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A nonlinear multivariable highly coupled system that is susceptible to both internal and external perturbations is the permanent magnet synchronous motor. A new reaching law‐based control approach that combines a sliding‐mode disturbance observer and an enhanced two‐vector model predictive current control is suggested in order to enhance the dynamic performance of the permanent magnet synchronous motor and its robustness to system disturbances. First, a new reaching law is developed in this paper to resolve the conflict between the traditional reaching law's chattering of the system and the reaching time of the sliding‐mode surface. This new reaching law not only makes the system's response more rapid but also reduces chattering; second, taking into account the possibility of systems with internal parameter uptake and external load disturbance, a sliding‐mode disturbance observer based on t is developed. Finally, the q‐axis current obtained from the speed sliding‐mode control rate is added to the model predictive current control, and an optimal duty cycle model predictive current control based on space vector modulation is designed because the traditional two‐vector model predictive current control is to optimize the duty cycle and voltage vectors separately, and the switching frequency is not fixed. The method suggested in this research may successfully suppress the sliding‐mode control system chattering and increase the resilience and dynamic response performance of the speed control system, according to simulation findings compared to the traditional reaching law and traditional duty cycle. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The cascaded H‐bridge (CHB) converter can achieve rich functions, such as active rectification, active power filtering, distributed energy storage, etc. The control of the CHB converter is a multiobjective optimization problem. Finite control set model predictive control (FCS‐MPC) is an effective method developed in recent years to solve this problem. However, because of the large amount of calculation, traditional FCS‐MPC cannot be directly used to control the multilevel CHB converter. It is also difficult to tune the weighting factors in traditional FCS‐MPC. To solve these problems, this paper proposes an improved sequential MPC (IS‐MPC) method for the CHB converter. The proposed method divides the control objectives of the CHB converter into different layers according to their priorities and the control degrees of freedom of each layer can be adjusted according to the system state. Simulation and hardware‐in‐the‐loop (HIL) implementation on a three‐phase 7‐level CHB converter shows that the proposed method can effectively control the ac current and submodule voltage of the CHB converter and reduce the switching loss. Moreover, it significantly reduced the calculation burden of traditional FCS‐MPC, and the use of the weighting factors is also avoided. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Exposure to narratives may have beneficial effects on children's social‐cognitive development because narratives provide information about the social world and often require social understanding for story comprehension. In the current study, we examined the influence of narratives presented via different media (books, audiobooks, TV/films) on theory‐of‐mind performance and mental verb comprehension in a sample of 114 three‐ to six‐year‐old preschool children. Parents' reports on the number of (children's) books at home, the overall duration of TV/film and audio media exposure, the frequency of shared book reading, watching children's TV/films and audiobook listening, and parent–child discussions about media content were collected. Children's theory‐of‐mind performance and mental verb comprehension were measured as dependent variables. When gender, age, language skills and parental education were controlled, only the number of children's books, shared book reading frequency, audio‐media exposure and audiobook usage significantly predicted children's theory‐of‐mind scores. None of the media exposure or the parent–child discussion variables had significant incremental effects above the family and child characteristics on mental verb comprehension. [ABSTRACT FROM AUTHOR]

This work studies the application of a sample-based model Monte Carlo Model Predictive Control (MCMPC) to address discontinuous events, such as collisions. The inclusion of contact dynamics in the prediction steps of MCMPC renders it advantageous for the operation of underwater snake robots in restricted environments during rescue missions. The Curvature Derivative Control (CDC) method is employed for the purpose of controlling the joints, with the exception of the head joint. The snake robot body demonstrates adaptability to various environments with the assistance of the CDC. Results obtained from the simulation demonstrate that the snake robot is capable of autonomously navigating from its beginning position outside the region to a pipe as the entrance in order to get inside. The robot successfully traverses through various internal structures within the pipe and ultimately reaches the intended destination, guided by a predetermined cost function of MCMPC. By using the capabilities of CUDA GPU parallel computing, the computational time is substantially lowered. [ABSTRACT FROM AUTHOR]

Objective: To identify and describe existing models for predicting knee pain in patients with knee osteoarthritis. Methods: The electronic databases PubMed, EMBASE, CINAHL, Web of Science, and Cochrane Library were searched from their inception to May 2023 for any studies to develop and validate a prediction model for predicting knee pain in patients with knee osteoarthritis. Two reviewers independently screened titles, abstracts, and full-text qualifications, and extracted data. Risk of bias was assessed using the PROBAST. Data extraction of eligible articles was extracted by a data extraction form based on CHARMS. The quality of evidence was graded according to GRADE. The results were summarized with descriptive statistics. Results: The search identified 2693 records. Sixteen articles reporting on 26 prediction models were included targeting occurrence (n = 9), others (n = 7), progression (n = 5), persistent (n = 2), incident (n = 1), frequent (n = 1), and flares (n = 1) of knee pain. Most of the studies (94%) were at high risk of bias. Model discrimination was assessed by the AUROC ranging from 0.62 to 0.81. The most common predictors were age, BMI, gender, baseline pain, and joint space width. Only frequent knee pain had a moderate quality of evidence; all other types of knee pain had a low quality of evidence. Conclusion: There are many prediction models for knee pain in patients with knee osteoarthritis that do show promise. However, the clinical extensibility, applicability, and interpretability of predictive tools should be considered during model development. [ABSTRACT FROM AUTHOR]

BACKGROUND: In the last decades, emerging evidence has shown that muscle growth is not homogeneous along a muscle head. This phenomenon is known as regional muscle hypertrophy and has led to several questions regarding the implications it may have for health and sports performance. OBJECTIVE: The aim of this study was to determine whether regional hypertrophy can be predicted by surface electromyography (sEMG). METHODS: 36 participants performed two arm exercises (preacher curls and inclined curls) in a random order to muscle failure at 70% of the 1 RM of the bicep curl exercise. As every participant performed a different number of repetitions, Peak sEMG and the integral of the sEMG of the last 3 repetitions was analyzed an compared to previously performed maximal voluntary isometric contractions (MVIC). RESULTS: The independent sample t -tests showed no significant differences neither in the peak nor in the integral of the sEMG between exercises for any given region. CONCLUSIONS: sEMG cannot be used to predict regional hypertrophy. [ABSTRACT FROM AUTHOR]

The dynamic behaviour of wildfires is mainly influenced by weather, fuel, and topography. Based on fundamental conservation laws involving numerous physical processes and large scales, atmospheric models require substantial computational resources. Therefore, coupling wildfire and atmospheric models is impractical for high resolutions. Instead, a static atmospheric wind field is typically input into the wildfire model, either as boundary conditions on the control surface or distributed over the control volume. Wildfire propagation models may be (i) data-driven ; (i i) theoretical ; or (i i i) mechanistic surrogates. Data-driven models are beyond the scope of this paper. Theoretical models are based on conservation laws (species, energy, mass, momentum) and are, therefore, computationally intensive; e.g. the Fire Dynamics Simulator (FDS). Mechanistic surrogate models do not closely follow fire dynamics laws, but related laws observed to make predictions more efficiently with sufficient accuracy; e.g. FARSITE, and FDS with the Level Set model (FDS-LS). Whether theoretical or mechanistic surrogate, these wildfire models may be coupled with or decoupled from wind models (e.g. Navier-Stokes equations). Only coupled models account for the effect of the fire on the wind field. In this paper, a series of simulations of wildfire propagation on grassland are performed using FDS-LS to study the impact of the fire-induced wind on the fire propagation dynamics. Results show that coupling leads to higher Rates of Spread (RoS), closer to those reported from field experiments, with increasing wind speeds and higher terrain slopes strengthening this trend. Aiming to capture the fire–wind interaction without the hefty cost of solving Navier-Stokes equations, a conceptual framework is proposed: 1) a swarm of unmanned aerial vehicles measure wind velocities at flight height; 2) the wind field is constructed with the acquired data; 3) the high-altitude wind field is mapped to near-surface, and 4) the near-surface wind field is fed into the wildfire model periodically. A series of simulations are performed using an in-house decoupled physics-based reduced-order fire propagation model (FireProM-F) enhanced by wind field "measurements". In this proof of concept, wind velocities are not measured but extracted from physics-based Large Eddy Simulations taken as ground truth. Unsurprisingly, higher measurement frequencies lead to more accurate and realistic predictions of the propagating fire front. An initial attempt is made to study the effect of wind measurement uncertainty on the model predictions by adding Gaussian noise. Preliminary results show that higher noise leads to the fire front displaying more irregular shapes and slower propagation. • Higher wildfire model prediction accuracy normally achieved by coupling it with wind model such as Navier-Stokes equations. • Coupled fire-wind models often too computationally expensive for real-time use in operational contexts. • Prediction accuracy of wildfire model uncoupled from wind model enhanced by periodic inputs of updated wind field. • Wind field to be constructed from UAV swarm measurements, surrogated here to velocities obtained from full CFD simulations. • Proposed method to enhance wildfire model predictions robust to Gaussian noise added to represent measurement uncertainty. [ABSTRACT FROM AUTHOR]

Manual co-speech gestures can facilitate language comprehension, especially in adverse listening conditions. However, we do not know whether gestures influence simultaneous interpreters' language comprehension in adverse listening conditions, and if so, whether this influence is modulated by interpreting experience, or by active simultaneous interpreting (SI). We exposed 24 interpreters and 24 bilinguals without interpreting experience to utterances with semantically related gestures, semantically unrelated gestures, or without gestures while engaging in comprehension (interpreters and bilinguals) or in SI (interpreters only). Tasks were administered in clear and noisy speech. Accuracy and reaction time were measured, and participants' gaze was tracked. During comprehension, semantically related gestures facilitated both groups' processing in noise. Facilitation was not modulated by interpreting experience. However, when interpreting noisy speech, interpreters did not benefit from gestures. This suggests that the comprehension component, and specifically crossmodal information processing, in SI differs from that of other language comprehension. [ABSTRACT FROM AUTHOR]

Individual responses to changes in the environment by different species drive a better understanding of the dynamics of diversity and habitat filtering. Hypotheses based on functional and morphometric characteristics of the species are especially useful for accounting for different interpretations of biological responses.This study aimed to determine the responses of semiaquatic species of insects to habitat changes from the elaboration of hypotheses based on the importance of species traits facing environmental variations. We tested these hypotheses with maximum likelihood models to explain the occurrence of the species in streams of the central Amazon region.In a total of 17 collected Gerromorpha species, we used 14 to develop five predictive models of occurrence considering the bionomic characteristics of each species and their possible relationships with habitat changes in 33 streams in the central Amazon region. We used maximum likelihood models to assess the fit of the models to the observed occurrence, with environmental characteristics as covariates affecting the occurrence probability, and sampling effort affecting the detection probability.A total of five species exhibited changes in the probability of occurrence in streams related to an environmental condition (riparian forest disturbance, flow heterogeneity, and surface habitat heterogeneity). The probability of occurrence tended to reduce for four of the five species and increase for one with increase of environmental impacts. Furthermore, the sampling effort covariate did not affect the probability of detection in most species, indicating that the group showed a high detection.The use of functional characteristics of Gerromorpha species to develop ecological hypotheses proved relevant and indicated unexpected relationships, expanding our knowledge of this community of aquatic insects. Therefore, including the individual responses of organisms to environmental variations helps in studies on aquatic biodiversity, both for basic ecology and for conservation and bioassessment. [ABSTRACT FROM AUTHOR]

Background: We aimed to develop a tool for predicting HNF1B mutations in children with congenital abnormalities of the kidneys and urinary tract (CAKUT). Methods: The clinical and laboratory data from 234 children and young adults with known HNF1B mutation status were collected and analyzed retrospectively. All subjects were randomly divided into a training (70%) and a validation set (30%). A random forest model was constructed to predict HNF1B mutations. The recursive feature elimination algorithm was used for feature selection for the model, and receiver operating characteristic curve statistics was used to verify its predictive effect. Results: A total of 213 patients were analyzed, including HNF1B-positive (mut + , n = 109) and HNF1B-negative (mut − , n = 104) subjects. The majority of patients had mild chronic kidney disease. Kidney phenotype was similar between groups, but bilateral kidney anomalies were more frequent in the mut + group. Hypomagnesemia and hypermagnesuria were the most common abnormalities in mut + patients and were highly selective of HNF1B. Hypomagnesemia based on age-appropriate norms had a better discriminatory value than the age-independent cutoff of 0.7 mmol/l. Pancreatic anomalies were almost exclusively found in mut + patients. No subjects had hypokalemia; the mean serum potassium level was lower in the HNF1B cohort. The abovementioned, discriminative parameters were selected for the model, which showed a good performance (area under the curve: 0.85; sensitivity of 93.67%, specificity of 73.57%). A corresponding calculator was developed for use and validation. Conclusions: This study developed a simple tool for predicting HNF1B mutations in children and young adults with CAKUT. [ABSTRACT FROM AUTHOR]

The Vehicle Dynamics Control (VDC) system is designed to enhance vehicle stability by effectively applying corrective yaw moments through differential brake forces during severe maneuvers. The VDC configuration primarily comprises two essential components: the supervisor and coordinators. The supervisor is responsible for determining appropriate corrective yaw moments, while the coordinators decide the tire forces necessary to achieve the desired corrective yaw moments. In modern times, various control schemes, such as model predictive controls, h-infinity controls, and relative controls, have been extensively investigated for the supervisors. However, in contrast, research concerning the coordinator component has not received much attention, resulting in relatively low research numbers. Most of the research has focused on the utilization of VDC coordinators that decide tire forces solely in proportion to corrective yaw moments. This approach leads to significant errors due to assumptions that do not account for the nonlinear characteristics of tires. In this research analysis, a coordinator considering nonlinear tire characteristics, such as the friction ellipse effect is presented. This crucial method of considering tire's nonlinear characteristics significantly enhances the accuracy of achieving the corrective yaw moment. Overall, the developed coordinator was validated through both simulations and experiments. [ABSTRACT FROM AUTHOR]

A combined steering and braking collision avoidance control method based on model predictive control is proposed in the present paper. This method can overcome the limitations of single steering or braking collision avoidance and improve vehicle safety and tracking performance in extremely complex conditions. The utilization rate of tire force, path tracking performance, and driving stability are considered in the design of the model predictive control (MPC) optimization objective. In addition, the front wheel angle and four-wheel braking torque are taken as the optimization variables. Simulation results showed that the proposed method can greatly improve the path tracking performance and driving stability of vehicles. [ABSTRACT FROM AUTHOR]

As a bridge between planners and actuators, trajectory tracking is a critical and essential part of robot navigation, autonomous driving and some other fields. While traditional trajectory tracking methods generally have disadvantages such as poor tracking accuracy, high modeling requirements, and heavy computational load. This paper proposes an end-to-end trajectory tracking method based on reinforcement learning, and an information encoding network and a reinforcement learning policy network are constructed. A multi-task dense reward function for trajectory tracking is designed. For efficient encoding of local trajectory information, a self-attentive mechanism is developed. A virtual simulation environment is constructed for model training by modeling the trajectory tracking task. Comparing with model predictive control and pure pursuit in the tracking experiments of several reference trajectories, the obtained results show that the proposed method has significant advantages in terms of lateral tracking. [ABSTRACT FROM AUTHOR]

Nonlinear Model Predictive Control (NMPC) is effective for local planning of automated vehicles, especially when there exist dynamical objects and multipe requirements. But it requires many computation resources for numerical optimization, which limits its practical application becase of the limited power of onboard unit. To extend the application range of the NMPC based local planner, the coupled nonlinear vehicle dynamics model is adopted based on the numerical analysis, which conversely requires much more discretization poits for acceptable accuracy. For better computation efficiency, Lagrange polynomials are used to discretize the vehicle dynamics model and objective function with less points and fine numerical accuracy. Furthermore, an adaptive strategy is designed to determine the order of Lagrange polynomials according to running state by numerical analysis of discretization error. Both acceleration effect and performance of the local planner designed by NMPC are validated by experimental tests under scenarios with multiple dynamical obstacles. The test results show that compared with the original one the accuracy and efficiency are improved by 74% and 60%, respectively. [ABSTRACT FROM AUTHOR]

Galaxy-cluster gravitational lenses enable the study of faint galaxies even at large lookback times, and, recently, time-delay constraints on the Hubble constant. There have been few tests, however, of lens model predictions adjacent to the critical curve (≲8″) where the magnification is greatest. In a companion paper, we use the GLAFIC lens model to constrain the Balmer L – σ relation for H ii regions in a galaxy at redshift z = 1.49 strongly lensed by the MACS J1149 galaxy cluster. Here we perform a detailed comparison between the predictions of 10 cluster lens models that employ multiple modeling assumptions with our measurements of 11 magnified, giant H ii regions. We find that that the models predict magnifications an average factor of 6.2 smaller, a ∼2 σ tension, than that inferred from the H ii regions under the assumption that they follow the low-redshift L – σ relation. To evaluate the possibility that the lens model magnifications are strongly biased, we next consider the flux ratios among knots in three images of Sp1149, and find that these are consistent with model predictions. Moreover, while the mass-sheet degeneracy could in principle account for a factor of ∼6 discrepancy in magnification, the value of H 0 inferred from SN Refsdal's time delay would become implausibly small. We conclude that the lens models are not likely to be highly biased, and that instead the H ii regions in Sp1149 are substantially more luminous than the low-redshift Balmer L – σ relation predicts. [ABSTRACT FROM AUTHOR]

• In this work, we suggested a computerized decision-making process for deciding on the best birthing style. • A deep neural network model (DNN) was developed by using train set with h2o package. The model was selected on the basis of AUC (Area Under the Curve) & KS (Kolmogorov-Smirnov) score. The AUC, KS score for train set were 0.99, 0.98 respectively. • The prediction error rates for caeseraen & vaginal classes in train data are 0.02 & 0.00 respectively. • The results support the use of these algorithms in the creation of a clinical decision system to help gynecologists choose the most appropriate delivery method. One of the factors that worry obstetricians the most is the method of delivery. In recent years, the rate of caesarean sections has steadily climbed and now exceeds the threshold advised by medical organizations. Obstetricians typically lack the tools they need to assess whether vaginal delivery or a caesarean delivery is more appropriate. In this work, we suggested a computerized decision-making process for deciding on the best birthing style. The data was collected from 101 pregnant subjects who were admitted to hospital in eastern India for delivery from January 2021 to September 2021.The data set had 101 instances & 11 variables. The response was a binary variable with "caesarean" & "vaginal" as the outputs. A deep neural network model (DNN) was developed by using train set with h2o package. The model was selected on the basis of AUC (Area under the Curve) & KS (Kolmogorov-Smirnov) score. The AUC, KS score for train set were 0.99, 0.98 respectively. The prediction error rates for caeseraen & vaginal classes in train data are 0.02 & 0.00 respectively. The results support the use of these algorithms in the creation of a clinical decision system to help gynaecologists choose the most appropriate delivery method. [ABSTRACT FROM AUTHOR]

• Create a prediction model based on anti-angiogenesis for patients with locally advanced cervical cancer. • The level of benefit of targeted therapy could be predicted before treatment, and the model validation was good. This study aimed to develop and evaluate an anti-angiogenesis-based model for predicting the survival and the potential benefits of targeted therapy for patients with localized advanced cervical cancer. We collected clinical data from 163 patients with cervical cancer who received paclitaxel and cisplatin (TP) or TP plus bevacizumab during or after radiotherapy from June 2017 to February 2023. We analyzed the clinical measures of recent efficacy and overall survival (OS) using univariate and logistic multivariate and Cox regression methods, respectively. We constructed a nomogram model and evaluated its efficacy using the c-index, the area under the curve (AUC), a calibration curve, and the clinical decision curve (DCA). We found that targeted agents and hemoglobin were independent determinants of near-term efficacy (P < 0.05), while targeted agents and stage were independent factors of OS (P < 0.05). We developed a predictive model for an OS prognostic nomogram and performed internal validation 1000 times using the Bootstrap re-sampling method. The c-index was 0.81, and the AUC was 0.84 (P < 0.01).The calibration curves showed a good agreement between the projected and actual values. The DCA curve indicated that the model had a high positive predictive accuracy. We developed a novel anti-angiogenesis-based survival prediction model for patients with locally advanced cervical cancer. This model could estimate the benefit of targeted therapy before treatment, and it had good validation. [ABSTRACT FROM AUTHOR]

Site-specific estimation of lime requirement requires high-resolution maps of soil organic carbon (SOC), clay and pH. These maps can be generated with digital soil mapping models fitted on covariates observed by proximal soil sensors. However, the quality of the derived maps depends on the applied methodology. We assessed the effects of (i) training sample size (5–100); (ii) sampling design (simple random sampling (SRS), conditioned Latin hypercube sampling (cLHS) and k-means sampling (KM)); and (iii) prediction model (multiple linear regression (MLR) and random forest (RF)) on the prediction performance for the above mentioned three soil properties. The case study is based on conditional geostatistical simulations using 250 soil samples from a 51 ha field in Eastern Germany. Lin's concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing training sample sizes, relative improvements of RMSE and CCC decreased exponentially. We found the lowest median RMSE values with 100 training observations i.e., 1.73%, 0.21% and 0.3 for clay, SOC and pH, respectively. However, already with a sample size of 10, models of moderate quality (CCC > 0.65) were obtained for all three soil properties. cLHS and KM performed significantly better than SRS. MLR showed lower median RMSE values than RF for SOC and pH for smaller sample sizes, but RF outperformed MLR if at least 25–30 or 75–100 soil samples were used for SOC or pH, respectively. For clay, the median RMSE was lower with RF, regardless of sample size. [ABSTRACT FROM AUTHOR]

The crop water stress index (CWSI) is widely used for assessing water status in vineyards, but its accuracy can be compromised by various factors. Despite its known limitations, the question remains whether it is inferior to the current practice of direct measurements of Ψstem of a few representative vines. This study aimed to address three key knowledge gaps: (1) determining whether Ψstem (measured in few vines) or CWSI (providing greater spatial representation) better represents vineyard water status; (2) identifying the optimal scale for using CWSI for precision irrigation; and (3) understanding the seasonal impact on the CWSI-Ψstem relationship and establishing a reliable Ψstem prediction model based on CWSI and meteorological parameters. The analysis, conducted at five spatial scales in a single vineyard from 2017 to 2020, demonstrated that the performance of the CWSI- Ψstem model improved with increasing scale and when meteorological variables were integrated. This integration helped mitigate apparent seasonal effects on the CWSI-Ψstem relationship. R2 were 0.36 and 0.57 at the vine and the vineyard scales, respectively. These values rose to 0.51 and 0.85, respectively, with the incorporation of meteorological variables. Additionally, a CWSI-based model, enhanced by meteorological variables, outperformed current water status monitoring at both vineyard (2.5 ha) and management cell (MC) scales (0.09 ha). Despite reduced accuracy at smaller scales, water status evaluation at the management cell scale produced significantly lower Ψstem errors compared to whole vineyard evaluation. This is anticipated to enable more effective irrigation decision-making for small-scale management zones in vineyards implementing precision irrigation. [ABSTRACT FROM AUTHOR]

Background: Pediatricians' offices are primary locations for pediatric influenza vaccination; however, pharmacists are also well-positioned as immunizers. Considering the current COVID-19 pandemic and Public Readiness and Emergency Preparedness (PREP) Act, pharmacists' authority to vaccinate children has been recently expanded. Methods: We used the de-identified Optum ClinformaticsTM Data Mart database to identify demographic and clinical predictors of pharmacist-administered pediatric influenza vaccination compared with influenza vaccination in pediatricians' offices. Procedures codes for influenza vaccinations among children were captured for the 2016-2017 influenza season. Logistic regression was used to identify significant predictors. Results: We included 336 841 children receiving influenza vaccines by a pharmacist (5.2%) or in pediatricians' offices (94.8%). The following significant predictors were identified: older pediatric age groups (13-17 years odds ratio [OR] 91.51, 5-12 years OR 35.41), states allowing pharmacist-administered influenza vaccination at younger ages (no age restrictions OR, 26.68, minimum age 2-4 years old OR, 33.76), influenza vaccination outside of pediatricians' offices in the previous year (pharmacist-administered OR, 22.18, convenience care OR 4.15, emergency care OR 1.69), geographic region (South OR, 2.02, Midwest OR 1.60, and West OR 1.38), and routine health exam or follow-up in the prior 6-months (OR, 1.59). Conclusions: The strongest drivers of pharmacist-administered pediatric influenza vaccination were older pediatric age, more lenient minimum age restrictions, and previous influenza vaccination in a pharmacy. Due to the COVID-19 pandemic, the PREP Act, and forthcoming pediatric COVID-19 vaccines for children, pharmacists may play a greater role in pediatric vaccination resulting in sustained changes in pediatric vaccination practices. [ABSTRACT FROM AUTHOR]

Objective: The Interpersonal Theory of Suicide (ITS) could help identify differences in groups of suicidal adolescents and inform treatment. Method: Latent Profile Analysis (LPA) using thwarted belongingness (TB), perceived burdensomeness (PB), hopelessness, and capability was conducted on data from an at‐risk clinical sample (N = 500). The ITS prediction that changes in TB and PB are associated with changes in suicidal ideation was tested using admission and discharge data. Results: Latent Profile Analysis identified three profiles with increasing complexity and severity on ITS factors. The profiles were labelled low‐severity (7.6% of participants), moderate‐severity (45.2%), and high‐severity (47.2%). ITS predictions were partially supported for the full sample and only for the high‐severity and moderate‐severity subgroups, whereby changes in TB were significantly associated with changes in suicidal ideation over the course of treatment. However, changes in PB were only significant in the moderate‐severity subgroup, and none of the ITS predictions were supported in the low‐severity subgroup. Additionally, effect sizes for changes in TB and PB were modest in all analyses. Conclusions: Our findings highlight the importance reducing low belongingness in youth, which is a component of all supported interventions of youth suicide prevention. However, given the modest association of changes in ITS variables had with changes in suicidal ideation, it may be fruitful to elaborate on the relative importance on types of low belongingness or include other non‐ITS variables. [ABSTRACT FROM AUTHOR]

In this work, a finite element cohesive fatigue model for concrete is proposed. A procedure to simulate monotonic rupture is included. Damage is calculated as a function of accumulated crack openings and the current traction on the cohesive zone. Only mode I fracture is considered. Concretes with different strengths and specimens with different sizes are considered. Size dependency was introduced in the model through the cohesive strength and the corresponding cohesive length. Other monotonic and cyclic properties are considered constant or dependent on the material strength. An excellent fit with experiments is obtained. Methodology is able to capture an embrittlement of the fatigue process with increase in size. In this case, crack (or process zone) becomes unstable for shorter sizes and a more abrupt change to unstable growth is observed. Predictive capabilities of the model are observed considering S‐N curves. A good match was observed in the low‐cycle fatigue range. Highlights: Size dependency is introduced in the model by the cohesive strengthExponent parameter used to estimate damage depends primarily on concrete strengthRelative endurance limit magnitude is related to the process zone sizeModel is able to capture a size embrittlement in the fatigue propagation [ABSTRACT FROM AUTHOR]

In this paper, the creep‐fatigue responses of the nickel‐based superalloy Inconel 718 have been investigated by conducting hybrid stress–strain‐controlled creep‐fatigue interaction (HCFI) loading experiments at 650°C with different dwell stresses and dwell periods. Deformation responses demonstrate that stress dwell leads to complex cyclic responses, such as an increase in softening rate and changes in inelastic strain rate. Then, a novel creep equivalent stress determination method is proposed based on stress relaxed characteristics. After modifying the continuum damage mechanics (CDM) model with this method, accurate prediction results of the creep‐fatigue cyclic life under strain‐controlled creep‐fatigue loading and HCFI loading can be obtained. Finally, the damage evolutions of Inconel 718 under HCFI loading are analyzed by CDM model. The results of damage evolution indicate that the trend of damage accumulation under different test conditions is consistent with the trend of inelastic strain in the material. Highlights: The creep‐fatigue responses of Inconel 718 under HCFI loading are investigated.A creep equivalent stress determination method based on stress relaxation is proposed.Accurate life prediction results obtained under various loadings at high temperature. [ABSTRACT FROM AUTHOR]

With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes exposes the body to a state of high blood sugar for a long time and causes varying degrees of hardening of the arteries, making it more prone to cardiovascular emergencies. Objective: The aim of the study was to explore the risk factors of early arterial stiffness in patients with type 2 diabetes mellitus (T2DM). Methods: A retrospective study was conducted on 316 T2DM patients without macroangiopathy in The First Hospital of Qinhuangdao. Early arterial stiffness was evaluated by brachial-ankle pulse wave velocity (baPWV). Results: Ninety patients (28.5%) had baPWV≥1800cm/s. baPWV showed positive correlation with systolic blood pressure (r=0.456, p<0.001), diastolic blood pressure (r=0.133, p=0.018), urine albumin-creatinine ratio (UACR) (r=0.232, p<0.001), neutrophil lymphocyte ratio (NLR) (r=0.185, p=0.001), and visceral fat area (r=0.139, p=0.014). In multiple linear regression analysis, systolic blood pressure (β=6.240, p<0.001), UACR (β=3.805, p=0.019), NLR (β=43.722, p=0.013), and visceral fat area (β=0.778, p=0.030) were significant independent predictors for baPWV. Conclusion: The decline of arterial elasticity was common in T2DM patients without macroangiopathy. Elevated blood pressure, microangiopathy, chronic inflammation, and visceral fat accumulation were the risk factors of early arterial stiffness in patients with T2DM. [ABSTRACT FROM AUTHOR]

One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile walls. The maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent structures. Nowadays, the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of calculation. This paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls, namely eXtreme gradient boosting (XGBoost), least square support vector regressor (LS-SVR), and random forest (RF), to predict maximum lateral displacement of pile walls. The results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669, the highest coefficient of determination 0.9991, and the lowest root mean square error 0.3544. Although the LS-SVR, and RF models were less accurate than the XGBoost model, they provided good prediction results of maximum lateral displacement of pile walls for numerical outcomes. Furthermore, a sensitivity analysis was performed to determine the most effective parameters in the XGBoost model. This analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction. [ABSTRACT FROM AUTHOR]

This paper presents a surrogate modeling approach for predicting ground surface settlement caused by synchronous grouting during shield tunneling process. The proposed method combines finite element simulations with machine learning algorithms and introduces an intelligent optimization algorithm to invert geological parameters and synchronous grouting variables, thereby predicting ground surface settlement without conducting numerous finite element analyses. Two surrogate models based on the random forest algorithm are established. The first is a parameter inversion surrogate model that combines an artificial fish swarm algorithm with random forest, taking into account the actual number and distribution of complex soil layers. The second model predicts surface settlement during synchronous grouting by employing actual cover-diameter ratio, inverted soil parameters, and grouting variables. To avoid changes to input parameters caused by the number of overlying soil layers, the dataset of this model is generated by the finite element model of the homogeneous soil layer. The surrogate modeling approach is validated by the case history of a large-diameter shield tunnel in Beijing, providing an alternative to numerical computation that can efficiently predict surface settlement with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Numerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given the paucity of monitoring data in field settings. This paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying Bayesian model averaging to several representative prediction models. By accounting for both model and parameter uncertainties, this approach enables more realistic predictions of ground deformations than individual models. Specifically, our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements, while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal deformations. Importantly, our analysis reveals that when monitoring data are sparse, model uncertainties may contribute up to 78.7% of the total uncertainties. Thus, obtaining sufficient data for parameter fitting is crucial for accurate predictions. The proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations. [ABSTRACT FROM AUTHOR]

The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, x, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for x have several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of x, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between x and other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of x with any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases. [ABSTRACT FROM AUTHOR]

This paper conducts a theoretical analysis of ground settlements due to shield tunneling in multi-layered soils which are usually encountered in urban areas. The proposed theoretical solution which is based on the general form of the Mindlin's solution and Loganathan-Poulos formula can comprehensively consider the in-process tunneling parameters including: unbalanced face pressure, shield-soil friction, unbalanced tail grouting pressure, unbalanced secondary grouting pressure, overloading during tunneling and the ground volume loss. The method is verified by comparing with the field data from the Qinghuayuan Tunnel Project in terms of the ground surface settlements along the longitudinal and transverse direction. Due to the local settlement or heave caused by the certain tunneling parameters, the ground surface settlements calculated using current solution along the longitudinal direction presents an irregular S-shaped curve instead of the traditional S-shaped curve. Results also find that the effect of the unbalanced secondary grouting pressure and the overloading during tunneling cannot be ignored. [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]

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]

The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinoma of the tongue cases prospectively. Intensity-modulated radiotherapy (IMRT) plans for 20 patients with carcinoma of the tongue were generated using the Eclipse treatment planning system. A machine learning model was generated using a Python 3.10 computer language in a Jupyter notebook using Anaconda software. The PTVs and OARs doses obtained from the clinical treatment plans were used as a primary dataset. Machine learning models are built with two different datasets (10 and 20) for each selected volume. Volumes from 10 new sets of patients were fed into the software for predicting the corresponding dose values. Through the input given, the plan generated dose values of 10 patients were compared with the predicted outcomes of the 10 and 20 dataset models. The model created using the PTVs volume data predicted the dose values with increased accuracy. By verifying the model prediction with the TPS generated value, both the 10 and 20 dataset models predict all the 10 PTVs data within an error bound of 3% and most of the OARs data within an error bound of 5%. The dosimetric features implemented in the machine learning models reasonably predict both the PTVs dose parameter and OARs constraints and give confidence in decision-making during the clinical planning process. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to examine the associations of 24-h movement behaviors (moderate to vigorous physical activity [MVPA], light physical activity [LPA], sedentary behavior [SB], and sleep) with age-, sex-, and race-specific a real bone mineral density (aBMD) z-score parameters at clinical sites in young pediatric cancer survivors. Methods: This cross-sectional multicenter study was carried out within the iBoneFIT framework in which 116 young pediatric cancer survivors (12.1 ± 3.3 yr old; 42%female) were recruited. We obtained anthropometric and body composition data (i.e., body mass, stature, body mass index, and region-specific lean mass), time spent in movement behaviors over at least seven consecutive 24-h periods (wGT3x-BT accelerometer, ActiGraph), and aBMD z-score parameters (age-, sex-, and race-specific total at the body, total hip, femoral neck and lumbar spine). Survivors were classified according to somatic maturity (pre or peri/postpubertal depending on the estimated years from peak height velocity). The adjusted models' coefficients were used to predict the effect of reallocating time proportionally across behaviors on the outcomes. Results: In prepubertal young pediatric cancer survivors, reallocating time to MVPA from LPA, SB, and sleep was significantly associated with higher aBMD at total body (B = 1.765, P = 0.005), total hip (B = 1.709, P = 0.003), and lumbar spine (B = 2.093, P = 0.001). In peri/postpubertal survivors, reallocating time to LPA from MVPA, SB, and sleep was significantly associated with higher aBMD at all sites (B = 2.090 to 2.609, P = 0.003 to 0.038). Reallocating time to SB from MVPA or LPA was significantly associated with lower aBMD atmost sites in prepubertal and peri/postpubertal survivors, respectively. Finally, reallocating time to sleep from MVPA, LPA, and SB was significantly associated with lower aBMD at total body (B = -2.572, P = 0.036) and total hip (B = -3.371, P = 0.015). Conclusions: These findings suggest that every move counts and underline the benefits of increasing MVPA or LPA, when low MVPA levels are present, for bone regeneration after pediatric cancer treatment completion. [ABSTRACT FROM AUTHOR]

Objective: To construct a simple term small‐for‐gestational‐age (SGA) neonate prediction model that is clinically practical. Methods: This analysis was based on the Born in Guangzhou Cohort Study (BIGCS). Mothers who had a singleton pregnancy, delivered a term neonate, and had an ultrasonography within 30 + 0 to 32 + 6 weeks of gestation were included. Term SGA was defined with customized population percentiles. Prediction models were constructed with backward selection logistic regression in a four‐step approach, where model 1 contained fetal biometrics only, models 2 and 3 included maternal features and a time factor (weeks between ultrasonography and delivery), respectively; and model 4 contained all features mentioned. The prediction performance of individual models was evaluated based on area under the curve (AUC) and a calibration test was performed. Results: The prevalence of SGA in the study population of 21 346 women was 11.5%. With a complete‐case analysis approach, data of 19 954 women were used for model construction and validation. The AUC of the four models were 0.781, 0.793, 0.823, and 0.834, respectively, and all were well‐calibrated. Model 3 consisted of fetal biometrics and corrected for time to delivery was chosen as the final model to build risk prediction graphs for clinical use. Conclusion: A prediction model derived from fetal biometrics in early third trimester is satisfactory to predict SGA. Synopsis: A simple SGA prediction model based on fetal biometrics that requires no additional tests and no prior knowledge of the patient is clinically useful. [ABSTRACT FROM AUTHOR]

Objective: HELLP syndrome is a severe complication of hypertensive disorders of pregnancy that can cause multiple organ dysfunction and maternal death in a short period of time. Although HELLP syndrome is more common in patients with pre‐eclampsia (PE), there is currently no effective way to identify high‐risk individuals who may progress from gestational hypertension (GH) to PE complicated with HELLP syndrome. This study aimed to establish and validate a prediction model for PE complicated with HELLP syndrome, providing a basis for early detection and identification of high‐risk individuals in clinical practice. Methods: This retrospective case–control study collected data on 326 patients with GH and 139 patients with PE complicated with HELLP syndrome from January 2015 to December 2019. An additional 206 patients with GH and 70 patients with PE complicated with HELLP syndrome who were treated from January 2020 to December 2022 were collected for external validation. General and clinical data were collected, and single‐and multiple‐factor logistic regression analyses were used to screen for independent factors affecting PE complicated with HELLP syndrome. The diagnostic performance of different indicators was evaluated using ROC curves. A prediction model for PE complicated with HELLP syndrome was constructed, and its efficacy was verified using ROC curves. Results: The results of single‐factor analysis showed that age, SBP, DBP, MAP, hemoglobin, AST, ALT, cholinesterase, alkaline phosphatase, gamma‐glutamyl transferase, total protein, total bilirubin, direct bilirubin, indirect bilirubin, BUN, UA, creatinine, APTT, international normalized ratio of prothrombin, D‐dimer, fibrinogen, fibrinogen degradation products, Ca, and aspartate‐aminotransferase to platelet ratio index (APRI) were factors influencing PE with HELLP syndrome. The results of multiple‐factor logistic regression analysis showed that MAP, APRI, CHE, FDP, and Ca were independent factors affecting PE complicated with HELLP syndrome. Based on these results, a prediction model was established, with Y = 9.861 + 2.998APRI + 0.055MAP + 0.014FDP − 0.005CHE − 7.452*Ca. Conclusions: The predictive model for PE complicated with HELLP syndrome includes APRI, MAP, FDP, CHE, and Ca. This model can be used as a quantitative tool for predicting and evaluating the development of GH into PE complicated with HELLP syndrome. Synopsis: The predictive model of HELLP syndrome in early‐onset pre‐eclampsia was constructed, and the factors included MAP, PT‐INR, CHE, FDP and APRI. [ABSTRACT FROM AUTHOR]

Variable selection involves selecting truly important predictors from p-dimensional multivariate functional predictors in functional predictive models. In this paper, a variable selection method is designed for scalar-on-function predictions entangled with nonlinear joint associations among scalar response and multiple functional predictors. First, a nonparametric functional nonlinear conditional correlation coefficient, namely, the FunNCC coefficient, is proposed to measure complex dependencies, including the nonmonotonic marginal dependence, along with the conditional associations of redundancy, complement, and interaction. Then, a model-free feature ordering and selection method is designed, where the FunNCC is utilized to rank relevance, enabling the selection of a subset of predictors with the strongest joint dependence. Since this method allows for quantitatively evaluating the contributions of predictors in explaining responses, it achieves moderate model interpretability. Finally, extensive simulation studies and two real-data cases involving air pollution regression and hand gesture recognition are conducted to evaluate the finite sample performance of the proposed method, and the results show that the proposed FunNCC and variable selection methods outperform state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

This study aimed to develop a model that specifies the predictive effects of factors on death anxiety among Chinese patients with cancer using structural equation modeling. Using convenience sampling, data were collected from 353 cancer patients. Self-administered questionnaires included Social Support Rating Scale, Rosenberg Self-Esteem Scale, Connor Davidson Resilience Scale, Templer's Death Anxiety Scale, and socio-demographic factors. The results showed that social support, self-esteem, and resilience significantly impacted death anxiety. The final model fitted the data acceptably (χ2 = 37.319, df =31, p = 0.201). Social support mediated death anxiety through self-esteem and resilience. Resilience mediated the buffer effect of self-esteem on death anxiety as an intermediary factor. Findings suggest the need for further studies to explore effective interventions to provide social support and improve self-esteem and resilience among patients with cancer to alleviate death anxiety. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to compare the radiological tumor (T)-category using multiparametric MRI with the pathological T category in patients with oral tongue squamous cell carcinoma (OTSCC) and to examine which is a better predictor of prognosis. Methods: This retrospective study included 110 consecutive patients with surgically resected primary OTSCC who underwent preoperative contrast-enhanced MRI. T categories determined by maximum diameter and depth of invasion were retrospectively assessed based on the pathological specimen and multiparametric MRI. The MRI assessment included the axial and coronal T1-weighted image (T1WI), axial T2-weighted image (T2WI), coronal fat-suppressed T2WI, and axial and coronal fat-suppressed contrast-enhanced T1WI (CET1WI). Axial and coronal CET1WI measurements were divided into two groups: measurements excluding peritumoral enhancement (MEP) and measurements including peritumoral enhancement. The prognostic values for recurrence and disease-specific survival after radiological and pathological T categorization of cases into T1/T2 and T3/T4 groups were compared. Results: The T category of MEP on coronal CET1WI was the most relevant prognostic factor for recurrence [hazard ratio (HR) = 3.30, p = 0.001] and the HR was higher than the HR for pathological assessment (HR = 2.26, p = 0.026). The T category determined by MEP on coronal CET1WI was also the most relevant prognostic factor for disease-specific survival (HR = 3.12, p = 0.03), and the HR was higher than the HR for pathological assessment (HR = 2.02, p = 0.20). Conclusion: The T category determined by MEP on the coronal CET1WI was the best prognostic factor among all radiological and pathological T category measurements. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. Methods: Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. Results: Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. Conclusion: Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT. [ABSTRACT FROM AUTHOR]

Background: This study aims to analyze the influencing factors of postoperative Low Anterior Resection Syndrome (LARS) in patients with middle and low rectal cancer who underwent robotic surgery. It also seeks to predict the probability of LARS through a visual, quantitative, and graphical nomogram. This approach is expected to lower the risk of postoperative LARS in these patients and improve their quality of life through effective prevention and early intervention. Patients and methods: This research involved patients with middle and low rectal cancer who underwent robotic surgery in the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Nanchang University from January 2015 to October 2022. A series of intestinal dysfunction symptoms arising from postoperative rectal cancer were diagnosed and graded using LARS scoring criteria. After the initial screening of all variables related to LARS with Lasso regression, they were included in logistic regression for further univariate and multivariate analysis to identify independent risk factors for LARS. A prediction model was then constructed. Results: The study included 358 patients. The parameters identified by Lasso regression included obstruction, BMI, tumor localization, maximum tumor diameter, AJCC stage, stoma, neoadjuvant therapy (NAT), and postoperative adjuvant therapy (AT). Univariate and multivariate analyses indicated that a higher BMI, lower tumor localization, higher AJCC stage, neoadjuvant therapy, and postoperative adjuvant therapy were independent risk factors for total LARS. The AUC of the prediction nomogram was 0.834, with a sensitivity of 0.825 and specificity of 0.741. The calibration curve demonstrated excellent concordance with the nomogram, indicating the prediction curve fit the diagonal well. Conclusion: Higher BMI, lower tumor localization, higher AJCC stage, neoadjuvant therapy, and adjuvant therapy were identified as independent risk factors for total LARS. A new predictive nomogram for postoperative LARS in patients with middle and low rectal cancer undergoing robotic surgery was developed, proving to be stable and reliable. This tool will assist clinicians in managing the postoperative treatment of these patients, facilitating better clinical decision-making and maximizing patient benefits. [ABSTRACT FROM AUTHOR]

This cross-sectional study included 211 employed family caregivers with older relatives living in care facilities in Japan. Using multiple linear regression analysis, we examined the caregiving context after institutionalization of an older family member. Specifically, we examined predictors of negative spillover from caregiving to employment among family caregivers. The outcome was the extent of negative spillover. Primary predictors included caregiver characteristics and postinstitutionalization caregiving contexts such as caregiving tasks and dissatisfaction with institutional care services. Among all caregivers, 134 (63.5%) were female, and approximately half of all caregivers reported satisfaction with institutional care services. We found that dissatisfaction with institutional care services and being a female each had a main effect on greater negative spillover. However, they did not have any interacting effect on negative spillover after the institutionalization. Negative spillover did not terminate when older family members were institutionalized. Higher satisfaction with institutional care may reduce negative spillover. [ABSTRACT FROM AUTHOR]

According to various human needs, such as mineral supply, transportation, and underground storage, tunnel excavation has become commonplace today. However, one of the undesirable phenomena encountered during tunnel excavating, particularly when employing drilling and blasting techniques, is the overbreak phenomenon (OBP). This phenomenon not only compromises the safety of the work environment but also leads to increased operating costs. Therefore, accurately predicting the occurrence of OBP in tunnel excavation is of utmost importance. Due to the numerous uncertainties and complexities associated with rock formations, traditional methods have limited accuracy in predicting OBP. Consequently, this research used the rock engineering system (RES) method to comprehensively understand the physical and mechanical characteristics of the rock mass at any given point. By employing the RES method, accurate modeling of the OBP can be achieved in a time- and cost-efficient manner without requiring extensive and complicated coding. To construct the model using the RES method, 217 data points from three case studies were utilized: the Tarzeh underground coal mine in Iran, the Azad tunnel on the Tehran-North road in Alborz, Iran, and an Indian mine. The parameters considered in this study that affect the OBP (%) included perimeter hole powder factor (PPF, kg/m3), spacing-to-burden ratio of contour holes (S/B, kg), tunnel section area (A, m2), specific drilling (SD, m/m3), and rock mass rating (RMR, %). To compare and evaluate the RES method, various regression methods were also employed. Statistical criteria revealed that the RES method (RMSE = 0.0046, MSE = 0.068, R2 = 0.945) demonstrated high accuracy compared to other regression methods. Consequently, it can be concluded that the RES is a highly valuable and essential tool for rock engineers. [ABSTRACT FROM AUTHOR]

Active and semi-active suspensions for passenger cars traditionally enhance comfort through body control, and vehicle handling by reducing the tyre load variations induced by road irregularities. Active suspensions can also be designed to track a desired yaw rate profile through the control of the lateral load transfer distribution between the front and rear axles. This paper considers an integrated system including semi-active and active suspension actuation to control the yaw, roll, pitch and heave dynamics excited by the driving actions. To this purpose, two novel real-time-capable implicit nonlinear model predictive control (NMPC) formulations, excluding and including cost function weight adaptation, are proposed and compared with the passive vehicle, and the controlled vehicle with two combinations of skyhook and active roll control, the first based on a pseudoinverse decoupling transformation for obtaining the damping force contributions, and the second using an inverse formulation. The algorithms are assessed through an experimentally validated simulation model, along manoeuvres corresponding to sub-limit and limit handling operation, to analyse the trade-off between body motion reduction and cornering response enhancement. The results show that the adaptable NMPC configuration provides the best performance in all scenarios, also for significant variations of the main vehicle and tyre parameters. [ABSTRACT FROM AUTHOR]

To investigate the correlationf between residual drift and remaining seismic capacity, 24 reinforced concrete pier specimens were subjected to lateral load reversals. By normalizing the specified drift against the value at specimen failure, the normalized residual drift exhibits a strong correlation with the normalized lateral drift, thereby providing a predictive model for residual drift. After determining the remaining seismic capacity derived from energy dissipation, a linear correlation was established between the normalized residual drift and the remaining seismic capacity. Consequently, normalized residual drift was selected as a performance indicator for seismic assessment, enabling the quantitative determination of structural damage states. [ABSTRACT FROM AUTHOR]

This study presents a predictive geometallurgical model for the Ouansimi copper deposit in Morocco, focusing on the flotation of mixed copper ores. The deposit was divided into two geometallurgical domains (GMD) with distinct oxidation rates using K-means clustering. The principal component analysis-partial least squares regression method was applied to establish the relationship between mineralogical features, chemical assay, location factors, and flotation performance, enabling the identification of critical factors influencing flotation responses and offering opportunities for improvement. The developed models for copper and silver in GMD (I) outperformed those in GMD (II). Specifically, the negative impact of copper oxide-bearing minerals' response to flotation xanthate collectors and refractory copper minerals in the gangue on copper flotation performance was identified. Additionally, the performance of silver, likely occurring as an inclusion within chalcocite, was influenced by the interlocking and association between copper-bearing minerals and dolomite. While valuable insights were gained, knowledge gaps related to the presence of copper oxide minerals in the hypogene stage and the genesis of the Ouansimi deposit were identified. Further investigation is warranted to comprehend better the mode of occurrence and potential upgrading of precious metals in low-grade by-products in the final concentrate. In conclusion, our integrated approach, incorporating geological, mineralogical, and metallurgical data, holds promise for enhancing efficiency and profitability in the mining industry through optimized flotation performance in mixed copper ores. The findings contribute to a deeper understanding of mineral processing challenges and offer valuable insights for future exploration and ore processing strategies. [ABSTRACT FROM AUTHOR]

The determination of the appropriate initial dose for tacrolimus is crucial in achieving the target concentration promptly and avoiding adverse effects and poor prognosis. However, the trial‐and‐error approach is still common practice. This study aimed to establish a prediction model for an initial dosing algorithm of tacrolimus in patients receiving a lung transplant. A total of 210 lung transplant recipients were enrolled, and 26 single nucleotide polymorphisms (SNP) from 18 genes that could potentially affect tacrolimus pharmacokinetics were genotyped. Associations between SNPs and tacrolimus concentration/dose ratio were analyzed. SNPs that remained significant in pharmacogenomic analysis were further combined with clinical factors to construct a prediction model for tacrolimus initial dose. The dose needed to reach steady state tacrolimus concentrations and achieve the target range was used to validate model prediction efficiency. Our final model consisted of 7 predictors—CYP3A5 rs776746, SLCO1B3 rs4149117, SLC2A2 rs1499821, NFATc4 rs1955915, alanine aminotransferase, direct bilirubin, and hematocrit—and explained 41.4% variance in the tacrolimus concentration/dose ratio. It achieved an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.746‐0.861). The Hosmer‐Lemeshow test yielded a nonsignificant P value of.790, suggesting good fit of the model. The predicted dose exhibited good correlation with the observed dose in the early postoperative period (r = 0.748, P less than.001). Our study provided a genotype‐guided prediction model for tacrolimus initial dose, which may help to guide individualized dosing of tacrolimus in the lung transplant population in clinical practice. [ABSTRACT FROM AUTHOR]

In recent years, extreme focus on renewable energy has intensified due to environmental concerns and the depletion of fossil fuel supplies. In a DC microgrid that includes AC grid, photovoltaic (PV), wind, and battery storage systems, there are some problems such as intermittency and variability, mismatched generation and demand, inefficient energy utilization by storage batteries, and unstable DC link voltage. To overcome these problems, an integrated strategy for better energy management and a new MPPT technique is proposed in this research. The robust model predictive control (RMPC) method is proposed to deal with uncertainties and disturbances while offering the best possible control options. A comparison of the two algorithms reveals that the RMPC performs better than the conventional model predictive control (MPC) method. To harvest the maximum solar electricity from the PV system, a sophisticated MPPT optimization technique called muddled tuna swarm optimization (MTSO) is applied. Drone squadron optimization (DSO) and slime mould optimization (SMO) are outperformed by MTSO in terms of dynamic performance, effectively tracking the maximum power point (MPP) of the PV system even under various irradiance conditions. The suggested RMPC approach and MTSO technique are effective in achieving optimal energy and battery management as well as maximum solar power extraction, according to the simulation findings. On the OPAL-RT platform, real-time simulation is used to test the control strategy. [ABSTRACT FROM AUTHOR]

In recent years, Model Predictive Torque Control (MPTC) has gained popularity as a powerful method of controlling Induction Motors (IMs). There are, however, some factors such as the weighted coefficient that must be taken into account to attain satisfactory performance at different operation points for stator flux. As of now, an empirical procedure has been used for tuning the weighting factor in MPTC, which is not as effective. Model Predictive Flux Control (MPFC) is proposed in this paper, which eliminates complicated process of predicting the stator current by using the flux vectors as control variables. In order to get over these obstacles, a novel Model Predictive Control (MPC) method utilizing a Cascaded Artificial Neural Network (ANN) is proposed in this research. As a result, the control complexity is diminished and weighting factor in standard MPTC is eliminated. In-depth evaluations and comparisons of MPTC and MPFC are conducted, covering low-speed function, dynamic response, and steady-state performance. In contract to traditional approaches, the preferred method has much lower computational cost, virtuous dynamic response, and superior steady-state performance. The overall proposed work is simulated using MATLAB/SIMULINK platform and experimental tests performed on 3Φ VSI fed IM drives are used to verify the efficacy of proposed approach. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the switching frequency is not fixed and the number of samples is large when using the traditional finite control set-model predictive control (FCS-MPC) method for a five-level inverter with coupled inductors, an improved model prediction method is proposed. This improved method combines the space vector pulse width modulation and the minimum current error optimization principle to design the modulation strategy, and combines the modulation function with the evaluation function to obtain a modulation function that satisfies the minimum current error. Compared with the traditional finite set model prediction method, the improved method can realize the balance of the coupled inductor current without measuring the coupled inductor current, can fix the switching frequency of power switches, and retains the rapidity of the traditional model predictive control. The effectiveness of the proposed method is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]