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This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester. Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers. • The paper examines spatial transferability of mode choice models between eHUBS cities. • Model scaling resulted in improvement of disaggregate measures of transferability. • Updating ASC improves aggregate mode share predictions of transferred model. • Scaled mixed logit model with updated ASC explain 75% of overall behaviour. • Transferability of mixed logit model gives better results as compared to MNL model. [ABSTRACT FROM AUTHOR]

Accurate estimation of natural gas consumption plays a crucial role in energy planning, economic stability, environmental protection, and investment decisions. Despite numerous predictive studies related to natural gas consumption, most of these studies focus on univariate prediction tasks, lacking a reasonable and efficient multivariate prediction approach. To obtain precise forecasting results, this paper constructs a new adaptive grey multivariate prediction model called AGMPM(r,N) based on the fractional-order accumulation operation and grey system theory. It is found that AGMPM(r,N) can degenerate into some grey multivariable prediction models by replacing its own hyperparameters, which reflects its uniformity. In addition, the new model is unbiased for some special time series. In particular, the grey wolf optimizer is used to facilitate the model solution process. To validate the effectiveness of AGMPM(r,N), AGMPM(r,N) and 27 competing algorithms (9 natural gas consumption prediction methods, 10 multivariate prediction models, 7 machine learning algorithms and a statistical predictive model) are used to study the natural gas consumption in China. In addition, ablation experiments are executed. The results of experimental analysis show that the MAPE and MAE of AGMPM(r,N) are 2.795% and 89.880, respectively, which are superior to all competing methods and ablation models. [ABSTRACT FROM AUTHOR]

The chiller energy consumption prediction plays an important role in reducing the building energy consumption. Continuously improving the performance of the prediction model is the key to ensuring the accuracy of the chiller energy consumption prediction. As a result, a new improved ensemble model of the chiller energy consumption prediction is proposed, based on the idea of algorithmic ensemble aided by an attention mechanism (AM). To guarantee that the proposed model can see information from many spatial and structural viewpoints, the improved ensemble model incorporates the benefits of several base prediction methods. The suggested approach chooses crucial features to simplify the model input by utilizing AM, thereby reducing the complexity of the samples. The improved ensemble model was experimentally verified in an actual multi-chiller system, and the results showed that the established model could obtain good prediction results. Finally, the proposed model was compared with existing, well-known prediction models. To evaluate the accuracy of these models, the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), variance accounted for (VAF), and coefficient of determination (R 2) were used as the evaluation indices. With an MAE of 2.858, an MAPE of 1.2755 %, an MSE of 20.1878, a VAF of 93.6058, and an R 2 of 0.9923, the findings demonstrate that the improved ensemble model has a greater prediction accuracy than the other well-known prediction methods. The suggested model enhances the variety of empirical model algorithm libraries. [ABSTRACT FROM AUTHOR]

The present study aims to develop an effective risk-prediction score (RPS) to improve screening efficiency and contribute to secondary prevention of colorectal cancer (CRC). Screening for colorectal lesions. 14,398 high-risk individuals aged 50–65 years were included. The baseline characteristics of participants with and without colorectal lesions (CL) were compared using a Chi-squared test. The overall population was randomly split into a training set and a test set in the ratio of 80% and 20%. One-factor and multifactor logistic regression analyses were performed in the training set to construct the RPS (scores of 0–9.62). Area under curve (AUC) was calculated as an estimate of predictive performance using the receiver-operating characteristic (ROC) curve in the test set. In the study population, being male, advanced age, current or previous smoking, weekly alcohol consumption, high body mass index (BMI ≥24 kg/m2), and previously detected colonic polyp were associated with higher risk of CL. Compared to the low-risk group (0–2.31 points), the ORs and 95% confidence intervals (CIs) for the moderate-risk group (2.31–3.85 points) and high-risk group (3.85–8.42 points) were 1.58 (1.44, 1.73) and 2.52 (2.30, 2.76), respectively. For every 1-point increase in score, participants had a 27% increased risk of CL (OR:1.27, 95% CI: 1.24, 1.30). For participants with CL predicted by RPS, the area under the working characteristic curve was 0.61 (P < 0.001). Our RPS can quickly and efficiently identify multiple lesions of the colorectum. Combining RPS with existing screening strategies facilitates the identification of very high-risk individuals and may help to prevent CRC. [ABSTRACT FROM AUTHOR]

Accurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. Cox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. Research findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86–0.98), single (HR = 0.11, 95% CI: 0.02–0.68), having children (one child, HR = 0.07, 95%CI: 0.01–0.87; more than one child, HR = 0.33, 95%CI: 0.04–2.48), living in dormitory (HR = 0.25, 95%CI: 0.08–0.77), non-student (employee, HR = 0.24, 95% CI: 0.07–0.88; non-employee, HR = 0.09, 95% CI: 0.02–0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82–0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23–22.48), living alone (HR = 4.37, 95% CI: 1.60–11.94), having suicide history (HR = 2.51, 95% CI: 1.06–5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00–1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00–1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. A novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures. [ABSTRACT FROM AUTHOR]

It is widely accepted that loneliness is associated with health problems, but less is known about the predictors of loneliness. In this study, we constructed a model to predict individual risk of loneliness during adulthood. Data were from the prospective population-based FinHealth cohort study with 3444 participants (mean age 55.5 years, 53.4% women) who responded to a 81-item self-administered questionnaire and reported not to be lonely at baseline in 2017. The outcome was self-reported loneliness at follow-up in 2020. Predictive models were constructed using bootstrap enhanced LASSO regression (bolasso). The C-index from the final model including 11 predictors from the best bolasso -models varied between 0.65 (95% CI 0.61 to 0.70) and 0.71 (95% CI 0.67 to 0.75) the pooled C -index being 0.68 (95% CI 0.61 to 0.75). Although survey-based individualised prediction models for loneliness achieved a reasonable C-index, their predictive value was limited. High detection rates were associated with high false positive rates, while lower false positive rates were associated with low detection rates. These findings suggest that incident loneliness during adulthood. may be difficult to predict with standard survey data. • We constructed a survey -based model predicting individual level risk of loneliness. • The overall predictive performance of the model was reasonable. • However, high detection rate was accompanied with high false positive rate. • The model included variables related to social relations, but also to poor sleep, female sex and depressive symptoms. [ABSTRACT FROM AUTHOR]

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

The prediction method of MUF (Maximum Usable Frequency) has been extensively deliberated as a pivotal technology in HF (High Frequency) communication. To verify the reliability of MUF prediction models commonly used in HF communications, we evaluated the prediction accuracy of seven typical prediction models based on maximum observed frequency (MOFs) and predicted MUFs. The oblique-sounding (OS) circuit was from Changchun to Jingyang, with the vertical-sounding (VS) station situated in Beijing which is, approximately at the midpoint of this oblique-sounding (OS) circuit. The RMSE (root mean square error) and RRMSE (relative root mean square error) between the MOFs and predicted MUFs by the model were used to evaluate the accuracy of the models. The results of statistical analysis show that: (a) the prediction results of the seven models are consistent with the observed results in trend, among which the prediction results of the Lockwood model (LWM), the nonuniform ionospheric conditions MUF calculation model (NYM), and the model proposed by Istituto Nazionale di Geofisica e Vulcanologia (INGV) are in good agreement with the observed results; (b) In the four seasons, the seasonal distribution of the seven models is significantly different, and the INGV model has a better prediction effect; (c) The INGV model is superior to other models in relatively high solar activity epochs and relatively low solar activity epochs, followed by the LWM model, with little difference between the two models and close prediction accuracy; (d) The INGV model has a better prediction result in four different periods of a day, and the improved simplified MUF calculation model (MSPM) has a great prediction result in the periods of nighttime. [ABSTRACT FROM AUTHOR]

Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them. We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases. Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis. We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery. [ABSTRACT FROM AUTHOR]

Preoperative planning has gained popularity in the management of reverse shoulder arthroplasty (RSA). Commercially available software provides 3-dimensional segmentation of scapula and humerus, as well as providing arc of motion for the implanted articulation and identifying potential areas of bony impingement. However, these software algorithms use a fixed scapula model, disregarding the preoperative clinical range of motion (C-ROM) of the patient, be it glenohumeral or scapulothoracic, as well as any soft tissue parameters. This study aims to compare the ROM based on preoperative planning software by using the implant position from postoperative computed tomography (CT) images (predicted ROM using preoperative planning software [P-ROM]), with the C-ROM assessed at minimum of 2 years of follow-up. Preoperative and postoperative CT scans of 46 patients who underwent primary RSA between 2017 and 2021 were analyzed. At the postoperative 2-year review, each patient was assessed for active ROM. Implant size and position based on operative notes and postoperative CT scans were used to replicate the performed surgery in the planning software. Abduction, flexion, and external rotation motion were simulated and recorded. The relationship between C-ROM and P-ROM was investigated using linear regression analysis, Pearson correlation coefficient, and paired t -test. P-ROM was significantly lower than C-ROM at 2 years postoperatively (P <.001), with an average discrepancy of 78° in abduction, 47° in flexion, and 37° in external rotation (C-ROM: abduction 155° ± 21° [80°-180°]; flexion 160° ± 17° [90°-180°]; external rotation 52° ± 14° [10°-80°] vs. P-ROM: abduction 77° ± 13° [53°-107°]; flexion 112° ± 25° [67°-180°]; external rotation 15° ± 21° [0°-79°]). The linear regression analysis indicated weak agreement between C-ROM and P-ROM (abduction R2 = 0.03; flexion R2 = 0.01; external rotation R2 = 0.04). Pearson's correlation coefficients revealed weak correlations of −0.18, 0.03, and 0.21 for abduction, flexion, and external rotation, respectively. P-ROM based on preoperative software in its current form does not allow the prediction of the C-ROM at 2 years of follow-up for patients undergoing RSA. [ABSTRACT FROM AUTHOR]

This article presents a hybrid model to predict the positions of the ball screw drive system of machine tool and then modify the trajectory through constructing a pre-compensation method to reduce servo errors in machine motion axes. To achieve this objective, a flexible control model is initially developed to characterize the ball screw drive system, and by leveraging this model, a high-bandwidth controller is constructed, with its physical representation, i.e. the state-space equation, being derived. Subsequently, a data-driven hybrid model is proposed to predict the positions of the ball screw drive system concerning the next multiple time steps from the current time step, and then the predicted positions associated with these steps are utilized as initial conditions to adjust and compensate for the physical model's prediction errors corresponding to these multiple time steps. As a result, a compensated trajectory with high tracking accuracy is generated. Finally, experiments confirm that the proposed prediction method offers superior prediction accuracy and enhanced adaptability, and the pre-compensated trajectory leads to reduced tracking errors. [Display omitted] • A hybrid model is constructed to pre-compensate servo error in the ball screw system. • A high bandwidth controller is designed by incorporating the feedback of the velocity. • A compensated trajectory with high tracking accuracy is generated. • The method can achieve a nice tracking accuracy and improve the bandwidth. • A series of comparative experiments validate the proposed method. [ABSTRACT FROM AUTHOR]

Faced with climate change and increasing environmental pollution, countries around the world are actively exploring strategies for clean, renewable energy. However, under high-pressure conditions, hydrogen leaks can lead to spontaneous combustion, which significantly raises the safety risk of hydrogen applications. This study employs fluid dynamic simulation methods to investigate the development process of shock waves within tubes. It analyzes the effect of release conditions on the propagation speed and shock wave intensity, revealing the impact of shock wave intensity on the temperature distribution and gas composition variation within the hydrogen-air mixture boundary layer. The results show that the tube length and bends have relatively little impact on the shock waves, while discharge pressure and tube diameter significantly affect the propagation speed and intensity of shock waves. Combined with dimensional analysis and data fitting, a predictive model for autoignition of high-pressure hydrogen leakage is established: (P r / P a)((DXr x(H₂) r O₂)) /V s 2) = 1505(X / D)−1.329. • Studied the influence of the discharge conditions on the propagation process of the excitation wave in the tube. • Elucidated the microscopic influence of the leading shock wave on the gas properties at the boundary layer. • Propose a prediction model for spontaneous combustion of high-pressure hydrogen leakage. [ABSTRACT FROM AUTHOR]

Hydrogen-enriched methane (HEM) combustion is an effective method for reducing the consumption of carbonaceous fuels. However, high adiabatic flame temperature of H 2 can cause large NO emissions. To reduce the NO and CO 2 emissions from HEM combustion, the Box-Behnken design (BBD) method combining response surface experimental design, analysis of variance, and multi-nonlinear regression models was adopted. The effects of the H 2 doping ratio (X H2), preheated air temperature (T air), excess air coefficient of burners arranged at the bottom (E x,b), and their interactions on the NO, N 2 O, and CO 2 emissions of a gas-fired boiler were investigated. According to the BBD method, the NO, N 2 O, and CO 2 emissions prediction models with R2 exceeding 0.98 were proposed, respectively. The results showed that the increase of T air had a positive effect on reducing NO emissions, as the NO exhausted from HEM combustion is mainly thermal NO. Compared with T air and E x,b , X H2 was the most significant factor affecting N 2 O and CO 2 emissions, indicating that an increase in X H2 can significantly reduce greenhouse gas emissions. Additionally, the formation and consumption of N 2 O emissions presented a competitive mechanism at X H2 increased from 0 to 0.8, and CO 2 emissions reduced with the increase of X H2 , T air , and E x,b. Furthermore, a comprehensive emission prediction model with NO, N 2 O, and CO 2 emissions was proposed. With the target of minimizing total NO, N 2 O and CO 2 emissions, three groups of combinations for X H2 , T air , and E x,b were obtained from the equal weight, entropy weight method, and criteria importance through the inter-criteria correlation method as 1:1:1, 24:9:17, and 21:11:18. The results showed that for minimize emissions, there is not much difference in the results calculated based on the optimal boundary conditions under different weights. For various reduction targets for nitrogen, carbon, and greenhouse gases, three groups of optimal combinations of X H2 , T air , and E x,b were obtained and their accuracies were verified based on numerical calculations with relative errors below 6%. The proposed comprehensive pollutant emission prediction models are conducive to calculating the pollutant emissions of HEM combustion and guiding the investigation of prediction models considering other operating conditions. • Box-Behnken design method was first adopted to study NO, N 2 O, and CO 2 emissions. • NO, N 2 O and CO 2 emission prediction models for quick calculation were proposed. • A comprehensive emission prediction model for H 2 /CH 4 combustion was proposed. • The optimal boundary combination for less pollutant emissions was obtained. [ABSTRACT FROM AUTHOR]

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction. [ABSTRACT FROM AUTHOR]

The deflagration evolution of premixed CH 4 /H 2 /air mixtures in sudden expansion duct was investigated by explosion experiments. Based on the previous research on different obstacle widths and spaces, the effect of various hydrogen blend ratios and the fuel equivalence ratios on deflagration evolution were further investigated. The results showed that the flame inversion is pronounced for hydrogen blend greater than 40% and the fuel equivalence ratio no less than 1, which is closely related to the increased flame propagation speed. Besides, the results showed that the overpressure in the chamber increases as the hydrogen blend ratio increases. Moreover, the maximum pressure (P red) and the maximum pressure rise rate ((dP/dt) max) are observed at the fuel equivalence ratio for the low hydrogen blend ratio, however, for the high hydrogen blends, the maximum values are observed at slightly higher fuel equivalence ratios. Furthermore, a model was developed to predict the maximum overpressure under conditions of sudden expansion and half-open ducts. • The effect of sudden expansion duct on the deflagration evolution was investigated. • The equivalence ratio for obtaining the maximum pressure is close to 1.2 at high hydrogen blend ratio. • As The hydrogen blend ratio increases, the second explosion pressure becomes high. • The addition of obstacles and hydrogen can promote the flame to inverse. • The pressure prediction model suited for the sudden expansion duct is corrected. [ABSTRACT FROM AUTHOR]

Camber in hot-rolled plates significantly impacts product quality and rolling process stability, making accurate camber prediction crucial. However, it is challenging to measure asymmetric factors impacting camber in real production, hindering the ability of current models to predict and analyze the overall camber of rolled plates. This study proposes a method that combines asymmetric fluctuations of measurable variables with deep learning to predict camber. We developed a data analysis platform for plate processing and constructed a camber dataset using machine vision technology. During the modeling phase, a hot-rolled plate camber prediction model, BiLSTM-AM-Seq2Seq, was proposed, integrating a bidirectional long short-term memory network, an attention mechanism, and a sequence-to-sequence model. Additionally, an improved scheduled sampling method was also introduced to steer model training. Model performance was evaluated with real-world production data, demonstrating superior accuracy and stability. Specifically, the model achieved a mean absolute error of 12.29 mm and a root mean square error of 17.32 mm. Consequently, this model meets the demands of practical production and addresses the need for overall camber prediction of hot rolled plates. • Data-driven prediction model for overall camber shape of rolled plates is proposed. • Modeling using fluctuations of observable variables to address the absence of key asymmetric variables in real production. • Data analysis platform was developed and real data were used to demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

This study presents a simulation model to predict the nozzle surface freezing phenomenon that causes delays in refueling hydrogen vehicles. Nozzle surface freezing is known to occur when vehicles are refueled consecutively. The developed model allows consecutive refueling of vehicles and incorporates the actual nozzle material to accurately calculate the nozzle surface temperature. The simulation results indicate that, under the applied boundary conditions, the nozzle surface temperature reaches freezing conditions during the third refueling cycle, suggesting the potential suitability of using hot air or nitrogen after the second refueling. Future applications include exploring different boundary conditions in the developed hydrogen refueling model to simulate internal refueling pressure, temperature and nozzle icing conditions. This optimisation could facilitate decision making for the implementation of hot air or nitrogen, which could potentially reduce refueling time and contribute to cost savings at hydrogen refueling stations, depending on specific environmental conditions. • Charging hydrogen vehicles consecutively results in icing, causing delays. • A simulation model is proposed for consecutive vehicle charging, incorporating the nozzle where icing occurs. • Icing occurs during charging of three hydrogen vehicles consecutively (with a waiting time of 90 s). • After the second refueling, purging with heating air or similar means is considered appropriate. [ABSTRACT FROM AUTHOR]

Accurate estimation of gas hydrate formation condition is crucial for many reasons. In one hand, the gas hydrate formation is a promising approach for gas separation, cold energy storage, seawater desalination, etc. On the other hand, in some industries, this phenomenon may cause some challenges, such as pipelines blockage. The current study was undertaken to develop trustworthy predictive tools for methane hydrate formation temperature (MHFT) in the presence of brines. To achieve this target, 1051 experimental data pertinent to the methane hydrate equilibrium in 26 single and multiple brines over an extensive range of operating conditions were amassed from published studies. Four simple factors, including pressure, brine concentration, salt molecular weight and salt melting temperature were defined as the input variables, and the black-box intelligent techniques, including gaussian process regression (GPR), multilayer perceptron (MLP) and radial basis function (RBF) were employed to link MHFT to the mentioned factors. A plethora of graphical and statistically-based assessments were performed to determine the accuracy level of the proposed models. While all intelligent models had excellent performances, the one established by the GPR method showed the best agreements with experimental data, and gave the AARE and R2 values of 0.14% and 99.17%, respectively, in the testing step. The foregoing model was capable of predicting MHFT over a broad range of conditions with relative deviations mostly below 0.10%. Additionally, it showed an excellent performance in describing the physical variations of MHFT versus operating factors. The authority of the analyzed databank and the presented model was asserted via the William's plot investigation. Moreover, a sensitivity analysis was undertaken in order to determine the most fundamental factors in controlling MHFT. Eventually, a simple mathematical correlation was also derived based on the white-box intelligent approach of gene expression programming (GEP), which allowed the accurate estimation of MHFT with an AARE of 0.56%. [Display omitted] • Smart predictive models were presented for methane hydrate formation condition. • 1051 methane hydrate equilibrium data in the presence of 26 brines were analyzed. • The GPR method presented the best predictions with the testing AARE of 0.14%. • The models favorably described the physical variations of MHFT. • A simple and accurate MHFT correlation was derived by the GEP approach. [ABSTRACT FROM AUTHOR]

Innovation in the use of geosynthetics in roadway applications has resulted in the development of several different products, such as asphalt reinforcement geogrids and paving mats to minimize reflective cracks and limit moisture infiltration. For a proper performance of the reinforced asphalt systems, an adequate interface bond strength has proven to be crucial. However, the influence of the different paving interlayer characteristics, combined with tack coat types, and tack coat rates on the interface bond strength remains unclear. In this study, a comprehensive program of interface shear tests was conducted using Leutner shear device and laboratory-prepared reinforced asphalt specimens. The program involved eight geosynthetic types, two tack coat types, and three application rates. Results revealed that the geosynthetic type, tack coat type and rate, as well as the interactions among the parameters significantly affect interface bond strength. Multiple linear regression analysis indicated that geogrid aperture area, geosynthetic thickness, geotextile backing thickness, and the presence of bitumen coating are the most affecting parameters on bond strength. A predictive model for the bond strength based on geosynthetic parameters is presented. Based on literature results for specimens extracted from the field, the proposed predictive models were found to adequately predict interface bond strength. • Geosynthetic type is the most influential factor affecting interface bond strength. • Geogrid aperture, geosynthetic thickness and bitumen coating affects bond strength. • The contribution of bonding mechanisms on shear strength was identified. • Key geosynthetic parameters were identified to predict interface bond strength. • Proposed predictive models showed adequacy to predict interface bond strength. [ABSTRACT FROM AUTHOR]

This study aims to explore the accumulated behavior of reinforced coarse-grained soils through cyclic triaxial tests and to develop a prediction model for the plastic shakedown limit. Cyclic triaxial test results illustrate that the reinforced specimens, especially those incorporating geocells, demonstrate the lowest accumulated axial strain and the highest plastic shakedown limit when compared to unreinforced ones under identical cyclic loading. Additionally, the accumulated axial strain at the plastic shakedown limit for reinforced specimens is determined. This strain is then used to determine the additional confining pressure exerted by geogrid or geocell, employing a function proposed by Yang and Han. By integrating the additional confining pressure into the plastic shakedown criterion for unreinforced specimens, a prediction model for the plastic shakedown limit in reinforced specimens is ultimately established. The model's applicability and the accuracy of computed additional confining pressure values are validated using experimental data. • Cyclic triaxial tests are conducted to investigate the coarse-grained soil. • The influence of reinforcement types on the accumulation behavior is analyzed. • The plastic shakedown limits of two types reinforced specimens are determined. • A prediction model is proposed to determine the soil's plastic shakedown limit. • The accuracy of the proposed model is validated using the experimental data. [ABSTRACT FROM AUTHOR]

To accurately, efficiently and stably predict the thermal displacement of the spindle, a prediction model based on the beluga whale algorithm (BWO) optimized bi-directional long and short-term memory neural network (BiLSTM) is introduced in this paper. Firstly, the thermal characterization and simulation analysis of the spindle are carried out, and the temperature and thermal displacement change characteristics of the spindle are obtained. Then the thermal deformation experiment of the spindle is carried out, and the temperature and displacement sensors are set up reasonably according to the temperature and thermal displacement change characteristics of the spindle, and the experimental data are collected and analyzed. The adaptive and globally convergent BWO is selected to optimize network parameters of BiLSTM, and the BWO-BiLSTM prediction model is constructed by learning the nonlinear correlation characteristics between spindle temperature and axial thermal displacement. The constructed BWO-BiLSTM prediction model is compared with other prediction models, and it is found through analysis that the prediction results output from the BWO-BiLSTM model have better accuracy and stability. The results of the study can provide a certain theoretical basis and technical support in predicting the spindle thermal displacement, which can help to promote the precision machining production of electric spindles. • Spindle thermal characterization and simulation studies are carried out. • Significant changes in axial temperature and thermal displacement are found. • Sensors are set up according to the results of studies. • A BWO-BiLSTM model is built to predict the spindle thermal displacement. [ABSTRACT FROM AUTHOR]

Kinematic coupling, as an efficient form of connection, is frequently employed for the repeated assembly and accurate positioning of replaceable opto-mechanical assemblies within high-power solid-state laser systems. During the kinematic coupling process, uncertainties in the mechanical behavior within the contact region often hinder the attainment of consistent positioning precision, potentially leading to an inability to meet operational requirements. This study aims to enhance the repetitive positioning precision of kinematic couplings. A predictive model for repetitive positioning precision is established, accounting for uncertainties in contact region forces. Based on this model, an optimization design of kinematic couplings is conducted to mitigate these challenges. Initially, accounting for uncertainties in mechanical parameters within the contact region, we formulate a static analysis computational model to elucidate the probabilistic distribution of these parameters. Subsequently, employing Hertz contact theory and Holm-Archard wear theory, we quantify the elastic deformation and equivalent wear for each contact region through the calculation of normal pressures. Building upon this foundation, we establish a predictive model for repetitive positioning precision. Finally, optimization design is carried out on the friction coefficient of contact surfaces and the structural dimensions of positioning elements. The proposed optimization design is validated through experimental verification. The findings demonstrate that optimization of structural dimensions and contact surface friction coefficients resulted in enhanced repetitive positioning precision of 35.9 %, effectively ameliorating the assembly quality. This study contributes a quantifiable analytical approach to the prediction and optimization design of repetitive positioning precision in kinematic couplings. • Repetitive positioning precision is the most critical performance indicator for kinematic couplings. • Simplify the stress, deformation and wear situation in 3D space for symmetrical structures into a plane for analysis. • A new method for predicting repeated positioning precision based on the uncertainty of mechanical parameters is proposed. • The concept that there exists a critical value for the deviation error affected by the initial deviation is proposed. [ABSTRACT FROM AUTHOR]

To explore the influencing factors of urinary tract infection (UTI) in hospitalized patients with spinal cord injury and to construct and verify the nomogram prediction model. This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China, were selected as the research objects, and they were randomly divided into training group (n = 390) and verification group (n = 168) according to the ratio of 7:3, and clinical data including socio-demographic characteristics, disease-related data, and laboratory examination data were collected. Univariate analysis and multivariate logistic regression were used to analyze the influencing factors of UTI in hospitalized patients with spinal cord injuries. Based on this, a nomogram prediction model was constructed with the use of R software, and the risk prediction efficiency of the nomogram model was verified by the receiver operating characteristic curve and calibration curve. Logistic regression analysis showed that the American Spinal Cord Injury Association (ASIA)-E grade (compared with ASIA-A grade) was an independent protective factor for UTI in hospitalized patients with spinal cord injury (odds ratio < 1, P < 0.05), while white blood cell count and indwelling catheter were independent risk factors for UTI in hospitalized patients with spinal cord injury (odds ratio > 1, P < 0.05). Based on this, a nomogram risk predictive model for predicting UTI in hospitalized rehabilitation patients with spinal cord injury was constructed, which proved to have good predictive efficiency. In the training group and the verification group, the area under the receiver operating characteristic curve of the nomogram model is 0.808 and 0.767, and the 95% confidence interval of the area under the receiver operating characteristic curve of the nomogram in the training group and the verification group is 0.760∼0.856 and 0.688∼0.845, respectively, indicating the nomogram model has good discrimination. According to the calibration curve, the prediction probability of the nomogram model and the actual frequency of UTI in the training group and the verification group are in good consistency, and the results of the Hosmer-Lemeshow bias test also suggest that the nomogram model has a good calibration degree in both the training group and the verification group (P = 0.329, 0.067). ASIA classification level, white blood cell count, and indwelling catheter are independent influencing factors of UTI in hospitalized patients with spinal cord injury. The nomogram prediction model based on the above factors can simply and effectively predict the risk of UTI in hospitalized patients with spinal cord injury, which is helpful for clinical medical staff to identify high-risk groups early and implement prevention, treatment, and nursing strategies in time. [ABSTRACT FROM AUTHOR]

Risk assessment is critically important in elective and high-risk interventions, particularly spine surgery. This narrative review describes the evolution of risk assessment from the earliest instruments focused on general surgical risk stratification, to more accurate and spine-specific risk calculators that quantified risk, to the current era of big data. The PubMed and SCOPUS databases were queried on October 11, 2023 using search terms to identify risk assessment tools (RATs) in spine surgery. A total of 108 manuscripts were included after screening with full-text review using the following inclusion criteria: 1) study population of adult spine surgical patients, 2) studies describing validation and subsequent performance of preoperative RATs, and 3) studies published in English. Early RATs provided stratified patients into broad categories and allowed for improved communication between physicians. Subsequent risk calculators attempted to quantify risk by estimating general outcomes such as mortality, but then evolved to estimate spine-specific surgical complications. The integration of novel concepts such as invasiveness, frailty, genetic biomarkers, and sarcopenia led to the development of more sophisticated predictive models that estimate the risk of spine-specific complications and long-term outcomes. RATs have undergone a transformative shift from generalized risk stratification to quantitative predictive models. The next generation of tools will likely involve integration of radiographic and genetic biomarkers, machine learning, and artificial intelligence to improve the accuracy of these models and better inform patients, surgeons, and payers. [ABSTRACT FROM AUTHOR]

This paper examines the predictions of expectations-based reference-dependent models for risk-apportionment tasks that elicit higher-order risk attitudes. We consider some of the most commonly used specifications of Kőszegi and Rabin (2006, 2007) and disappointment aversion models. Our analysis reveals that higher order risky choices exhibited by decision makers defined by those model specifications depend on whether risks to be apportioned in these tasks are symmetric or asymmetric, whether they include small probability outcomes, and on the level of loss aversion. We highlight that some of the predicted choice behaviour in the risk-apportionment tasks differs from the ones in alternative models of decision under risk. We employ experimental data to examine whether choice patterns in the risk apportionment tasks are in line with the predictions of the model specifications described here. We find that only a small proportion of them are consistent with those predictions. [ABSTRACT FROM AUTHOR]

Accurate prediction of crack extension is crucial for ensuring the structural integrity of metal components exposed to diverse loads. While the Gurson model and its extensions are widely accepted for delineating ductile fracture stages, especially in porous materials with a rigid-perfectly plastic matrix, their efficacy diminishes in the presence of strain-hardening matrices. To address this limitation, our study introduces a robust numerical implementation and assessment of Perrin's model. In contrast to Gurson's approach, Perrin's model incorporates two distinct strain-hardening parameters derived from an intricate analysis of a strain-hardening hollow sphere subjected to axisymmetric loading. Moving beyond theoretical discussions, this paper actively engages in the rigorous evaluation of Perrin's model under various scenarios, encompassing isotropic, kinematic, and mixed isotropic-kinematic hardening conditions. The model's effectiveness is convincingly demonstrated through meticulous comparisons between numerical simulations and real-world experimental observations conducted on pre-cracked specimens. Facing challenges related to achieving global elastic-plastic convergence in large-scale simulations, our research introduces a sophisticated approach. Leveraging stiffness tangent moduli ensure the maintenance of quadratic convergence in global Newton method iterations. This not only enhances the reliability of our simulations but also underscores the practical applicability of our findings. The study also focuses on the necking and crack propagation of a cylindrical specimen through meticulous computational modeling. Our mesh captures intricate details and addresses stress gradients, revealing coplanar crack propagation contrary to prior beliefs. The confirmation of the cup-cone fracture effect challenges previous interpretations, emphasizing the critical role of void nucleation. This reinforces the credibility and applicability of our approach in advancing the understanding of material fracture behavior. In summary, this study significantly contributes to existing knowledge in fracture prediction models, offering a robust framework for predicting ductile fracture under substantial deformations. The presented findings pave the way for advancements in structural engineering, providing invaluable insights for optimizing the performance and resilience of metal structures in real-world applications. • We present a numerical implementation and assessment of a model for ductile fracture, considering three hardening scenarios. • We discuss the numerical implementation of the model and address the projection problem algorithm for three hardenings • We introduce a numerical integration strategy for handling significant deformations and rotations in complex structures. • We demonstrate the model effectiveness by comparing numerical simulations and experiments in pre-cracked specimens. [ABSTRACT FROM AUTHOR]

The decision to perform amputation of a limb in a patient with diabetic foot ulcer (DFU) is not an easy task. Prediction models aim to help the surgeon in decision making scenarios. Currently there are no prediction model to determine lower limb amputation during the first 30 days of hospitalization for patients with DFU. Classification And Regression Tree analysis was applied on data from a retrospective cohort of patients hospitalized for the management of diabetic foot ulcer, using an existing database from two Orthopaedics and Traumatology departments. The secondary analysis identified independent variables that can predict lower limb amputation (mayor or minor) during the first 30 days of hospitalization. Of the 573 patients in the database, 290 feet underwent a lower limb amputation during the first 30 days of hospitalization. Six different models were developed using a loss matrix to evaluate the error of not detecting false negatives. The selected tree produced 13 terminal nodes and after the pruning process, only one division remained in the optimal tree (Sensitivity: 69%, Specificity: 75%, Area Under the Curve: 0.76, Complexity Parameter: 0.01, Error: 0.85). Among the studied variables, the Wagner classification with a cut-off grade of 3 exceeded others in its predicting capacity. Wagner classification was the variable with the best capacity for predicting amputation within 30 days. Infectious state and vascular occlusion described indirectly by this classification reflects the importance of taking quick decisions in those patients with a higher compromise of these two conditions. Finally, an external validation of the model is still required. III [ABSTRACT FROM AUTHOR]

• Based on the characteristics of different blood products, seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model (an extended model of ARIMA) and long short-term memory (LSTM) model were used to forecast daily demands of blood components. • The forecasting models, which predicts future demands of different blood components based on historical data, can help managers to overcome the challenges of blood stock control more effectively, thereby reducing blood waste and blood shortages. An adequate blood supply is an important guarantee for saving lives and protecting health. In order to manage the blood supply more effectively when the condition of demand and supply are uncertainty, it is very important to forecast the demands of blood resources. SARIMAX model and LSTM model were integrated into the prediction system of blood station. The collection and supply data of blood components was directly imported into the forecasting models to achieve automatic data update and model update. The forecasting daily demands of apheresis platelets, washing red blood cells (RBCs), suspended RBCs and plasma were recorded from January to June 2023 and compared with real data. The prediction models had good forecasting performances. In the goodness of fit results of apheresis platelet model, the maximum value of coefficient of determination (R2) could reach 87.6%, and the minimum value of the mean absolute percentage error (MAPE) was only 0.0037. The predicted data of washing RBCs could be basically fitted, and the MAPE was 0.0121. For the prediction of suspended RBCs, the R2 was greater than 66%, and the MAPE could be 0.0372. The plasma model generated very high goodness of fit results, with R2 of over 90% and the lowest MAPE of 0.0394. The forecasting models, which predicts future demands of different blood components based on historical data, can help managers to overcome the challenges of blood stock control more effectively, thereby reducing blood waste and blood shortages. [ABSTRACT FROM AUTHOR]

• Satellite model based on attitude error in MRP and angular velocity error. • EMPC applied to pointing an underactuated satellite for large attitude maneuvers. • Satisfactory final pointing error of the algorithm, even in a critical actuator failure scenario. The space sector has become increasingly relevant, especially satellites, due to their ability to access different parts of the planet in a short space of time. The challenge of controlling the satellite's attitude is crucial to the success of a mission. However, the actuator failure condition makes this task even more challenging since the system becomes underactuated, and the control strategies that deal with this scenario and present a satisfactory computational cost are limited unless a simplification of the system dynamics is adopted, such as considering the total angular momentum equal to zero or the diagonal moment of inertia matrix. In this work, extended model predictive control (EMPC) was used to point a satellite in the nadir direction in the presence of actuator failure. This controller uses a model of the system to optimize the control variable over a finite prediction horizon, obtaining a near-optimal solution. The control strategy was tested through a Monte Carlo simulation in two failure scenarios, the first with a failure in the reaction wheel and then also with a failure in the magnetic-torque rod. The results showed that for the first failure scenario, the time required to perform pointing and the final error were lower than for the second failure scenario. The computational load was evaluated through a closed-loop simulation using an embedded processor based on the ARM platform. The simulation shows that the model requires less memory and is more efficient in terms of execution time. The robustness analysis shows that the controller is robust to external disturbance torques and errors of up to 50% in the inertia of the model, with no significant loss in performance. We suggest that the proposed technique is capable of performing pointing with satisfactory accuracy, even with two actuator failures. In addition, this approach can yield substantial cost savings, which is more pronounced for small satellite platforms such as CubeSats. [ABSTRACT FROM AUTHOR]

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader's speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations. • A hierarchical distributed model predictive control for multi-linear motor traction systems is introduced. • The nonlinear disturbance observers are adopted to improve the system's robustness. • A strict proof is constructed for the Lyapunov criterion of asymptotic stability. [ABSTRACT FROM AUTHOR]

Standard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. We describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. Using two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. MDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it. [ABSTRACT FROM AUTHOR]

There is limited consensus regarding management of spinal epidural abscesses (SEAs), particularly in patients without neurologic deficits. Several models have been created to predict failure of medical management in patients with SEA. We evaluate the external validity of 5 predictive models in an independent cohort of patients with SEA. One hundred seventy-six patients with SEA between 2010 and 2019 at our institution were identified, and variables relevant to each predictive model were collected. Published prediction models were used to assign probability of medical management failure to each patient. Predicted probabilities of medical failure and actual patient outcomes were used to create receiver operating characteristic (ROC) curves, with the area under the receiver operating characteristic curve used to quantify a model's discriminative ability. Calibration curves were plotted using predicted probabilities and actual outcomes. The Spiegelhalter z-test was used to determine adequate model calibration. One model (Kim et al) demonstrated good discriminative ability and adequate model calibration in our cohort (ROC = 0.831, P value = 0.83). Parameters included in the model were age >65, diabetes, methicillin-resistant Staphylococcus aureus infection, and neurologic impairment. Four additional models did not perform well for discrimination or calibration metrics (Patel et al, ROC = 0.580, P ≤ 0.0001; Shah et al, ROC = 0.653, P ≤ 0.0001; Baum et al, ROC = 0.498, P ≤ 0.0001; Page et al, ROC = 0.534, P ≤ 0.0001). Only 1 published predictive model demonstrated acceptable discrimination and calibration in our cohort, suggesting limited generalizability of the evaluated models. Multi-institutional data may facilitate the development of widely applicable models to predict medical management failure in patients with SEA. [ABSTRACT FROM AUTHOR]

Our objective was to evaluate the accuracy of predictive models for metritis spontaneous cure (SC) and cure among ceftiofur-treated cows using farm-collected data only, and with the addition of hemogram variables and circulating concentration of metabolites, minerals, and biomarkers (BM) of inflammation measured at time of diagnosis. Data related to parity, calving-related issues, BCS, rectal temperature, and DIM at metritis diagnosis were collected from a randomized clinical trial that included 422 metritic cows from 4 herds in Texas, California, and Florida. Metritis was defined as the presence of red-brownish, watery, and fetid vaginal discharge, and cure was defined as the absence of metritis 14 d after initial diagnosis. Cows were randomly allocated to receive systemic ceftiofur therapy (2 subcutaneous doses of 6.6 mg/kg of ceftiofur crystalline-free acid on the day of diagnosis and 3 d later; CEF) or to remain untreated (control). At enrollment (day of metritis diagnosis), blood samples were collected and submitted to complete blood count (CBC) and processed for the measurement of 13 minerals and BM of metabolism and inflammation. Univariable analysis to evaluate the association of farm-collected data and blood-assessed variables with metritis cure were performed, and variables with P ≤ 0.20 were offered to multivariable logistic regression models and retained if P ≤ 0.15. The areas under the curve for models predicting SC using farm data only and farm + BM were 0.70 and 0.76, respectively. Complete blood count variables were not retained in the models for SC. For models predicting cure among CEF cows, the area under the curve was 0.75, 0.77, 0.80, and 0.80 for models using farm data only, farm + CBC, farm + BM, and farm + CBC + BM, respectively. Predictive models of metritis cure had fair accuracy, with SC models being less accurate than models predictive of cure among CEF cows. Additionally, adding BM variables marginally improved the accuracy of models using farm collected data, and CBC data did not improve the accuracy of predictive models. [ABSTRACT FROM AUTHOR]

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated-health monitoring device (AHMD) from −21 ± 3 to 60 ± 3 d relative to calving to monitor rumination time and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 d in milk (DIM). Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD), were randomly allocated to 1 of 2 treatments: control (CON; n = 62), no treatment at the time of metritis diagnosis (d 0); or ceftiofur (CEF; n = 70), subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC, and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and DIM at metritis diagnosis) and behavior data (i.e., daily averages of rumination time, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with P -values ≤0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, with sensitivity (Se) of 89% and specificity (Sp) of 77%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, with Se of 93% and Sp of 87%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions. [ABSTRACT FROM AUTHOR]

With multiparous Jersey cows, colostrum production seems to be variable. Due to this, we aimed to identify specific variables involved in colostrum production and quality. From 2021 to 2023, data from 28 US farms (415 multiparous Jersey cows) were used to investigate if colostrum yield, IgG concentration (g/L), and IgG yield (g) could be predicted by farm variables and transmitting abilities. With the data collected, multiple regression equations were developed to aid in predicting colostrum yield, IgG concentration, and IgG yield. Colostrum was weighed and sampled for IgG analysis. Dairy Herd Information (DHI), calving, diet, and management information data were compiled. Days below 5°C (D<), days above 23°C (D>), and days between 5 and 23°C (D) were recorded. We evaluated transmitting abilities for milk, fat, protein, and dollars; previous lactation milk yield, fat percent, fat yield, protein percent, protein yield, previous lactation somatic cell score, previous lactation days open, previous lactation days dry, previous lactation days in milk, and previous parity; and current lactation parity, days dry, and calving information, birth ordinal day, and latitude. Colostrum yield, IgG yield, and concentration had 1 added to correct for values = 0. After addition, values >0 were transformed to ln or log 10. Nontransformed variables were also used to develop the model. Variance inflation factor analysis was conducted, followed by backward elimination. The log 10 colostrum yield model (R2 = 0.55; β in parentheses) included herd size (−0.0001), ordinal days (−0.001), ln ordinal days (0.07), latitude (−0.02), dry period length (0.004), D< (−0.005), D (−0.003), time to harvest (0.05), ln time to harvest (−0.35), IgG (−0.004), log 10 IgG (0.46), feedings per day (0.06), ln pasture access (−0.13), and ln previous lactation days open (0.14). The model showed that previous lactation days open contributed the most toward increasing and latitude contributed the most toward decreasing colostrum yield. The IgG model (R2 = 0.21) included herd size (0.02), D> (0.38), ln time to harvest (−19.42), colostrum yield (−4.29), ln diet type (18.00), ln previous lactation fat percent (74.43), and previous parity (5.72). The model showed that previous lactation milkfat percent contributed the most toward increasing and time from parturition to colostrum harvest contributed the most toward decreasing colostrum IgG concentration. The log 10 IgG yield model (R2 = 0.79) included ln ordinal days (0.03), time to harvest (−0.01), colostrum yield (−0.11), ln colostrum yield (1.20), ln pasture access (−0.09), ln previous lactation fat percent (0.53), and previous parity (0.02). The model showed that colostrum yield contributed the most toward increasing IgG yield, followed by previous lactation milkfat percentage. Pasture access contributed the most toward decreasing IgG yield, although the contribution was very small. These models were validated using 39 samples from 22 farms. Actual minus predicted colostrum yield and IgG concentration and yield were 0.89 kg, −21.10 g/L, and −65.15 g, respectively. These models indicate that dry period management and cow information can predict colostrum yield and IgG concentration and yield. [ABSTRACT FROM AUTHOR]

Prediction models are everywhere in clinical medicine. We use them to assign a diagnosis or a prognosis, and there have been continuous efforts to develop better prediction models. It is important to understand the fundamentals of prediction modelling, thus, we herein describe nine steps to develop and validate a clinical prediction model with the intention of implementing it in clinical practice: Determine if there is a need for a new prediction model; define the purpose and intended use of the model; assess the quality and quantity of the data you wish to develop the model on; develop the model using sound statistical methods; generate risk predictions on the probability scale (0-100%); evaluate the performance of the model in terms of discrimination, calibration, and clinical utility; validate the model using bootstrapping to correct for the apparent optimism in performance; validate the model on external datasets to assess the generalisability and transportability of the model; and finally publish the model so that it can be implemented or validated by others. [ABSTRACT FROM AUTHOR]

A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3–17 years in the United States based on clinical manifestations and complex environments. A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples. A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable. A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD. To construct a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD. We employed machine learning algorithms to identify 13 independent prognostic factors of ADHD from multi-dimensional predictors. We successfully developed a highly accurate early ADHD prediction model with a C-index of 0.887. This reliable and user-friendly tool incorporates multiple dimensions of prediction, enabling g parents, teachers, and physicians to identify children at a high risk of ADHD. By empowering parents, teachers, and physicians with the ability to identify children at high risk, we can facilitate early recognition and family management, leading to improved outcomes for children with ADHD. [Display omitted] • We developed a nomogram for ADHD in children aged 3-17, which can aid in early recognition and family management of ADHD. • The nomogram was validated externally by using another database, proving its reliability in accurately predicting ADHD risk. • This nomogram forms a collaborative diagnostic, therapeutic, and management model involving society, family, and school. [ABSTRACT FROM AUTHOR]

Ultrasound (US) is widely used for evaluating various orbital conditions. However, accurately diagnosing malignant orbital masses using US remains challenging. We aimed to develop an ultrasonic feature-based model to predict the presence of malignant tumors in the orbit. A total of 510 patients with orbital masses were enrolled between January 2017 and April 2023. They were divided into a development cohort and a validation cohort. In the development cohort (n = 408), the ultrasonic and clinical features with differential values were identified. Based on these features, a predictive model and nomogram were constructed. The diagnostic performance of the model was compared with that of MRI or observers, and further validated in the validation cohort (n = 102). The involvement of more than two quadrants, irregular shape, extremely low echo of the solid part, presence of echogenic foci, cast-like appearance, and two demographic characteristics (age and sex) were identified as independent features related to malignant tumors of the orbit. The predictive model constructed based on these features exhibited better performance in identifying malignant tumors compared to MRI (AUC = 0.78 [95% CI: 0.73, 0.82] vs. 0.69 [95% CI: 0.64, 0.74], p = 0.03) and observers (AUC = 0.93 [95% CI: 0.90, 0.95] vs. Observer 1, AUC = 0.80 [95% CI: 0.76, 0.84], p < 0.01; vs. Observer 2, AUC = 0.71 [95% CI: 0.66, 0.76], p < 0.01). In the validation cohort, the predictive model achieved an AUC of 0.88 (95% CI: 0.81, 0.94). The ultrasonic-clinical feature-based predictive model can accurately identify malignant orbital tumors, offering a convenient approach in clinical practice. [ABSTRACT FROM AUTHOR]

Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10-fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding. • Data collection for species-specific tissue compositions in cattle, chicken, sheep and swine. • Prediction of tissue:plasma partition coefficients within a factor 10. • Future research needs on species differences in protein binding. [ABSTRACT FROM AUTHOR]

Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning. • Proposed state-of-the-art model to predict the radiotherapy dose intelligently. • Automatic prediction results were stable across medical centers. • Computer-aided dose prediction can accelerate treatment planning. • The model also showed good generalizability for endometrial cancer. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non–small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy. A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011–2019. A logistic regression model was trained for the 2011–2016 curative patient cohort and validated for the 2017–2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan–Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups. 1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01). Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care. • Survival model for inoperable stage I-III nonsmall cell lung cancer. • Federated learning connects seven radiation oncology institutes. • Case-control propensity matched decision support simulation indicates a potential 11% increase in overall survival. [ABSTRACT FROM AUTHOR]

High precision ionospheric Total Electron Content(TEC) prediction is of great significance for improving the accuracy of Global Navigation Satellite System(GNSS), preventing natural disasters, and ensuring wireless communication. Given the varying frequencies of TEC signals, a hybrid CEEMDAN-BiLSTM-PSO-LSSVM-FE model for predicting ionospheric TEC content is proposed in this paper. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the extracted hourly TEC sequence and calculate the fuzzy entropy (FE) of the subsequences. Then, the signal is divided into high-frequency and low-frequency parts based on the fuzzy entropy value, for the high-frequency component, Bidirectional Long Short-Term Memory network (BiLSTM) is used for prediction; for the low-frequency component, Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) is used for prediction. The hourly TEC values collected from six evenly distributed GPS stations in China are used as the main input variable for the proposed model, with solar and geomagnetic activity data used as auxiliary data, using the TEC data from the previous 48 h to forecast the TEC content for the next hour. The performance of the model is evaluated by comparing it with other models under different geographical locations, seasons, solar and geomagnetic activity conditions. Experimental results show that the proposed hybrid model outperforms other models, with a correlation coefficient R2 and root mean square error (RMSE) of 0.99 and 0.20 TECU, respectively. The model effectively overcomes the problem of low TEC prediction accuracy and can provide more precise ionospheric delay correction services. [ABSTRACT FROM AUTHOR]

Major surgery is associated with high complication rates. Several risk scores exist to assess individual patient risk before surgery but have limited precision. Novel prognostic factors can be included as additional building blocks in existing prediction models. A candidate prognostic factor, measured by cardiopulmonary exercise testing, is ventilatory efficiency (VE/VCO 2). The aim of this systematic review was to summarise evidence regarding VE/VCO 2 as a prognostic factor for postoperative complications in patients undergoing major surgery. A medical library specialist developed the search strategy. No database-provided limits, considering study types, languages, publication years, or any other formal criteria were applied to any of the sources. Two reviewers assessed eligibility of each record and rated risk of bias in included studies. From 10,082 screened records, 65 studies were identified as eligible. We extracted adjusted associations from 32 studies and unadjusted from 33 studies. Risk of bias was a concern in the domains 'study confounding' and 'statistical analysis'. VE/VCO 2 was reported as a prognostic factor for short-term complications after thoracic and abdominal surgery. VE/VCO 2 was also reported as a prognostic factor for mid- to long-term mortality. Data-driven covariable selection was applied in 31 studies. Eighteen studies excluded VE/VCO 2 from the final multivariable regression owing to data-driven model-building approaches. This systematic review identifies VE/VCO 2 as a predictor for short-term complications after thoracic and abdominal surgery. However, the available data do not allow conclusions about clinical decision-making. Future studies should select covariables for adjustment a priori based on external knowledge. PROSPERO (CRD42022369944). [ABSTRACT FROM AUTHOR]

We explored the impact of stromal tumor-infiltrating lymphocytes (sTILs) on the prognostic value of an early death model for advanced buccal cancer. We assessed 121 patients with advanced buccal cancer who underwent primary tumor resection at a medical center. Predictors of early death and 5-year overall survival (OS) were analyzed using Cox regression models. Performance of models was evaluated with the Harrell C and Akaike information criterion. The net reclassification improvement of the early death model was also calculated relative to the 5-year OS model for one-year all-cause mortality. A total of 121 patients with advanced buccal cancer were recruited. Mean age was 56.1 ± 9.8 years; 117 (96.7%) patients were male. sTILs ≤30%, clinical nodal disease, pathological nodal disease, poor differentiation, lymphovascular invasion, perineural invasion, WPOI 5, and no adjuvant radiotherapy were risk factors for early death in univariate analysis. In multivariate analysis, clinical TNM, sTILs, clinical nodal disease, poor differentiation, lymphovascular invasion, and no adjuvant RT were independent factors for early death. sTILs, pathological nodal disease, poor differentiation, lymphovascular invasion, and no adjuvant RT were independent factors for early death in the multivariate model with pathological TNM. The discriminatory ability was better for early death model for 1-year all-cause mortality. Finally, incorporation of sTILs into the early death model increased net reclassification by 21% for the clinical TNM model and 28% for the pathological TNM model. Addition of sTILs improved the early death model, which may help physicians to identify high-risk patients for more intensive treatment and follow-up. • Stromal tumor-infiltrating lymphocytes (sTILs) was an independent prognosticator in advanced buccal cancer. • Early death model with incorporation of sTILs had a significant net reclassification improvement for one-year mortality. • High-risk patients identified by early death model might receive more intensive treatment and close follow-up. [ABSTRACT FROM AUTHOR]

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control—cross-coupling control with deep neural network feedforward (NMPC-CCC-DNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPC-CCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller. • The nonlinear model of an asymmetric multi-hydraulic system is established. • The entirety model-type solution is proposed with nonlinear model predictive control and cross-coupling control algorithm. • A novel disturbance compensator based on the inverse deep neural network model is proposed. • Results showed the proposed controller can decrease 60.8% and up to 91.1% sysnchronization root mean square error. [ABSTRACT FROM AUTHOR]