing state of the art study in both datasets based on independent set testing. The SVM-based approach has exhibited accuracy score of 0.659 for the MiRNA dataset, which is better than the previous study. The ET classifier has surpassed the accuracy of Pre-MiRNA dataset as compared to the existing benchmark study, achieving an impressive score of 0.67. The proposed method can be used in future research to predict abiotic stresses in plants. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is a serious postoperative complication associated with increased morbidity and mortality. Identifying patients at risk for AKI is important for risk stratification and management. This study aimed to develop an AKI risk prediction model for colectomy and determine if the operative approach (laparoscopic versus open) alters the influence of predictive factors through an interaction term analysis. The American College of Surgeons National Surgical Quality Improvement Program database was analyzed from 2005 to 2019. Patients undergoing laparoscopic and open colectomy were identified and propensity score matched. Multivariable logistic regression identified significant preoperative demographic, comorbidity, and laboratory value predictors of AKI. The predictive ability of a baseline model consisting of these variables was compared to a proposed model incorporating interaction terms between operative approach and predictor variables using the likelihood ratio test, c-statistic, and Brier score. Shapley Additive Explanations values assessed relative importance of significant predictors. 252,372 patients were included in the analysis. Significant AKI predictors were hypertension, age, sex, race, body mass index, smoking, diabetes, preoperative sepsis, Congestive heart failure, preoperative creatinine, preoperative albumin, and operative approach (P < 0.001). The proposed model with interaction terms had improved predictive ability per the likelihood ratio test (P < 0.05) but had no statistically significant interaction terms. C-statistic and Brier scores did not improve. Shapley Additive Explanations analysis showed hypertension had the highest importance. The importance of age and diabetes showed some variation between operative approaches. While the inclusion of interaction terms collectively improved AKI prediction, no individual operative approach interaction terms were significant. Including operative approach interactions may enhance predictive ability of AKI risk models for colectomy. [ABSTRACT FROM AUTHOR]

The rational fertilisation of olive trees, based on adding exclusively the nutrients that are actually needed, is important from both the economic and environmental sustainability points of view. This paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression. The paper shows the convenience of addressing the problem as a Multiple Instance Regression, and compares two strategies of data aggregation and different choices of feature vectors derived from the raw multispectral data. The models provided good results for N, P and K (r 2 = 0.76, r 2 = 0.87 and r 2 = 0.91, respectively for the Hojiblanca model, and r 2 = 0.79, r 2 = 0.80 and r 2 = 0.80 for the Picual model). The rest of nutrients studied also offered good results for both the Picual and Hojiblanca models, ranging from r 2 = 0.69 for B to r 2 = 0.93 for Cu. The results indicate a robust performance of the models and a potential for improvement with the addition of more data, along with an advantage of considering individual models for each cultivar variety. Overall, these results are very promising for the estimation of the leaf nutrient content of olives trees and the detection of spatial variability in the fertilisation needs of orchards. [Display omitted] • Leaf nutrient content of olive trees is predicted from UAV multispectral data. • Multiple Instance Regression is applied and its benefits discussed. • Models provide good results for N, P and K in the Hojiblanca and Picual models. • The rest of nutrients also show acceptable results. • Results are promising for detecting spatial variability in fertilisation needs. [ABSTRACT FROM AUTHOR]

This study presents the development of a maize ear model and a predictive approach for kernel damage in maize threshing, integrating physical simulation and predictive analytics to understand better and forecast threshing-related damage. First, a maize ear model was developed to analyse kernel damage during threshing. Through the angle of repose experiments, the optimal number of spheres for the kernel model was established as 65. Tensile tests were conducted to evaluate the kernel-cob bond strength, revealing an average relative error in the bonding force of 8.71%. Vogel impact energy modelling was applied to the kernel threshing process to determine kernel damage. The correlation between the speed of seed grain movement and the occurrence of damage was analysed by post-processing to identify locations with frequent kernel damage in the drum. In-depth data analysis of kernel damage in the threshing drum further elucidates the inherent relationship between kernel velocity and damage extent. The study then focused on applying neural networks to predict damage rates. The comparative evaluation shows that the PSO-LSTM model has better prediction accuracy than LSTM and RNN models, with the PSO-LSTM network achieving an RMSE of 0.096, a R 2 of 99.96%, and a final damage rate of 2.41% in validation tests. Threshing experiments were conducted to verify the model, showing a 1.4% discrepancy between predicted and actual damage rates. This study proposes a kernel damage prediction model and provides new insights and directions for the structural design of threshing drums. • Applied Vogel model for threshing damage analysis. • Seeds are vulnerable to damage at the top and bottom of the drum. • PSO-LSTM outperforms LSTM, RNN in damage prediction. • Verified model accuracy with practical threshing experiments. • Identified optimal kernel number through repose angle tests. [ABSTRACT FROM AUTHOR]

Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana , based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO 2 and O 2 , and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO 2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell. • We developed a Resource Balance Analysis (RBA) model of the plant cell. • RBA accurately predicts the resource allocation of A. thaliana photosynthetic cell. • Sensitivity analyses highlighted the most influential model parameters. • RBA captures the macroscopic behavior of the Farquhar model for abiotic constraints. [ABSTRACT FROM AUTHOR]

We examine behavior in a two-stage group contest where intra-group contests are followed by an inter-group contest. Rewards accrue to the winning group, with winners of the intra-group contest within that group receiving a greater reward. The model generates a discouragement effect, where losers from the first stage exert less effort in the second stage than winners. In contrast to the related literature, we show that a prior win may be disadvantageous, generating lower profits for first stage winners as compared to losers. We consider exogenous asymmetry between groups arising from a biased group contest success function. Although the asymmetry occurs in the second stage, its effect plays out in the first stage, with higher intra-group conflict in the advantaged group. Experimental results support the qualitative predictions of the model. However, losers from the first stage bear a higher burden of the group contribution than the theoretical prediction. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a model for predicting the risk of prolonged mechanical ventilation (PMV) following surgical repair of acute type A aortic dissection (AAAD). We retrospectively collected clinical data from 381 patients with AAAD who underwent emergency surgery. Clinical features variables for predicting postoperative PMV were selected through univariate analysis, least absolute shrinkage and selection operator regression analysis, and multivariate logistic regression analysis. A risk prediction model was established using a nomogram. The model's accuracy and reliability were evaluated using the area under the curve of the receiver operating characteristic curve and the calibration curve. Internal validation of the model was performed using bootstrap resampling. The clinical applicability of the model was assessed using decision curve analysis and clinical impact curve. Among the 381 patients, 199 patients (52.2%) experienced postoperative PMV. The predictive model exhibited good discriminative ability (area under the curve = 0.827, 95% confidence interval: 0.786-0.868, P < 0.05). The calibration curve confirmed that the predicted outcomes of the model closely approximated the ideal curve, indicating agreement between the predicted and actual results (with an average absolute error of 0.01 based on 1000 bootstrap resampling). The decision curve analysis curve demonstrated that the model has significant clinical value. The nomogram model established in this study can be used to predict the risk of postoperative PMV in patients with AAAD. It serves as a practical tool to assist clinicians in adjusting treatment strategies promptly and implementing targeted therapeutic measures. [ABSTRACT FROM AUTHOR]

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor. • Hybrid framework improves bioprocess model predictive capabilities. • ML-modulated stoichiometric model predicts accurate instantaneous fluxes. • Prediction accuracy of cell state shifts and resource distribution affects concentration profiles. [ABSTRACT FROM AUTHOR]

Removing senescent leaves is vital for boosting greenhouse tomato yield. This study suggests utilising sucrose concentrations in tomato petiole ends as an indicator for assessing leaf senescence. Sucrose concentrations in different leaf positions were analysed during fruiting, pre-harvesting, and harvesting periods. Leaf area and photosynthetically active radiation (PAR) data from tomato plants were input into tomato photosynthesis and respiration models simulating the yield for each compound leaf. The hyperspectral technique enabled rapid and non-destructive determination of sucrose concentration in tomato petioles using a portable spectroradiometer (ASD Fieldspec® 3) that measured spectral reflectance in 120 petiole samples. Raw spectral reflectance underwent various pretreatments. Principal component analysis (PCA) reduced data dimensions, and sucrose-spectral quantitative prediction models were established using support vector regression (SVR), k-nearest neighbour (KNN), and partial least squares (PLS). The best model for sucrose-spectral quantitative prediction was obtained by moving average (MA) preprocessing, PCA dimensionality reduction and PLS algorithm, resulting in determination coefficient of prediction set (Rp2) of 0.98 and residual predictive deviation (RPD) of 7.12. Sucrose quantitative prediction and photosynthetic respiration models exhibited a parallel trend, with assimilation capacity increasing and then decreasing with leaf position, indicating that functional leaves produce the most assimilates. Young leaves, being immature, produce limited assimilates. Despite well-developed senescent leaves, the occlusion by upper leaves hinders the release of photosynthetic potential, resulting in negative net assimilates. These findings provide compelling evidence that spectroscopy can quantitatively predict sucrose concentration in tomato petioles, aiding in effective senescent leaf identification. [Display omitted] • A quantitative relationship between spectral reflectance and sucrose was build. • Identifying senescent leaf by measuring sucrose content in tomato petioles. • Net leaf assimilation validate the senescent leaf identification criterion. [ABSTRACT FROM AUTHOR]

Bacillus species, such as Bacillus subtilis and Bacillus licheniformis , are important industrial bacteria. However, there is a lack of standardized and predictable genetic tools for convenient and reproducible assembly of genetic modules in Bacillus species to realize their full potential. In this study, we constructed a Ribosome Binding Site (RBS) library in B. licheniformis , which provides incremental regulation of expression levels over a 104-fold range. Additionally, we developed a model to quantify the resulting translation rates. We successfully demonstrated the robust expression of various target genes using the RBS library and showed that the model accurately predicts the translation rates of arbitrary coding genes. Importantly, we also extended the use of the RBS library and prediction model to B. subtilis, B. thuringiensis, and B. amyloliquefacie. The versatility of the RBS library and its prediction model enables quantification of biological behavior, facilitating reliable forward engineering of gene expression. • A shRBS library was constructed in Bacillus licheniformis , enabling incremental regulation of expression levels across a 104-fold range. • A predictive model was developed to accurately estimate the expression levels of shRBSs with arbitrary genes. • The shRBS library and its prediction model demonstrate reliability for application in other Bacillus species. • The shRBS library is feasible to regulate multiple genes and achieve desired expression levels, thereby maximizing metabolic flux towards the target products. [ABSTRACT FROM AUTHOR]

In this paper we review, upgrade, and synthesize existing models from evolutionary game theory, all of which aim at explaining human behaviour in the ultimatum game. Our new and improved versions of Gale et al. (1995) , Nowak et al. (2000) , and Rand et al. (2013) avoid shortcomings that the original versions have, one of which is that the results in the first and the last are driven by bias in the mutations. We also compare the predictions of these three models with the existing experimental evidence by looking at properties of the distributions of minimal acceptable offers. We find that the observed distributions do not conform to the predictions from Gale et al. (1995) , Rand et al. (2013) , or any other model in which there is no fitness benefit to rejecting. This does not rule out commitment-based explanations, such as Nowak et al. (2000). [ABSTRACT FROM AUTHOR]

• We propose an approach that quantitatively evaluates the impact of protein pretrained language model (PLM) in protein–ligand interaction (PLI) predictions, which allows us to select the optimal PLM for a given downstream task without exhaustively testing each PLM, thus avoiding the costly computational expense. • The mechanisms underlying the influence of protein PLMs on PLI tasks are explored. Our observations suggest that pre-training serves as a process of embedding prior knowledge, as evidenced by the increased distinctiveness of feature distributions among different tasks after pre-training. • Empirical results on three widely-used protein PLMs (TAPE, ESM-1b, and ProtTrans) across three PLI tasks (PDBbind, Kinase, and DUD-E) demonstrated that protein PLMs indeed enhanced PLI prediction, particularly in the PDBbind and Kinase tasks. Protein-ligand interaction (PLI) is a critical step for drug discovery. Recently, protein pretrained language models (PLMs) have showcased exceptional performance across a wide range of protein-related tasks. However, a significant heterogeneity exists between the PLM and PLI tasks, leading to a degree of uncertainty. In this study, we propose a method that quantitatively assesses the significance of protein PLMs in PLI prediction. Specifically, we analyze the performance of three widely-used protein PLMs (TAPE, ESM-1b, and ProtTrans) on three PLI tasks (PDBbind, Kinase, and DUD-E). The model with pre-training consistently achieves improved performance and decreased time cost, demonstrating that enhance both the accuracy and efficiency of PLI prediction. By quantitatively assessing the transferability, the optimal PLM for each PLI task is identified without the need for costly transfer experiments. Additionally, we examine the contributions of PLMs on the distribution of feature space, highlighting the improved discriminability after pre-training. Our findings provide insights into the mechanisms underlying PLMs in PLI prediction and pave the way for the design of more interpretable and accurate PLMs in the future. Code and data are freely available at https://github.com/brian-zZZ/PLM-PLI. [ABSTRACT FROM AUTHOR]

[Display omitted] When ice accumulates on a surface, it can adversely impact functionality and safety of a platform in infrastructure, transportation, and energy sectors. Despite several attempts to model the ice adhesion strength on ice-shedding materials, none have been able to justify variation in the ice adhesion strength measured by various laboratories on a simple bare substrate. This is primarily due to the fact that the effect of underlying substrate of an ice-shedding material has been entirely neglected. Here, we establish a comprehensive predictive model for ice adhesion using the shear force method on a multi-layered material. The model considers both shear resistance of the material and shear stress transfer to the underlying substrate. We conducted experiments to validate the model predictions on the effect of coating and substrate properties on the ice adhesion. The model reveals the importance of the underlying substrate of a coating on ice adhesion. Most importantly, the correlation between the ice adhesion and the coating thickness are entirely different for elastomeric and non-elastomeric materials. This model justifies different measured ice adhesion across various laboratories on the same material and elucidates how one could achieve both low ice adhesion and high mechanical durability. Such predictive model and understanding provides a rich platform to guide the future material innovation with minimal adhesion to the ice. [ABSTRACT FROM AUTHOR]

To compare performance of Assessment of Different NEoplasias in the adneXa (ADNEX model), Ovarian-Adnexal Reporting and Data System (O-RADS), Simple Rules Risk (SRR) assessment and the two-step strategy based on the application of Simple Rules (SR) followed by SRR and SR followed by ADNEX in the pre-operative discrimination between benign and malignant adnexal masses (AMs). We conducted a retrospective study from January-2018 to December-2021 in which consecutive patients with at AMs were recruited. Accuracy metrics included sensitivity (SE) and specificity (SP) with their 95% confidence intervals (CI) were calculated for ADNEX, O-RADS and SRR. When SR was inconclusive a "two-step strategy" was adopted applying SR + ADNEX model and SR + SRR assessment. A total of 514 women were included, 400 (77.8%) had a benign ovarian tumor and 114 (22.2%) had a malignant tumor. At a threshold malignancy risk of >10%, the SE and SP of ADNEX model, O-RADS and SRR were: 0.92 (95% CI, 0.86–0.96) and 0.88 (95% CI, 0.85–0.91); 0.93 (95% CI, 0.87–0.97) and 0.89 (95% CI, 0.96–0.92); 0.88 (95% CI, 0.80–0.93) and 0.84 (95% CI, 0.80–0.87), respectively. When we applied SR, 109 (21.2%) cases resulted inconclusive. The SE and SP of two-step strategy SR + SRR assessment and SR + ADNEX model were 0.88 (95% CI, 0.80–0.93) and 0.92 (95% CI, 0.89–0.94), SR + ADNEX model 0.90 (95% CI, 0.83–0.95) and 0.93 (95% CI, 0.90–0.96), respectively. O-RADS presented the highest SE, similar to ADNEX model and SR + ADNEX model. However, the SR + ADNEX model presented the higher performance accuracy with the higher SP and PPV. This two-step strategy, SR and ADNEX model applicated to inconclusive SR, is convenient for clinical evaluation. • O-RADS, ADNEX and the two-step strategy (SR plus ADENX) have a sensitivity exceeding 90% for discriminating adnexal masses. • SR plus ADNEX model applicated to inconclusive SR patients have the highest specificity and positive predictive value. • The association between SR and ADNEX model applicated to inconclusive SR patients presented the best performance accuracy [ABSTRACT FROM AUTHOR]

The sowing depth decision is a critical link in variable-depth sowing (VDS). Compared with rule-based sowing depth decisions, model-based methods are more intelligent and flexible under different growing conditions. This study develops a prediction model that predicts the germination and establishment of maize at different soil depths, enabling the rapid determination of the optimum sowing depth by comparing the predicted results. Dependent variables of the prediction model were selected from five seedling quality indexes, which were the emergence rate index (ER) and seedling uniformity index (U n). Independent variables were soil parameters, namely bulk density, soil depth, and soil moisture. Datasets for modelling were obtained from two experimental sites: one (240 data groups) for model training and the other (48 data groups) for model testing. After the classification of dependent variable data, a support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were used to process the training dataset and establish 186 models. Further assessment of the three selected models was performed using a test dataset. Finally, a model embedded in the SVM algorithm was developed, and achieved accuracies of 70.83% and 72.92% when predicting ER and U n. Maize emergence and establishment were effectively improved in the field by applying the developed model to select the optimum sowing depths. • A prediction model was developed to innovate research on sowing depth decision. • The model can predict the growth performance of maize at different soil depths. • Support vector machine was chosen as the modelling algorithm due to high robustness. [ABSTRACT FROM AUTHOR]

Adult granulosa cell tumors (AGCTs) are rare malignancies that accounts for approximately 1% of ovarian neoplasms. As there are currently no well-recognized models for predicting relapse-free survival (RFS), we performed a clinicopathological analysis to identify risk factors for AGCT recurrence. We investigated 130 patients with pathologically diagnosed AGCT as confirmed by the presence of the characteristic FOXL2 C402G mutation. Most patients had International Federation of Gynecology and Obstetrics stage I disease (n = 122, 95.3%). The 10-year RFS rate was 31.4% (22/70) and mean 10-year RFS was 74.4 (95% CI, 65.2–83.7) months. Ten patients experienced recurrence beyond the 10-year follow-up period. Undergoing fertility sparing surgery, an estrogen receptor-α (ERα) score (>0.25), and a Ki-67 index >15% were independent risk factors for recurrence in patients with stage I disease (bias-corrected C-index: 0.776). We constructed a nomogram with well-fitting calibration plots; the areas under the curve (AUCs) for 5-, and 10-year RFS prediction were 0.883 and 0.906 respectively. A simplified model with 3 predictive factors (ERα score, Ki-67 index, and primary surgical procedure) and 2 risk stratification subgroups (low- and high-risk) was constructed; its AUCs for 5-, and 10-year RFS prediction were 0.825 and 0.850 respectively. Kaplan-Meier survival curves showed significant differences in 10-year RFS between the low- and high-risk groups (p < 0.001). The type of primary surgical procedure, ERα score, and Ki-67 index are independent predictors of recurrence for patients with stage I AGCT. Our predictive model based on these factors showed good performance. • We evaluated recurrence risk in patients with adult granulosa cell tumors (AGCTs). • We focused on factors predicting recurrence in patients with stage I AGCT. • Independent predictors were the ERα score, Ki-67 index, and primary surgical procedure. • The histopathological characteristics were not associated with recurrence. • Our predictive model showed good reliability for predicting the risk of recurrence. [ABSTRACT FROM AUTHOR]

Preoperative differentiation of single-gland (SG) versus multigland (MG) primary hyperparathyroidism (PHPT) can assist with surgical planning, treatment prognostication, and patient counseling. The aim of this study was to identify preoperative predictors of SG-PHPT. Retrospective analysis of 408 patients with PHPT who underwent parathyroidectomy at a tertiary referral center. Comprehensive preoperative parameters, including demographic, laboratory, clinical, and imaging results were analyzed. Univariate analysis and binary logistic regression identified preoperative predictors of SG-PHPT. Receiver operator curves were used to analyze the predictive values of existing and novel preoperative predictive models. Elevated parathyroid hormone (PTH) (99.1 pg/mL in SG versus 93.0 pg/mL in MG), elevated calcium (10.8 mg/dL in SG versus 10.6 mg/dL in MG), lower phosphate levels (2.80 mg/dL in SG versus 2.95 mg/dL in MG), and positive imaging (ultrasound 75.6% in SG versus 56.5% in MG; sestamibi 70.8% in SG versus 45.5% in MG) were significantly associated with SG-PHPT. The Washington University Score (a predictive scoring system made from calcium, PTH, phosphate, ultrasound, and sestamibi) and the Washington University Index ([calcium × PTH]/phosphate) were comparable to previous scoring systems used to predict SG versus MG-PHPT. The association of lower phosphate with SG-PHPT is a novel finding. Previously identified predictors of SG-PHPT, including elevated PTH and positive imaging were confirmed. The Washington University Score and Index are comparable to previously described models and can be used to help surgeons predict if a patient may have SG versus MG-PHPT. [ABSTRACT FROM AUTHOR]

The quantum yield (QY) values of crops are important for assessing photosynthetic efficiency. Our hypothesis is that high-throughput prediction models of QY values can be established based on vegetation indices (VIs). The objectives of this research were to predict QY values based on VIs with high correlations and to compare the performances of classical machine learning with that of B-NAS methods. Compared with support vector regression (SVR) and other traditional prediction methods, in this study, a novel prediction model based on an optimised neural architecture search (B-NAS) was proposed, and achieved comparatively better results. In addition, an improved architecture was obtained and compared for ten different search architectures on the experimental dataset. The experimental results showed that the neural network model constructed based on the B-NAS method predicted QY values outperformed the models obtained using SVR and Adaboost methods in terms of all considered performance metrics (R2, RMSE, RPD, Bias). Meanwhile, the proposed model meets the criteria for a class A model. The final B-NAS model was tested on an independent validation set, obtaining similar performance metrics and an accuracy of 92% for post-prediction classification into three equal interval QY classes. An analysis of the neural architecture obtained by B-NAS showed that a four-layer network architecture had certain advantages. As well, the importance of the preprocessing and dropout layers is discussed. • Developed an optimised NAS model for predicting quantum yield of wheat in the field. • Examined the potential of NDVI, GNDVI, LCI and NDRE for prediction of quantum yield. • A B-NAS neural network with four layers and dropout normalisation gave best results. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology. [ABSTRACT FROM AUTHOR]

Near-Infrared spectroscopy (NIR) returns full spectra in the region between 750 and 2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection methods for the development of vineyard water status predictive models are presented. Spectra from grapevine leaves were collected on-the-go (from a moving vehicle) along nine dates during the 2015 season in a commercial vineyard using a NIR spectrometer (1200–2100 nm). Contemporarily, the stem water potential (Ψ stem) was also measured in the monitored vines. A manual selection, based on Variable Importance in Projection scores (VIP scores) to choose the spectrum intervals including the most important wavelengths (interval selection), the locally most important wavelengths in the spectrum (peak selection), as well as the Interval Partial Least Squares (IPLS) were tested as attribute selection methods. The results obtained for the estimation of Ψ stem using the whole spectrum (R2 P = 0.84, RMSEP = 0.167 MPa) were comparable to those yielded by the three attribute selection methods: the interval selection method (R2 P = 0.80, RMSEP = 0.186 MPa), the peak selection method (R2 P = 0.77, RMSEP = 0.201 MPa) and the IPLS (R2 P ∼ 0.62–0.79, RMSEP ∼ 0.186–0.252 MPa). The highest simplification was provided by two IPLS models with three wavelengths and bandwidths of 20 and 4 nm that yielded R2 P ∼0.78 and RMSEP∼ 0.190 MPa. These results corroborate the suitability of a highly reduced selection of NIR wavelengths for the prediction of grapevine water status, and its utility to develop simpler multispectral devices for vineyard water status estimation. [Display omitted] • Reduced NIR spectral information yielded accurate grapevine water status estimation. • Three different methods of NIR attribute selection were tested and compared. • Model of 1418–1422 nm, 1496–1500 nm & 1688–1692 nm worked similar to full spectrum. • NIR multispectral devices could be deployed for vineyard water status assessment. [ABSTRACT FROM AUTHOR]

Unplanned reoperation is an undesirable outcome with considerable risks and an increasingly assessed quality of care metric. There are no preoperative prediction models for reoperation after an index surgery in a broad surgical population in the literature. The Surgical Risk Preoperative Assessment System (SURPAS) preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict unplanned reoperation has not been assessed. This study's objective was to determine whether the SURPAS model could accurately predict unplanned reoperation. This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database, 2012-2018. An unplanned reoperation was defined as any unintended operation within 30 d of an initial scheduled operation. The 8-variable SURPAS model and a 29-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program nonlaboratory preoperative variables, were developed using multiple logistic regression and compared using discrimination and calibration metrics: C-indices (C), Hosmer–Lemeshow observed-to-expected plots, and Brier scores (BSs). The internal chronological validation of the SURPAS model was conducted using "training" (2012-2017) and "test" (2018) datasets. Of 5,777,108 patients, 162,387 (2.81%) underwent an unplanned reoperation. The SURPAS model's C-index of 0.748 was 99.20% of that for the full model (C = 0.754). Hosmer–Lemeshow plots showed good calibration for both models and BSs were similar (BS = 0.0264, full; BS = 0.0265, SURPAS). Internal chronological validation results were similar for the training (C = 0.749, BS = 0.0268) and test (C = 0.748, BS = 0.0250) datasets. The SURPAS model accurately predicted unplanned reoperation and was internally validated. Unplanned reoperation can be integrated into the SURPAS tool to provide preoperative risk assessment of this outcome, which could aid patient risk education. • Reoperations increase morbidity and may be a quality issue. • Preoperative prediction of reoperation is informative to surgeons and patients. • 8-variable Surgical Risk Preoperative Assessment System model predicts reoperation as well as 29-variable National Surgical Quality Improvement Program model. • The Surgical Risk Preoperative Assessment System model shows good internal chronological validation. [ABSTRACT FROM AUTHOR]

There are massive jobs submitted in the supercomputer, and the job management system is typically deployed to schedule these jobs and allocate compute resources. FCFS (First Come First Serve) is a popular scheduling policy and the job's priority is determined based on the arrival time. However, under the FCFS strategy, if existing idle resources cannot meet the requirement of the head job in the waiting queue, they cannot be allocated to other jobs, which suffers from resource waste. To optimize the resource utilization, the backfilling method is proposed, which allocates the reserved idle compute nodes to a small-size and short-running non-head job, on the premise of not delaying the original head job. Obtaining the job's runtime in advance is necessary for backfilling and the traditional method relies on the user's estimation. Unfortunately, the estimated runtime provided by users is generally overestimated. Many studies extract features from historical job logs and adopt machine learning to predict the runtime. However, traditional features are insufficient to characterize the job. In this paper, we collect job logs from two supercomputers and present a novel runtime prediction framework called PREP. PREP explores the job's running path as a new feature, which implies plentiful information about the job's properties, such as the user, the project, the scale of data sets, and the parameters used. As there is a strong correlation between the job's runtime and its running path, we group jobs with similar paths into a cluster and train a runtime prediction model for each cluster respectively. Extensive evaluations demonstrate that introducing the new feature can achieve higher prediction accuracy (88.5% and 82.3% in two production supercomputers respectively), and our framework has a more desirable prediction performance than other popular strategies like Last-2 and IRPA. In addition, the predicted runtime is inserted into the real job trace of a slurm simulator to verify the advantages of PREP. • PREP explores the job's running path as a new feature to support runtime prediction. • A strong correlation between the job's runtime and its running path is evaluated. • Multiple machine learning algorithms are adopted and compared when predicting. • PREP framework suits different types of supercomputers and gain desirable prediction result. [ABSTRACT FROM AUTHOR]

Drain fluid amylase (DFA) levels have been used to predict clinically relevant postoperative pancreatic fistula (CR-POPF) and guide postoperative drain management. Optimal DFA cutoff thresholds vary between studies, thereby prompting investigation of an alternative assessment technique. As DFA measurements could, in theory, be distorted by variations in ascites fluid production, we hypothesized that adjusting DFA for volume corrected drain fluid amylase (vDFA) would improve CR-POPF predictive models. A single-institution retrospective cohort study of patients, who underwent pancreatoduodenectomies (PD) and distal pancreatectomies (DP) between 2013 and 2019, was performed. DFAs and vDFAs were measured on postoperative day (POD) 3. Clinicopathologic variables were compared between cohorts by univariable and multivariable analyses and Receiver operating characteristic (ROC) curves. Patients developing a CR-POPF were more likely to be male and have elevated DFA, vDFA, and body mass index (BMI). vDFA use did not contribute to a superior CR-POPF predictive model compared to DFA–a finding consistent on subanalysis of surgery type PD versus DP. In CR-POPF predictive models, DFA, vDFA, and male sex significantly improved CR-POPF predictive models when considering both surgery subtypes, while only DFA and vDFA significantly improved models when cohorts were segregated by surgery type. Postoperative DFA remains a preferred method of predicting CR-POPF as the proposed vDFA assessment technique only adds complexity without increased discriminability. [ABSTRACT FROM AUTHOR]

Postoperative pneumonia (POP) is a common complication following lung cancer surgery and is associated with increased hospitalization costs and mortalities. We aimed to identify risk factors associated with POP and to develop a reliable predictive model. Patients who underwent lung cancer surgery between January 2015 and December 2021 in our hospital were enrolled. Least absolute shrinkage and selection operator regression analysis was used to select predictors of POP. Multivariable logistic regression was performed to construct the nomogram. Bootstrap resampling was conducted for internal validation. The performance of the model was evaluated by discrimination and calibration. A total of 5269 consecutive patients were enrolled. POP occurred in 1.7% of patients (92/5269). Five independent predictors were identified: age, predicted forced expiratory volume in 1 s, predicted diffusing capacity of the lungs for carbon monoxide, tuberculosis history, and surgery duration. The multivariable regression model showed good discrimination (C-index: 0.821, 95% confidence interval, 0.783-0.859), which was well validated by internal validation. The calibration curve illustrated good agreement between the predicted probability and observed probability of POP. Based on the easily available risk factors, our nomogram could predict the risk of POP with good discrimination and calibration. The model has good clinical practicability, enabling precise and targeted interventions to reduce the incidence of POP in high-risk patients. [ABSTRACT FROM AUTHOR]

We investigate self-serving belief distortion about dominant norms of honesty. Consider an environment where the subject can earn a monetary reward by lying. In contrast to the existing literature on motivated beliefs, we do not focus on distortion in one dimension alone, but instead consider beliefs in two dimensions: empirical (what other people do) and normative (what other people approve of). Our experimental findings are consistent with the predictions of a dual-self model in which conditional norm-followers strategically distort their beliefs to justify self-serving behavior. We argue that the asymmetry between what we infer from empirical as opposed to normative information is a key ingredient of belief distortion in our context: widespread honest behavior is a strong indicator of disapproval of lying (and thus that a norm of honesty is followed), but the opposite does not hold. Taken together, we show why, when, and which norm-relevant beliefs are strategically distorted. [ABSTRACT FROM AUTHOR]

To evaluate AERMOD for predicting livestock odour dispersion in the Canadian climate and flat terrain, 22 odour plumes were measured around a broiler barn for two days with distinct atmospheric stability classes, namely Aug 31st with C & D and Sep 2nd with A & B. The diurnally varying odour emission rates were measured and input for the modelling. The relationship between odour concentration (OC) and intensity (OI) previously established for this broiler barn was utilised to convert the measured OI to OC or modelled OC to OI. For comparing the hourly modelled predictions and 10-min field measurements, two peak-to-mean ratios (PTM) including a constant factor of 4 (PTM C) and a variable factor dependent on the atmospheric stability (PTM V) were applied. However, neither of them could sufficiently diminish the significance in the paired differences between model predictions and field measurements (p < 0.05). The efficacy of scaling factors specifically developed for the corresponding atmospheric stability classes based on the PTM adjustments were demonstrated, leading to an average OI agreement of 76% for Aug 31st but 97% for Sep 2nd, regardless of the PTM type. The choice of scaling factors could significantly influence the model performance. For Aug 31st, SF Geo was favourable for improving model predictions over short distances (within 300 or 400 m), otherwise SF All should be used for guaranteeing insignificant paired differences. In contrast, SF Geo was consistently preferred for Sep 2nd in terms of improving or at least retaining the acceptable percentage of FB i besides narrowing the paired differences. • AERMOD was assessed for predicting livestock odour dispersion in a cold flat region. • Previously obtained diurnal OE and OC–OI relationship of the broiler barn was used. • Constant or atmospheric stability based PTM was poor in adjusting model accuracy. • Scaling factor should be specifically derived for each atmospheric stability class. • Average OI agreement was 76% for Aug 31st (C & D) but 97% for Sep 2nd (A & B). [ABSTRACT FROM AUTHOR]

Generally, the computational grid consists of a large number of computing nodes, some of them are idle due to the uneven geographical distribution of computing requirements. This may cause workload unbalancing problems, which affect the performance of large-scale computational grids. In order to balance the computing requirements and computing nodes, we propose a job scheduling algorithm based on the workload prediction of computing nodes. We first analyze the causes of workload imbalance and the feasibility of reallocating computing resources. Secondly, we design an application and workload-aware scheduling algorithm (AWAS) by combining the previously designed workload prediction model. To reduce the complexity of the AWAS algorithm, we propose a parallel job scheduling method based on computing node workload prediction. The experiments show that the AWAS algorithm can balance the workload among different computing nodes on the real-world dataset. In addition, we propose the parallelism of workload prediction model from the perspective of internal structure and data set to make AWAS apply to more computing nodes of the large-scale computing grids. Experimental results show that the combination of the two can achieve satisfactory acceleration efficiency. • In this paper, we propose an application and grid computing nodes workload aware scheduling algorithm (AWAS). • AWAS optimizes grid loads using greedy and heuristic methods. • This paper parallelizes the scheduling process of AWAS to improve its efficiency. • The experiment results show that the AWAS can achieve workload balancing in China National Grid. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has widely been used for plant phenotyping applications. Calibration transfer is crucial for eliminating the prediction drift when a model developed in one plant phenotyping system needs to be deployed in another system. In this study two approaches (Domain Adversarial Neural Network - DANN and Adversarial Discriminative Domain Adaptation – ADDA) for transferring a deep learning model across hyperspectral imaging-based plant phenotyping systems were presented. The proposed techniques employ adversarial learning to minimise the discrepancy between the spectral data of two imaging facilities in the feature space. The calibration transfer analysis was examined by collecting the hyperspectral images of maize plants in two phenotyping facilities equipped with different sensors covering the visible near-infrared (VNIR) region under different lighting conditions. The capability of the suggested methods to transfer a relative water content (RWC) prediction model was investigated. The results showed that the prediction R2 improved from 0.603 to 0.849 and 0.843 and RMSE reduced from 23.52% to 14.15% and 12.73% for DANN and ADDA, respectively. The study showed that both approaches improved the performance of the transferred model compared to a similar deep learning model without adversarial transfer. Visualisation of feature space showed that both methods lead to similar source and target feature distributions, thus indicating that a prediction model developed using these common features can successfully be applied to both imaging facilities. The main benefit of the DANN and ADDA is that they do not need the standard samples to be imaged in two phenotyping systems or the collection of labelled data to carry out the transfer. The adversarial learning-based calibration transfer can promote the sharing of prediction models across different phenotyping systems. • Differences across phenotyping facilities hinders the application of exiting models. • Calibration transfer between different phenotyping facilities is critically important. • Adversarial domain adaptation based methods were proposed for calibration transfer. • Proposed methods resulted in RWC model shared between two facilities. • Proposed methods can help to eliminate the need of full recalibration. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter. • A multi-step workload prediction model based on Kalman Filter and SVR is proposed. • A dynamic VM consolidation approach based on resource prediction is presented. • A trade-off between SLA violation and energy consumption is pursued. • Analysis study is carried out on the energy consumed by executing our simulation. • Several experiments are conducted on Cloudsim to evaluate our approach. [ABSTRACT FROM AUTHOR]

The accurate prediction of indoor air temperatures is vital for the timely control of thermal environments in livestock buildings. However, ensuring the accuracy of air temperature predictions is challenging due to the uncertainty introduced by external factors and periodic fluctuations. New approaches are required to improve the prediction accuracy of models for indoor air temperatures. A discrete model is developed that incorporates time-period groups (TPGs), the group buffered rolling (GBR) mechanism, and TPG factors. The measured temperature data were divided into four TPGs in chronological order, and the GBR mechanism with TPG factors eliminated fluctuations and periodicity from the time-series data. The efficacy of the proposed model was verified by comparing its results with those of two existing models (the grey model and discrete grey model) and filed experiments in layer hen houses. The traditional model only reflected the increasing trend of original air temperature data, but did not reflect periodic fluctuations. The mean absolute percentage error (APE) was >10% in the traditional model. The GBR mechanism and TPG factors allowed the new model to reflect deviations in the original time-series at each time point in real-time, thus minimizing the APE and improving the prediction performance. The modified model was superior to the traditional models, exhibited higher accuracy and reliable performance (APE < 10%), and accurately captured the periodic fluctuations in air temperature. It is a helpful tool for predicting indoor air temperatures, and may help in the development of improved control strategies for the indoor thermal environment. • A new discrete grey model modified with the group buffered rolling mechanism. • The GBR-DGM shows improved prediction performance for indoor air temperature. • Accurately predicts temporal fluctuations in air temperatures in a layer hen house. • Applications include the development of improved environmental control systems. [ABSTRACT FROM AUTHOR]

In this paper, we present a highly accurate performance estimation methodology that accounts for architecture slack in workloads. Our work leverages the advanced instrumentation available in POWER8 processor that monitors core pipeline activity in relation to off-core memory accesses to build metrics for architecture slack characterization for workloads. Using these metrics, we construct a workload classifier that classifies workloads as core-bound and memory-bound and propose a performance prediction model for change in processor frequency for each class of workload – cPerf and mPerf, respectively. We evaluated these models with SPECCPU and PARSEC benchmark suites on a POWER8 based OpenPOWER system. We observed that the predicted performance with our models has high accuracy (97%) for both CPU and memory intensive benchmarks. We validated that the classifier is suitable to accurately classify phase of workloads during execution intervals. We developed an algorithm that uses classifier for phase classification and prediction models for performance estimation at runtime. We leveraged this algorithm and evaluated the execution time impacts of CPU and memory classified benchmarks. • DVFS for multi-threaded applications using core pipeline metrics. • Core pipeline metrics to accurately classify compute or memory bound workloads. • Monitoring activity of core pipeline units for architecture slack estimation. • Architecture slack for accurate phase classification and performance estimation. [ABSTRACT FROM AUTHOR]

Portable mid-infrared spectroscopy (pMIRS) combined with machine learning was used to predict selected parameters for soil organic carbon (SOC) storage. In particular, SOC, soil inorganic C (SIC), hot-water extractable C (hwC), clay and sand content were predicted for ten vineyards with varying geopedological settings. As a pre-test, spectra were collected from sieved and pressed tablets with 30 and 90 kPa respectively and compared to powdery samples in order to optimise sample preparation. Further, spectra from 30 kPa tablets were used to calibrate prediction models for a sample set (n = 540) of 10 vineyards with pronounced geopedological variation using Support Vector Machines (SVM). The calibrated SVM models performed well with R2 = 0.81–0.98 and RPIQ = 5.20–13.0 for all investigated parameters. Third, two years after the calibration samples, follow-up samples were collected from four of the vineyards. While the models performed excellent for hwC (R2 = 0.93), prediction accuracy for SOC was lower. Segmentation of the total dataset into SIC-free and SIC-containing samples resulted in better predictions of SOC of the first sampling period. For the prediction of the follow-up sampling dates, model performance could not be maintained. We conclude that pMIRS-SVM calibrations are suited for the prediction of parameters related to soil C storage under varying geopedological conditions and may provide potential for future C monitoring. Extending the database with additional samples from geopedological scenarios not included in this dataset may strengthen model robustness and help to evaluate effects of SIC content on model performance. • Compressed soil tablets reduce sample preparation labour for portable mid-infrared spectroscopy. • SVM model calibrations provide robust predictions for parameters related SOC storage under varying geopedological conditions. • Prediction for follow-up sampling via calibrated SVM models highlights the potential for future SOC monitoring purposes. • Arbitrarily occurring SIC influences MIR SVM model performance. [ABSTRACT FROM AUTHOR]

Construction and application of igRNA prediction model. [Display omitted] • We designed an igRNA editing strategy to achieve bystander-less single-base editing. • We used igRNA editing data from 5,000 genomic loci for deep learning. • We designated igRNA Prediction and Selection AI models (igRNA-PS) • igRNA-PS can predict the major editing site, optimal igRNA and single-base editing efficiency. CRISPR derived base editing techniques tend to edit multiple bases in the targeted region, which impedes precise reversion of disease-associated single nucleotide variations (SNVs). We designed an imperfect gRNA (igRNA) editing strategy to achieve bystander-less single-base editing. To predict the performance and provide ready-to-use igRNAs, we employed a high-throughput method to edit 5000 loci, each with approximate 19 systematically designed ABE igRNAs. Through deep learning of the relationship of editing efficiency, original gRNA sequence and igRNA sequence, AI models were constructed and tested, designated igRNA Prediction and Selection AI models (igRNA-PS). The models have three functions, First, they can identify the major editing site from the bystanders on a gRNA protospacer with a near 90% accuracy. second, a modified single-base editing efficiency (SBE), considering both single-base editing efficiency and product purity, can be predicted for any given igRNAs. Third, for an editing locus, a set of 64 igRNAs derived from a gRNA can be generated, evaluated through igRNA-PS to select for the best performer, and provided to the user. In this work, we overcome one of the most significant obstacles of base editors, and provide a convenient and efficient approach for single-base bystander-less ABE base editing. [ABSTRACT FROM AUTHOR]

This study introduces a prognostic model that quantifies infrastructure degradation in highway systems, incorporating the impacts of climate change using an advanced Markovian framework. By assimilating extensive historical maintenance records and detailed climatic data, the model employs a multi-tiered exponential erosion risk framework to enhance predictive accuracy. Our findings indicate a 15–20% acceleration in degradation rates under projected climate scenarios, emphasizing the necessity for climate-adaptive infrastructure management strategies. Utilizing maximal likelihood estimation, the model corrects sample distortion biases, resulting in a 30% improvement in the accuracy of degradation forecasts compared to conventional models. This accuracy enables maintenance cost savings of up to 25% by optimizing repair timings, thus avoiding premature interventions and reducing costs associated with reactive maintenance strategies. The validated model provides a robust tool for strategic planning and adaptive maintenance of highway systems, promoting resilient infrastructure management in the face of evolving climatic conditions. This research ensures that infrastructure professionals can anticipate and mitigate the impacts of climate change, optimizing maintenance budgets and extending the service life of highway assets. • Achieved a 30% improvement in predictive accuracy of highway degradation forecasts • Demonstrated maintenance savings of up to 25% through optimized repair scheduling. • Integrated multi-tiered exponential erosion risk model with real time climate data. • Enhanced infrastructure resilience by accurately forecasting degradation paths. • Developed a robust tool for strategic planning and maintenance of highway systems. [ABSTRACT FROM AUTHOR]

Challenge tests are commonly employed to evaluate the growth behavior of L. monocytogenes in food matrices; they are known for being expensive and time-consuming. An alternative could be the use of predictive models to forecast microbial behavior under different conditions. In this study, the growth behavior of L. monocytogenes in different fresh produce was evaluated using a predictive model based on the Gamma concept considering pH, water activity (a w), and temperature as input factors. An extensive literature search resulted in a total of 105 research articles selected to collect growth/no growth behavior data of L. monocytogenes. Up to 808 L. monocytogenes behavior values and physicochemical characteristics were extracted for different fruits and vegetables. The predictive performance of the model as a tool for identifying the produce commodities supporting the growth of L. monocytogenes was proved by comparing with the experimental data collected from the literature. The model provided satisfactory predictions on the behavior of L. monocytogenes in vegetables (>80% agreement with experimental observations). For leafy greens, a 90% agreement was achieved. In contrast, the performance of the Gamma model was less satisfactory for fruits, as it tends to overestimate the potential of acid commodities to inhibit the growth of L. monocytogenes. • The Gamma concept model for L. monocytogenes growth was tested in fresh produce. • The model performance was very satisfactory for leafy greens (90% of the cases). • Less satisfactory (71.8%) was the performance of the model for fruits. • The failure predictions of the model are discussed including the impact of other factors. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of components is a crucial aspect of mechanical systems prognostic and health management. However, during the online RUL prediction of components, the existing method cannot use the prediction results for feedback and optimize the prediction model. To address this issue, this paper proposes a framework for RUL prediction with adaptive dynamic feedback. First, a feedback multi-feature fusion (FMFF) method is designed. It introduces a series of non-exceeding threshold positive constants, that is fraction thresholds (FT), to perform division of the degradation state dimension. Besides, the first hitting time (FHT) from the current moment to FT is the fraction remaining useful life (FRUL). It is used to construct and update the fusion factors. Then, the feedback multi-model selection (FMMS) method is proposed for adaptive feedback and to select the degradation model based on the FRUL prediction results in different stages. The probability density function of RUL based on the FHT is calculated based on the FMMS method to realize the RUL prediction of the component. Finally, the effectiveness and superiority of the proposed framework are validated based on XJTU-SY dataset and run-to-failure test. [ABSTRACT FROM AUTHOR]

• The difference in median response time (ΔMRT) between negative faces and neutral shapes can be utilized in quantifying individual differences in the speed of emotional facial discrimination. • Negative faces task condition functional connectivity (FC) predicted individual ΔMRT in healthy adults and adolescents. • Negative faces task condition FC performed better in predicting these individual differences compared to resting-state FC. Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents a physics and data co-driven surrogate modeling method for efficient rare event simulation of civil and mechanical systems with high-dimensional input uncertainties. The method fuses interpretable low-fidelity physical models with data-driven error corrections. The hypothesis is that a well-designed and well-trained simplified physical model can preserve salient features of the original model, while data-fitting techniques can fill the remaining gaps between the surrogate and original model predictions. The coupled physics-data-driven surrogate model is adaptively trained using active learning, aiming to achieve a high correlation and small bias between the surrogate and original model responses in the critical parametric region of a rare event. A final importance sampling step is introduced to correct the surrogate model-based probability estimations. Static and dynamic problems with input uncertainties modeled by random field and stochastic process are studied to demonstrate the proposed method. • Physics-data-driven surrogate modeling method for estimating small probabilities. • Fusion of physical model, error correction, active learning, and importance sampling. • Highly efficient for rare event simulation with high-dimensional input uncertainties. [ABSTRACT FROM AUTHOR]

Investigating the complex interactions among physicochemical variables that influence the adsorptive removal of pollutants is a challenge for conventional one-variable-at-a-time (OVAT) batch methods. The adoption of machine learning-based chemometric prediction models is expected to be more accurate than the conventional method. This study proposed a novel modeling framework for predicting and optimizing the adsorptive removal of N-Nitrosodiphenylamine (NDPhA). Initially, models were trained by using OVAT data, with their hyperparameters subsequently fine-tuned through Bayesian optimization. In the second phase, the particle swarm optimization (PSO) technique was adopted to identify optimal parameters, specifically time, concentration, temperature, pH, and dose, to ensure the highest removal. The adopted analytical method enhances both prediction accuracy and removal efficiency. Utilizing OVAT data for NDPhA removal, the XGBoost regressor significantly outperformed other models. With a correlation coefficient of 0.9667 in the testing dataset, the XGBoost model exhibited its accuracy, emphasized by its low mean squared errors of 28.45 and mean absolute errors of 0.0982. Feature importance analysis consistently identified time and concentration as the most critical factors across all models. • Develops optimized ensemble models for adsorptive removal of N-Nitrosodiphenylamine. • One-variable-at-a-time (OVAT) experiments are conducted to get test/train data. • Hyperparameters optimization guarantees robust ensemble model development. • Time and concentration are most important parameters in predicting model. • XGBoost shows better performance compared to other models. [ABSTRACT FROM AUTHOR]

[Display omitted] Guidance on how to evaluate accuracy and algorithmic fairness across subgroups is missing for clinical models that flag patients for an intervention but when health care resources to administer that intervention are limited. We aimed to propose a framework of metrics that would fit this specific use case. We evaluated the following metrics and applied them to a Veterans Health Administration clinical model that flags patients for intervention who are at risk of overdose or a suicidal event among outpatients who were prescribed opioids (N = 405,817): Receiver – Operating Characteristic and area under the curve, precision – recall curve, calibration – reliability curve, false positive rate, false negative rate, and false omission rate. In addition, we developed a new approach to visualize false positives and false negatives that we named 'per true positive bars.' We demonstrate the utility of these metrics to our use case for three cohorts of patients at the highest risk (top 0.5 %, 1.0 %, and 5.0 %) by evaluating algorithmic fairness across the following age groups: <=30, 31–50, 51–65, and >65 years old. Metrics that allowed us to assess group differences more clearly were the false positive rate, false negative rate, false omission rate, and the new 'per true positive bars'. Metrics with limited utility to our use case were the Receiver – Operating Characteristic and area under the curve, the calibration – reliability curve, and the precision – recall curve. There is no "one size fits all" approach to model performance monitoring and bias analysis. Our work informs future researchers and clinicians who seek to evaluate accuracy and fairness of predictive models that identify patients to intervene on in the context of limited health care resources. In terms of ease of interpretation and utility for our use case, the new 'per true positive bars' may be the most intuitive to a range of stakeholders and facilitates choosing a threshold that allows weighing false positives against false negatives, which is especially important when predicting severe adverse events. [ABSTRACT FROM AUTHOR]

Cadmium, as a typical heavy metal, has the potential to induce soil pollution and threaten human health through the soil-plant-human pathway. The conventional evaluation method based on the total content in soil cannot accurately represent the content migrated from the food chain to plants and the human body. Previous studies focused on the process of plant enrichment of heavy metals in soil, and very few studies directly predicted human exposure or risk through the labile state of Cd in soil. Hence, a relatively accurate and convenient prediction model of Cd release and translocation in the soil-rice-human system was developed. This model utilizes available Cd and soil parameters to predict the bioavailability of Cd in soil, as well as the in vitro bioaccessibility of Cd in cooked rice. The bioavailability of Cd was determined by the Diffusive Gradients in Thin-films technology and BCR sequential extraction procedure, offering in-situ quantification, which presents a significant advantage over traditional monitoring methods and aligns closely with the actual uptake of heavy metals by plants. The experimental results show that the prediction model based on the concentration of heavy metal forms measured by BCR sequential extraction procedure and diffusive gradients in thin-films technique can accurately predict the Cd uptake in rice grains, gastric and gastrointestinal phase (R2=0.712, 0.600 and 0.629). This model accurately predicts Cd bioavailability and bioaccessibility across the soil-rice-human pathway, informing actual human Cd intake, offering scientific support for developing more effective risk assessment methods. [Display omitted] • The DGT technique was a better approach to assess bioavailable Cd in soil. • The model coupled with C DGT-Cd and C F1+F2-Cd accurately predicted the bioaccessibility of Cd. • The model-based health risk assessment results aligned well with the UBM-based results (RSME < 0.01). [ABSTRACT FROM AUTHOR]

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC. • This study has identified clearly different profiles of cfDNA fragmentation profile and end motifs of the CRC patients compared to the healthy individuals or patients with colorectal benign disease. • cfDNA fragmentation profiles exhibit a better diagnostic value for the detection of early-stage CRC. • The genome-wide cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. • cfDNA fragmentation profiles may potentially become a noninvasive assay for the detection and stratification of CRC. [ABSTRACT FROM AUTHOR]

[Display omitted] Thin walled and high rib components (TWHRC) are widely used for rocket cabins, aircraft wings, panels and frames in aerospace equipment as critical load bearing structures. This paper is aimed to develop a novel multi-DOF envelope forming machine (MEFM) with parallel kinematic mechanism (PKM) to realize efficient manufacturing of TWHRC with high performance. Since the heavy load MEFM with PKM has many total differences with the light load PKM, such as dynamic error and precision control, this paper conducts research on dynamic error prediction modelling and precision control method for this novel heavy load MEFM. Firstly, the configuration of MEFM is developed and the kinematic model of MEFM is established. Then, a RFC (rigid-flexible coupling) and RFE (rigid finite element) coupling dynamic model of MEFM is established considering time-variation of kinematic configuration caused by large deformation of all the kinematic elements. Based on the dynamic model, the dynamic deformation and error of MEFM are predicted and their generation mechanisms are revealed. It is found that the linear compression deformation of two-force links is the largest among all the kinematic elements, and it is the most sensitive to the heavy forming load and the most insensitive to the installation error of strain gauges, thus suiting for the feedback of large deformation of MEFM. Based on the above findings, a novel link strain feedback and dynamic error compensation control method is proposed. Finally, experiments under different control methods are conducted, showing that the motion precision of envelope tool and forming precision of formed components under the proposed control method are remarkably improved compared with that under the traditional control method (The geometric deviation of formed components under the proposed control method is remarkably reduced to −18 ∼ 32um from −64 ∼ 54um under the traditional control method). The experimental results verify that the proposed dynamic error prediction model and link strain feedback control method for heavy load MEFM are reasonable. [ABSTRACT FROM AUTHOR]

Transformer-based advancements have shown great promise in solving multi-task learning on dense prediction tasks. Well-designed task interaction modules of these methods further improve the performances by effectively transferring contextual information between tasks. However, many of these methods do not leverage the target task to guide contextual information from the source task. We propose the Task Fusion Upward Transformer (TFUT) model for multi-task learning on dense prediction. To facilitate task interaction, we introduce the Asymmetric Cross Task Interaction module, which utilizes asymmetric transmission in attention. During similarity calculations, the model leverages the target task to guide the expression of contextual information from the source task, ensuring effective transmission of the context information. In order to avoid the loss of detail and the discontinuity of gradient in upsampling, the Upward Transformer Decoder is designed to extract and align multi-scale features using multi-level convolution. The effectiveness of the proposed model has been demonstrated through experiments on the NYUD-v2 dataset and the PASCAL Context dataset. The experimental results show that this model has achieved optimal performance in various single task and multi-task scenarios. • Propose a Transformer based multi-task model on dense prediction. • Propose an asymmetric attention based task interaction method with task guidance. • Design high-quality and low-cost upsampling method to avoid image detail loss. • Incorporate CNN into Transformer to model both local objects and global spatial relationships simultaneously. • Achieve optimal multi-task performance on public datasets NYUD-v2 and PASCAL Context. [ABSTRACT FROM AUTHOR]