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Forecasting the flow of rivers is an essential task in water management resources. However, the flow changes significantly at the seasonal variation of the year, so it is difficult to predict the flow because of its complex nature. The study aims to find a predictive model for forecasting the inflow of the Indus River. Two multivariate models, the autoregressive distributed lag (ARDL) model and vector error correction model (VECM), are compared to examine whether there is any long‐ or short‐run association between variables. It is observed that the ARDL model shows long‐ and short‐run associations between inflow, outflow, and levels. In contrast, VECM shows only a long‐run association between the variables. It is concluded that the ARDL model performs better in predicting the future inflow of the Indus River than VECM based on the stability test or accuracy of the models. [ABSTRACT FROM AUTHOR]

Objective. The number of cancer survivors is increasing, and the high prevalence of frailty not only reduces quality of life but also affects the treatment of cancer patients. This study aimed to identify the prevalence and risk factors of frailty in cancer patients and to construct a nomogram to predict the probability of frailty. Methods. Nine hundred fifty‐eight cancer patients were included in this retrospective study, randomly divided into a development set (n = 680) and a validation set (n = 278). Frailty was assessed using the Tilburg frailty indicator (TFI). Social support, medical coping styles, and psychological distress were assessed by the Social Support Self‐Rating Scale (SSRS), Medical Coping Modes Questionnaire (MCMQ), and distress thermometer (DT), respectively. Results. The prevalence of frailty in cancer patients was 45.93%. Cancer patients who exercised regularly, ate a balanced diet, and actively coped with diseases were less likely to become frail. The risk factors for frailty identified by a multivariate analysis were parenteral nutrition, advanced TNM staging, vegetarian diet, unemployment, psychological distress ≥4, low physical activity, and negative coping styles. These risk factors were used to construct a nomogram, and the C‐index, calibration curve, and decision curve analysis (DCA) were used to assess the performance of the nomogram. The C‐index was 0.762, and the calibration curve showed satisfactory coherence. The net benefit of the nomogram was better between threshold probabilities of 17%–96% in DCA. Conclusion. Special focus needs to be placed on frail cancer patients due to their high prevalence and severe outcomes. Clinical medical workers could use this nomogram to identify high‐risk patients and intervene early to prevent frailty. [ABSTRACT FROM AUTHOR]

This study proposes a cooperative lane‐changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane‐changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane‐Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle's longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane‐changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle's lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014 m, 0.5631 m, and −0.7601 m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability. [ABSTRACT FROM AUTHOR]

The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis. [ABSTRACT FROM AUTHOR]

Background. This study aims to investigate the agreement between the NK and KS formulas in predicting the vault after implantation of an EVO‐implantable collamer lens (ICL). Methods. This retrospective study included 106 eyes of 57 patients who underwent ICL‐V4c implantation. Preoperative vault prediction was conducted by utilizing the NK and KS formulas, with postoperative measurements by anterior segment optical coherence tomography (AS‐OCT) at one month. The analysis focused on the consistency between predicted and achieved vaults, as well as the correlation between the achieved vault and various biometric parameters. Results. The mean achieved vault was 605.25 ± 212.72 µm, which was significantly smaller than the predicted vaults of 710.08 ± 195.08 and 673.80 ± 212.76 µm, using the NK and KS formulas, respectively (P < 0.05). The mean differences between the achieved vault and the predicted vault using the NK formula and KS formula were −104.82 μm (95% LoA: −600.38–391.19 μm) and −68.55 μm (95% LoA: −628.91–491.82 μm), respectively. Anterior chamber depth (ACD), vertical sulcus‐to‐sulcus (V‐STS) diameter, and crystalline lens rise (CLR) were independent factors associated with the achieved vault (P < 0.05). The two formulas showed no statistically significant difference in absolute prediction error (APE). Conclusion. The NK formula exhibited superior consistency and low predictive error compared to the KS formula in the 12.6 mm ICL group. AS‐OCT measurements overestimated the predicted ICL vault, especially in the 13.2 mm ICL size selection. Relying solely on the NK or KS formulas for predicting vaults before ICL surgery is not recommended. [ABSTRACT FROM AUTHOR]

Grey systems theory can be used to predict the evolution of a system with insufficient information. Unfortunately, the most used version of the grey model (GM), namely, GM(1,1), works best when the system series have an increasing exponential rate. In any other case, the GM(1,1) produces inaccurate predictions. In this paper, we examine the mathematical formulation of the conventional GM(1,1) in order to propose a new GM that addresses its shortcomings through a new time response function. Examples are presented to demonstrate the flexibility and accuracy of the new model when implemented with series of various natures. Comparisons with other intelligent GM(1,1) show that the proposed model performs better than the reference models. [ABSTRACT FROM AUTHOR]

In this study, classic thin‐layer drying models such as the Page model, Verma model, and Two‐term model were used to fit the thin‐layer drying data of feed. Based on this, a novel thin‐layer drying model for pelleted feed (modified Verma model) was proposed. The results indicate that the new model is the optimal choice for describing the drying kinetics of pelleted feed for growing pig and calf. The drying rate of pelleted feed is linearly correlated with the feed moisture content. The effective water diffusion coefficient for pelleted feed of growing pig and calf ranges from 1.86 × 10−10 to 2.75 × 10−9 m2/s and 3.32 × 10−10 to 3.87 × 10−9 m2/s, respectively. The activation energy (Ea) for moisture diffusion in both types of feed is 26.42 kJ/mol and 25.86 kJ/mol, respectively. By establishing predictive models for model parameters based on air temperature and air velocity variables, the new model demonstrates extrapolation capability, wide applicability, and practical value. This work lays the foundation for developing a similarly characterized deep‐bed cooling drying model for pelleted feed. [ABSTRACT FROM AUTHOR]

This study proposes a data‐driven method for predicting the probabilistic response of cable‐stayed bridges. The proposed method is used to construct an optimal prediction model based on a data‐driven machine‐learning method. In addition, the accuracy and efficiency of the prediction model are improved by considering the correlation coefficients between the input sensor data and the output of the target response. The proposed method is comprised of two steps: the selection of meaningful features and the construction of a probabilistic prediction model employing Gaussian process regression. The proposed method is applied to an in‐service cable‐stayed bridge in the Republic of Korea using actual measurement data from various sensors. For comparison purposes, two parametric studies are performed, and the effects of the proposed feature‐selection procedure are investigated based on the normalized correlation coefficients. Consequently, the proposed feature‐selection method is proven to increase the accuracy and efficiency of the prediction. [ABSTRACT FROM AUTHOR]

HCM is a heterogeneous monogenic cardiac disease that can lead to arrhythmia, heart failure, and atrial fibrillation. This study aims to identify biomarkers that have a positive impact on the treatment, diagnosis, and prediction of HCM through bioinformatics analysis. We selected the GSE36961 and GSE180313 datasets from the Gene Expression Omnibus (GEO) database for differential analysis. GSE36961 generated 6 modules through weighted gene co‐expression network analysis (WGCNA), with the green and grey modules showing the highest positive correlation with HCM (green module: cor = 0.88, p = 2e − 48; grey module: cor = 0.78, p = 4e − 31). GSE180313 generated 17 modules through WGCNA, with the turquoise module exhibiting the highest positive correlation with HCM (turquoise module: cor = 0.92, p = 6e − 09). We conducted GO and KEGG pathway analysis on the intersection genes of the selected modules from GSE36961 and GSE180313 and intersected their GO enriched pathways with the GO enriched pathways of endothelial cell subtypes calculated after clustering single‐cell data GSE181764, resulting in 383 genes on the enriched pathways. Subsequently, we used LASSO prediction on these 383 genes and identified RTN4, COL4A1, and IER3 as key genes involved in the occurrence and development of HCM. The expression levels of these genes were validated in the GSE68316 and GSE32453 datasets. In conclusion, RTN4, COL4A1, and IER3 are potential biomarkers of HCM, and protein degradation, mechanical stress, and hypoxia may be associated with the occurrence and development of HCM. [ABSTRACT FROM AUTHOR]

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short‐duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root‐mean‐square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system. [ABSTRACT FROM AUTHOR]

Aim. To explore the status of job crafting, leisure crafting, and burnout among nurses and to examine the impact of job crafting and leisure crafting variations on burnout using machine learning‐based models. Background. The prevalence of burnout among nurses poses a severe risk to their job performance, quality of healthcare, and the cohesiveness of nurse teams. Numerous studies have explored factors influencing nurse burnout; however, few involved job crafting and leisure crafting synchronously and elucidated the effect differences of the two crafting behaviors on nurse burnout. Methods. Multicentre cross‐sectional survey study. Nurses (n = 1235) from four Chinese tertiary hospitals were included. The Maslach Burnout Inventory‐General Survey, the Job Crafting Scale, and the Leisure Crafting Scale were employed for data collection. Four machine learning algorithms (logistic regression model, support vector machine, random forest, and gradient boosting tree) were used to analyze the data. Results. Nurses experienced mild to moderate levels of burnout and moderate to high levels of job crafting and leisure crafting. The AUC (in full) for the four models was from 0.809 to 0.821, among which the gradient boosting tree performed best, with 0.821 AUC, 0.739 accuracy, 0.470 sensitivity, 0.919 specificity, and 0.161 Brier. All models showed that job crafting was the most important predictor for burnout, while leisure crafting was identified as the second important predictor for burnout in the random forest model and gradient boosting tree model. Conclusion. Even if nurses experienced mild to moderate burnout, nurse managers should develop efficient interventions to reduce nurse burnout. Job crafting and leisure crafting may be beneficial preventative strategies against burnout among nurses at present. Implications for Nursing Management. Job and leisure crafting were identified as effective methods to reduce nurse burnout. Nurse managers should provide more opportunities for nurses' job crafting and encourage nurses crafting at their leisure time. [ABSTRACT FROM AUTHOR]

Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non‐eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non‐eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non‐eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single‐channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning‐based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP. [ABSTRACT FROM AUTHOR]

Patients with Parkinson's disease (PD) experience significantly reduced quality of life when PD is complicated with Pisa syndrome (PS). PS is a postural abnormality associated with a lateral bending of the trunk, causing the patient to lean to one side. Microsaccades during fixation are transmitted to the visual cortex, and this gaze movement may be impaired in PD. We aimed to detect presymptomatic signs of PS. We enrolled 50 patients with PD without dementia and investigated the visual systems in patients with concurrent PD and PS based on a Romberg ratio of<1.0. Gaze analysis, pupil diameter, stabilization tests, neuropsychological tests, and cerebral perfusion scintigraphy were reviewed and statistically analyzed. Two years later, we divided the patients into three groups as follows: PISA++ (patients who had PS at enrollment), PISA‐+ (patients without PS that developed PS during the 2‐year period), and PISA‐‐ (patients without PS that did not develop PS during the 2‐year period). The PISA‐+ group exhibited a significantly higher daily levodopa dose and longer fixations, as well as lower position discrimination, Wechsler Adult Intelligence Scale‐Third Edition blocking, and blood flow in the left supramarginal and orbital gyri than that in the PISA‐‐ group. The PISA++ group showed a significantly longer fixation time and lower Mini‐Mental State Examination score, Romberg ratio of area, amplitude, velocity of microsaccades, and blood flow in the left precuneus and cuneus than that in the PISA‐+ group. Before the onset of PS, hypoperfusion occurred in the correlative visual cortex and the position discrimination test. Patients with PS have reduced saccades and slow microsaccades. [ABSTRACT FROM AUTHOR]

Background. Although retaining new nurses is imperative for the future of the nursing profession, it remains a challenging task in the healthcare industry. Understanding the career journey of new graduates as they transition from students to nurses is vital. However, longitudinal studies investigating the factors influencing retention during this period are lacking. Aim. The aim of this study is to identify the influencing factors and develop a longitudinal prediction model for new graduate nurse retention. Methods. A secondary data analysis was conducted using the New Nurse e-Cohort Study dataset from two survey periods, November–December 2020 and February–March 2022. The participants were categorized into either retention or turnover groups based on their turnover experiences. A decision tree based on classification and regression tree (CART) analysis was utilized. Results. Of the total 586 participants, 463 (79%) were in the retention group. The CART model highlighted that new nurses' retention was significantly associated with younger age, higher readiness for practice (clinical problem-solving) during the nursing program, lower transition shock (such as confusion in professional values, loss of social support, and conflicts between theory and practice), and a higher person-environment fit (person-job fit). The predictive accuracy of the CART model was 79.7%. Conclusion. To retain new nurses, nursing educators and hospital managers should collaborate to prepare nursing students for actual practice, offer support during organizational socialization, and foster healthy professional values for competence in the workplace. Implications for Nursing Management. Transforming the educational strategies of nursing programs and hospital management policies is imperative to ultimately enhance the retention of new graduate nurses. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [ABSTRACT FROM AUTHOR]

Background. Children and adolescents with type 1 diabetes mellitus (T1DM) are frequently hospitalised for severe hypoglycaemia, hyperglycaemia, and diabetic ketoacidosis (DKA). While several risk factors have been recognised, clinically identifying these children at high risk of acute decompensation remains challenging. Objective. To develop a risk prediction model to accurately estimate the risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA in children and adolescents with T1DM. Materials and Methods. Using a retrospective dataset, baseline demographic and clinical data were collected from patients (<18 years) seen at a regional paediatric diabetes clinic from 1 January 2018 to 1 January 2020. The outcome was the number of emergency department presentations or hospital admissions for severe hypoglycaemia, hyperglycaemia, and DKA across the study period. Variables that were significant in univariate analysis were entered into a multivariable model. Receiver operator characteristic (ROC) curves assessed the model's discrimination and generated cut-offs for risk group stratification (low, medium, and high). Kaplan–Meier survival analysis measured time to acute healthcare utilisation across the risk groups. Results. Our multivariable risk prediction model consisted of five predictors (continuous glucose monitoring device, previous acute healthcare utilisation, missed appointments, and child welfare services involvement and socioeconomic status). The model exhibited good discrimination (area under the ROC = 0.81), accurately stratified children into low-, medium-, and high-risk groups, and demonstrated significant differences between median time to healthcare utilisation. Conclusion. Our model identified patients at an increased risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA. [ABSTRACT FROM AUTHOR]

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model's prediction power and spectral characteristic information. (2) The feature band selection method's prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content. [ABSTRACT FROM AUTHOR]

Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898–0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719–0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713. [ABSTRACT FROM AUTHOR]

Background. Polycystic ovary syndrome (PCOS) is an endocrine disease attributed to multiple genetic variants and environmental factors. We aimed to find the causal association of homocysteine (Hcy) with PCOS. Methods. A two-sample Mendelian randomization (MR) analysis was performed. We selected 14 single-nucleotide polymorphisms (SNPs) as instrumental variables to predict the risk of PCOS from genome-wide association studies (GWAS). The summary statistics of PCOS were obtained from 3 large genome-wide association studies in the European population, involving 4,138 cases and 20,129 controls, 3,609 cases and 229,788 controls, 994 cases and 165,817 controls, separately. Results. The IVM analyses revealed that plasma Hcy levels were not causally associated with the risk of PCOS in the meta-analysis (combined effect = 1.032, 95% confidence interval (CI): 0.885–1.203, p = 0.688). Conclusions. There was no sufficient evidence to support the causal association of the Hcy with the risk of PCOS. [ABSTRACT FROM AUTHOR]

Interwell connectivity assessment in polymer-driven reservoirs is critical for setting appropriate injection rates and improving oil recovery. Traditional deep learning techniques often lack accuracy and reliability when applied to short-term oilfield production data. In response, the A-LSTM algorithm is proposed, which integrates the attention mechanism with a long- and short-term memory network (LSTM). The predictive accuracy of A-LSTM is assessed and juxtaposed with LSTM and support vector regression (SVR) algorithms for short-term single-well daily oil production analysis. The Huber loss function was utilized to quantify the difference between predicted and actual results, resulting in a dynamic production prediction model. An interwell connectivity (IWC) assessment model is then obtained by fusing the dynamic production prediction model with the EFAST method, thus demonstrating the superior prediction accuracy of A-LSTM in oil production prediction and connectivity assessment. Moreover, the credibility of the assessment is further corroborated through numerical simulations and interwell tracer tests. The study results showed that the interwell connectivity evaluation model based on the A-LSTM algorithm and EFAST method is not only capable of accurately predicting the single-well daily oil production using a small sample dataset but also a highly reliable method for interwell connectivity evaluation, and the application of the interwell connectivity assessment model can further guide polymer flooding work in oilfields. [ABSTRACT FROM AUTHOR]

Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the "ImageNet" collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model's generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model's outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F 1 -score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Purpose. Consolidation with durvalumab is standard of care in the management of unresectable stage 3 non-small-cell lung cancer (NSCLC) postchemoradiation, and pneumonitis is an independent potential treatment complication of both treatment strategies. This study seeks to determine the timing of radiation pneumonitis (RP) by receipt of durvalumab. In addition, we reviewed the preventative strategies guided by pathophysiology of pneumonitis. Methods. We identified patients with unresectable Stage 3 NSCLC who developed grade ≥2 RP after chemoradiotherapy. Time-to-RP was defined from date of completion of radiotherapy to date of radiological diagnosis of RP and accompanying clinical symptoms. Early RP was defined as RP within 2 months of completion of radiotherapy. Differences in time-to-RP by receipt of durvalumab were evaluated using Wilcoxon rank-sum test. Differences in those who had early vs late RP by receipt of durvalumab was evaluated using Fisher's exact test. Logistic regression was used to evaluate patient and treatment factors associated with early RP. Results. Of the 144 patients with Stage 3 NSCLC who had definitive chemoradiotherapy, 31 (22%) developed grade ≥2 RP and were included in the study. There was one patient with grade 5 RP. The median age of the cohort was 67 years (range 41–87). The mean lung dose, V5Gy, and V20Gy were 15.8Gy (SD = 1.56), 60.14% (SD 2.73), and 29.96% (SD 1.82), respectively. Twelve (39%) patients received durvalumab. The median time-to-RP was 3.4 months (range: 1.7–7.2) and 2.3 months (range: 0.6–9.6) in patients who had durvalumab and no durvalumab, respectively (P = 0.01). 83% (10/12) of patients who had durvalumab and 58% (11/19) of patients who did not have durvalumab had late RP (P = 0.14). No other patient and treatment factors were associated with early RP. Conclusion. Patients on durvalumab may have late-onset RP; therefore, further studies with larger cohort of patients and development of new predictive models that incorporate evolving management are needed should preventative strategies of RP be considered in routine clinical practice. [ABSTRACT FROM AUTHOR]

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]

Hearing aids and other hearing devices should provide the user with a benefit, for example, compensate for effects of a hearing loss or cancel undesired sounds. However, wearing hearing devices can also have negative effects on perception, previously demonstrated mostly for spatial hearing, sound quality and the perception of the own voice. When hearing devices are set to transparency, that is, provide no gain and resemble open-ear listening as well as possible, these side effects can be studied in isolation. In the present work, we conducted a series of experiments that are concerned with the effect of transparent hearing devices on speech perception in a collocated speech-in-noise task. In such a situation, listening through a hearing device is not expected to have any negative effect, since both speech and noise undergo identical processing, such that the signal-to-noise ratio at ear is not altered and spatial effects are irrelevant. However, we found a consistent hearing device disadvantage for speech intelligibility and similar trends for rated listening effort. Several hypotheses for the possible origin for this disadvantage were tested by including several different devices, gain settings and stimulus levels. While effects of self-noise and nonlinear distortions were ruled out, the exact reason for a hearing device disadvantage on speech perception is still unclear. However, a significant relation to auditory model predictions demonstrate that the speech intelligibility disadvantage is related to sound quality, and is most probably caused by insufficient equalization, artifacts of frequency-dependent signal processing and processing delays. [ABSTRACT FROM AUTHOR]

Background. Although neonatal sepsis is a major public health problem contributing to 30-50% of neonatal deaths in low- and middle-income countries, data on predictors of time to death are limited in Eastern Ethiopia. This study is aimed at determining predictors of time to death among neonates with sepsis admitted in public hospitals in Eastern Ethiopia. Methods. An institutional-based retrospective cohort study was conducted among 415 neonates admitted to referral hospitals in Eastern Ethiopia with sepsis from January 1, 2021, to December 31, 2021. Data were collected from medical records by using structured checklist and entered using EpiData 3.1 and analyzed using Stata 17. The Kaplan-Meier curves and log-rank tests were used to describe survival experience among different categories. The proportional hazard assumption and goodness of fit for the Cox regression model were checked. The Cox regression model was used to identify the significant predictors. Hazard ratios (HRs) with 95% confidence intervals (CI) were calculated. Finally, statistical significance was set at a p value < 0.05 in the Cox regression analysis. Results. Of the 415 neonates with neonatal sepsis, 71 (17.1%) (95% CI: 13.60-21.08) died at discharge, with a median time to death of 14 days. The overall incidence rate of mortality was 36.5 per 1000 neonate days. Low birthweight (AHR = 2.50 ; 95% CI: 1.15-5.44), maternal age ≥ 35 years (AHR = 3.17 ; 95% CI: 1.11, 9.04), low fifth-minute Apgar score (AHR: 2.32; 95% CI: 1.30-4.14), and late initiation of breastfeeding (AHR = 4.82 ; 95% CI: 1.40-16.65) were independent predictors of mortality among neonates with sepsis. Conclusions. Almost one in five neonates with sepsis died at discharge. Low birthweight, maternal age ≥ 35 years, low fifth-minute Apgar score, and late initiation of breastfeeding were predictors of mortality. [ABSTRACT FROM AUTHOR]

Background. Violence at work against healthcare professionals is a frequent and pervasive problem. There are growing data that indicate nurses are especially susceptible to experiencing violent acts at work. Resilience helps strengthen nursing competency in the proper management of stressful circumstances, like being exposed to workplace violence. Aim. The aim of the study is to assess the impact of resilience on workplace violence among mental health nurses. Method. A cross-sectional research design was used to conduct this study with a convenience sample of 361 nurses recruited from a governmental psychiatric hospital in Saudi Arabia. Tools. Data were collected using two tools: the first tool was the workplace violence questionnaire, which collected nurses' demographic, job, and workplace violence data, and the second was the resilience at work scale to assess nurses' resilience. Results. This survey found that 70.4% of nurses experienced workplace violence in the last year, and fewer than half were resilient at work. Close to one-third (33.5%) of nurses were terrified and confused after workplace violence. The most violent repercussions were psychological (46.8%): dread, tension, and worry. Additionally, 48.8% of nurses exhibited high work resilience. Conclusion. A considerable percentage of mental health nurses encountered instances of violence during the provision of care inside mental health hospital settings. The variables of nationality (non-Saudi nurses), rotating work shift, educational levels, and exhibiting a lower level of resilience were found to have a statistically significant correlation with instances of workplace violence. Recommendation. Training programs and educational initiatives should be developed and implemented to equip nurses with the necessary knowledge and skills to effectively respond to and prevent workplace violence incidents. By providing comprehensive training, healthcare organizations can empower nurses to address and mitigate workplace violence, ultimately creating a safer and more supportive work environment. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative model for evaluating the impact of environmental factors on drone-to-ground control station datalink communications. Our approach integrates both deterministic and stochastic processes to account for small-scale and large-scale fading effects, encompassing propagation attenuation, the Rician fading model, and Gaussian noise to accurately reflect real-world conditions. The model is implemented on signals transmitted using spread spectrum modulation. Through a comparative analysis of the model's predictions against actual signals received in three distinct environments, the model's efficacy in diverse scenarios is affirmed. Error metrics obtained from Monte Carlo simulations are employed to validate the theoretical results against experimental data. The proposed approach is pivotal for predicting the transmission range and understanding the electromagnetic susceptibility of the datalink, offering a substantial contribution to the optimization of remote drone control. [ABSTRACT FROM AUTHOR]

Iran is a developing country with low levels of economic development and globalization and is ruled by a theocratic government. To address the lack of national research on well-being in Iran, this retrospective observational study aims to examine life satisfaction and its main determinants among Iranian adults. Using World Gallup Poll data collected between 2006 and 2017, we examined life satisfaction as a cognitive aspect of subjective well-being in relation to various factors. Our results show that income is the strongest predictor of life satisfaction, followed by standard of living, gender, social support, age, negative affect, and education. In developing countries such as Iran, which face significant economic, political, and social challenges, individuals prioritize the satisfaction of basic needs by emphasizing factors such as the socioeconomic status. In contrast, developed countries with established welfare systems may emphasize other values such as social connections and healthy lifestyle behaviors as key factors in life satisfaction. This study contributes to a deeper understanding of the determinants of life satisfaction in Iran and provides insights for future research and policy making. [ABSTRACT FROM AUTHOR]

Purpose. Breast cancer is the most common cancer among women globally, with an incidence of approximately two million cases in 2018. Organised age-based breast cancer screening programs were established worldwide to detect breast cancer earlier and to reduce mortality. Currently, there is substantial anticipation regarding risk-adjusted screening programs, considering various risk factors in addition to age. The present study investigated the discriminatory accuracy of breast cancer risk prediction models and whether they suit risk-based screening programs. Methods. Following the PICO scheme, we conducted an overview of reviews and systematically searched four databases. All methodological steps, including the literature selection, data extraction and synthesis, and the quality appraisal were conducted following the 4-eyes principle. For the quality assessment, the AMSTAR 2 tool was used. Results. We included eight systematic reviews out of 833 hits based on the prespecified inclusion criteria. The eight systematic reviews comprised ninety-nine primary studies that were also considered for the data analysis. Three systematic reviews were assessed as having a high risk of bias, while the others were rated with a moderate or low risk of bias. Most identified breast cancer risk prediction models showed a low prognostic quality. Adding breast density and genetic information as risk factors only moderately improved the models' discriminatory accuracy. Conclusion. All breast cancer risk prediction models published to date show a limited ability to predict the individual breast cancer risk in women. Hence, it is too early to implement them in national breast cancer screening programs. Relevant randomised controlled trials about the benefit-harm ratio of risk-adjusted breast cancer screening programs compared to conventional age-based programs need to be awaited. [ABSTRACT FROM AUTHOR]

Purpose. To evaluate the efficacy of spironolactone in the treatment of chronic central serous chorioretinopathy (CSC) and identify imaging characteristics that can predict the benefit of spironolactone treatment. Methods. Patients with chronic CSC were treated with spironolactone (20 mg/tid) and followed for 6 months. The primary outcome measure was complete resolution of the subretinal fluid (SRF), and the best-corrected visual acuity (BCVA), the SRF area, the central macular thickness (CMT), the subfoveal choroidal thickness (SFCT), and the density of the choriocapillaris vessel and adverse events were secondary outcome measures. Patients who presented complete resolution of SRF were included in the responder group and the other patients who had moderate or no resolution were included in the nonresponder group. Imaging characteristic comparisons between the responder and nonresponder groups were performed with regression analyses to identify factors that are predictive of a good response to treatment. Results. Forty-two eyes of 42 patients with a mean age of 46.06 ± 6.66 years were included. A total of 57.1% of the patients achieved a complete resolution of SRF. The mean SRF area, CMT, and SFCT decreased significantly (all P < 0.05) throughout the follow-up period and BCVA improved slightly (P > 0.05). The vascular density of the choriocapillaris of the fellow eyes did not vary significantly during treatment. Logistic regression analysis revealed that SFCT (P = 0.002) and the intact ellipsoid zone (P = 0.001) were correlated with disease resolution. A relatively higher baseline SFCT was a predictive factor associated with a good response to treatment according to multivariate analysis. Conclusions. This study suggested that oral spironolactone could be an effective and safe therapy for chronic CSC patients. Eyes with a higher baseline SFCT and intact ellipsoid zone could have a good response. These parameters are an important prognostic marker. [ABSTRACT FROM AUTHOR]

Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations. [ABSTRACT FROM AUTHOR]

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time-activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross-validation (CV) R 2 ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R 2 of the RF model increased up to 7% (from 0.82 ± 0.13 to 0.88 ± 0.10), while the RMSE reduced up to 18% (from 19.8 ± 5.4 to 16.3 ± 4.5) in BJ. [ABSTRACT FROM AUTHOR]

For weak grid scenario with high new energy proportion, large fluctuations of load are prone to cause low-voltage ride through. Moreover, stator transient magnetic flux will cause overvoltage and overcurrent problems in the rotor of doubly fed induction generator. Based on model predictive control, a control strategy for transient flux linkage and reactive power compensation is proposed. Firstly, regarding the issue of reactive power allocation of grid side converters (GSC) and rotor side converters (RSC), an allocation strategy is derived under minimizing winding energy loss on case of low-voltage ride through, enabling the wind energy conversion system to provide reactive power support during grid voltage recovery process. Meanwhile, an improved mixed second- and third-order generalized integrator (MSTOGI) phase-locked loop (PLL) is used to extract the positive and negative sequence components of the power grid voltage, further for RSC control. Secondly, in response to power grid faults, considering the influence of stator DC transient flux and negative sequence flux components on rotor current, by injecting rotor transient compensation current and stator flux feedforward compensation into RSC, the rotor impulse voltage and loop current are reduced. Moreover, combined with model predictive control algorithm, a control strategy of rotor current is designed. Finally, a simulation platform is built to validate the effectiveness of the proposed method based on comparing with several traditional vector control low-voltage ride through methods. [ABSTRACT FROM AUTHOR]

This research focuses on a comprehensive comparative analysis of shear strength prediction in slab–column connections, integrating machine learning, design codes, and finite element analysis (FEA). The existing empirical models lack the influencing parameters that decrease their prediction accuracy. In this paper, current design codes of American Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innovative approaches like the compressive force path method and machine learning models are employed to predict the punching shear strength using a comprehensive database of 610 samples. The database consists of seven key parameters including slab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength of longitudinal steel (fy), longitudinal reinforcement ratio (ρl), ultimate load-carrying capacity (Vu), and concrete compressive strength (fc). Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. PSOFNN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% and 1.214%, respectively. The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. The research also provides insight into the application of metaheuristic models along with ANN, revealing that not all metaheuristic models can outperform ANN as usually perceived. The study also highlights superior predictive capabilities of EC2 over ACI 318-19 for punching shear values. [ABSTRACT FROM AUTHOR]

Epigenetic regulation has been thoroughly investigated in recent years and has emerged as an important aspect of chronic lymphocytic leukemia (CLL) biology. Characteristic aberrant features such as methylation patterns and global DNA hypomethylation were the early findings of the research during the last decades. The investigation in this field led to the identification of a large number of genes where methylation features correlated with important clinical and laboratory parameters. Gene-specific analyses investigated methylation in the gene body enhancer regions as well as promoter regions. The findings included genes and proteins involved in key pathways that play central roles in the pathophysiology of the disease. Τhe application of these findings beyond the theoretical understanding can not only lead to the creation of prognostic and predictive models and scores but also to the design of novel therapeutic agents. The following is a review focusing on the present knowledge about single gene/gene promoter methylation or mRNA expression in CLL cases as well as records of older data that have been published in past papers. [ABSTRACT FROM AUTHOR]

This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model's fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is a significant pig disease causing substantial annual losses exceeding half a billion dollars to the United States pork industry. The cocirculation and emergence of genetically distinct PRRSV-2 viruses hinder PRRS control, especially vaccine development. Similar to other viral infections like seasonal flu and SARS-CoV-2, predictive tools for identifying potential emerging viral variants may prospectively aid in preemptive disease mitigation. However, such predictions have not been made for PRRSV-2, despite the abundance of relevant data. In this study, we analyzed a decade's worth of virus ORF5 sequences (n = 20,700) and corresponding metadata to identify phylogenetic-based early indicators for short-term (12 months) and long-term (24 months) variant emergence. Our analysis focuses on PRRSV-2 Lineage 1, which was the predominant lineage within the U.S. during this period. We evaluated population expansion, spatial distribution, and genetic diversity as key success metrics for variant emergence. Our findings indicate that successful variants were best characterized as those that underwent population expansion alongside widespread geographical spread but had limited genetic diversification. Conditional logistic regression revealed the local branching index as the sole informative indicator for predicting population expansion (balanced accuracy (BA) = 0.75), while ancestral branch length was strongly linked to future genetic diversity (BA = 0.79). Predicting spatial dispersion relied on the branch length and putative antigenic difference (BA = 0.67), but their causal relationships remain unclear. Although the predictive models effectively captured most emerging variants (sensitivity = 0.58–0.81), they exhibited relatively low positive predictive value (PPV = 0.09–0.16). This initial step in PRRSV-2 prediction is a crucial step for more precise prevention strategies against PRRS in the future. [ABSTRACT FROM AUTHOR]

This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using p value, Kendall's tau, and Sen's slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region. [ABSTRACT FROM AUTHOR]

Objective. The aim of the present study was to investigate the relationship between individual coping (IC) and dyadic coping (DC), more specifically the respective predictive impact of DC and sociodemographic, partnership-related, and medical variables on IC in haematological cancer patients. Methods. In this multicenter longitudinal study, we examined DCI (Dyadic Coping Inventory) and IC (Freiburg Questionnaire on Coping with Illness) in 214 haematological cancer patients after their treatment and six months later. Associations between the coping constructs were calculated using Pearson correlations. Multiple linear regression analyses were conducted to examine the predictive impact of patient-specific variables on IC strategies. Results. Pearson correlation revealed numerous significant associations between DC and IC at the baseline and follow-up, but none of them yielded r ≥ 0.4 (p < 0.01). Regression analyses showed a predictive impact of supportive DC on compliance (p < 0.05) and of common DC on active IC (p < 0.01). Concerning patient-specific variables, age stood out as a positive predictor of trivialization (p < 0.001) and compliance (p < 0.01) and psychological distress as a predictor of depressive IC (p < 0.001), distraction (p < 0.05), and trivialization (p < 0.05). Conclusion. Our results indicate that patients' personal characteristics, such as age and psychological distress, outweigh the influence of DC on IC in haematological cancer patients. [ABSTRACT FROM AUTHOR]

When high-speed trains operate under braking conditions, the wheel-rail interaction increases significantly, which can lead to more serious wear problems. To analyze the evolution of wheel wear and the dynamic characteristics of wheel-rail under braking conditions, this paper first carries out long-term monitoring of the service state of wheel-rail during the operation period. The study analyzed the matching characteristics of the measured wheel-rail profile and calibrated the wear model by using the collected data. The resulting wear prediction model was then used to examine the wheel wear characteristics under braking conditions. The research results indicate that during the operation period, the wheel experiences tread concave wear within a range of ±20 mm of the rolling circle, at a rate of approximately 0.05 mm per 10,000 km. Meanwhile, the rail experiences top surface wear at a natural rate of about 0.09 mm per year. Concave wear causes the contact point of the wheel-rail to appear in two zones, resulting in a sudden change of contact geometric parameters. The concave worn wheel and rail with a 60 N profile have better matching compared to the 60 rail profile. Increasing the braking torque and wheel-rail friction coefficient will significantly increase the wheel wear depth on straight sections. On small-radius curve sections, rail side lubrication can significantly reduce high rail side wheel flange wear. A worn concave wheel can lead to unfavorable wheel-rail contact geometry characteristics and increase low-frequency components in the vehicle's lateral dynamic response. [ABSTRACT FROM AUTHOR]

Background. Although several risk-predictive models for patients undergoing surgical valve replacement (SVR) have been published, reports on composite endpoints of adverse events in these patients are limited. This study aimed to establish a novel, easy-to-use prognostic prediction model of composite endpoints in patients following SVR. Methods. According to the inclusion criteria, patients with successful SVR were enrolled. Adverse events, including heart failure hospitalization, stroke, major bleeding, uncontrolled infection, secondary surgery, postoperative arrhythmia, and all-cause mortality during follow-up, were tracked. All datasets were randomly divided into the derivation and validation cohorts at a ratio of 7 to 3. Logistic regression analysis was used to screen for independent predictors and construct a nomogram for adverse events. We further presented a calibration curve and decision curve analysis for evaluating prediction models. Results. According to the multivariate logistic regression analyses, three variables were selected for the final predictive model, including platelet-to-lymphocyte ratio, diabetes mellitus, and albumin. A nomogram was then constructed to present the results. The C-index of the model was 0.73 (95% confidence interval: 0.65–0.81) for the derivation cohort and 0.75 (95% confidence interval: 0.64–0.86) for the validation cohort. The calibration curve demonstrated that the results of the nomogram agreed with actual observations (Brier score = 0.09). Conclusions. We developed an effective nomogram to predict the occurrence of composite adverse events in patients following SVR. This model could be used to evaluate the mid-term risks of adverse events as well as provide clinicians and patients with a basis for decision-making. [ABSTRACT FROM AUTHOR]

Current models to determine the risk of airborne disease infection are typically based on a backward quantification of observed infections, leading to uncertainties, e.g., due to the lack of knowledge whether the index person was a superspreader. In contrast, the present work presents a forward infection risk model that calculates the inhaled dose of infectious virus based on the virus emission rate of an emitter and a prediction of Lagrangian particle trajectories using CFD, taking both the residence time of individual particles and the biodegradation rate into account. The estimation of the dose-response is then based on data from human challenge studies. Considering the available data for SARS-CoV-2 from the literature, it is shown that the model can be used to estimate the risk of infection with SARS-CoV-2 in the cabin of a Do728 single-aisle aircraft. However, the virus emission rate during normal breathing varies between different studies and also by about two orders of magnitude within one and the same study. A sensitivity analysis shows that the uncertainty in the input parameters leads to uncertainty in the prediction of the infection risk, which is between 0 and 12 infections among 70 passengers. This highlights the importance and challenges in terms of superspreaders for risk prediction, which are difficult to capture using standard backward calculations. Further, biological inactivation was found to have no significant impact on the risk of infection for SARS-CoV-2 in the considered aircraft cabin. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN. [ABSTRACT FROM AUTHOR]

The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations. [ABSTRACT FROM AUTHOR]

Gypseous soil poses a significant challenge in geotechnical engineering due to its susceptibility to collapse under saturation. This type of soil covers approximately 33% of regions in Iraq, primarily in unsaturated conditions. This study focuses on two types of gypseous soils: one with moderate gypsum content from Karbala city (G1) and the other with high-gypsum content from Tikrit city (G2), to investigate the effects of cement treatment on their water retention behavior and potential for reducing collapse. Different percentages of cement were mixed with both soils to determine the optimum soil–cement mixture for reducing collapse potential (CP) through single oedometer tests. The water retention characteristics and water retention behavior of samples with varying gypsum content and different levels of cement treatment were examined and compared using a controlled-suction oedometer. The soil–water retention curve (SWRC) of these natural and treated gypseous soils was also investigated and compared in both wetting and drying paths. Additionally, multiple pedotransfer functions (PTFs) were assessed to identify or adapt prediction equation(s) for the SWRC of gypseous soil both with and without cement treatment with acceptable accuracy. The results show that adding cement can decrease the CP of gypseous soils; it also affects their SWRC significantly. By making some simple modifications, the PTFs demonstrate acceptable estimations for the water retention curve of both natural and cement-treated gypseous soils. [ABSTRACT FROM AUTHOR]

To improve the accuracy of deflection prediction for prestressed concrete (PSC) box-girder bridges with a stay cable system (SCS) during tensioning, this study employs the Latin hypercube sampling (LHS) technique to sample random variables affecting long-term deflection of the main girder. A long-term deflection randomness analysis model is established, and the shear stiffness degradation factor is included to quantify the contribution of diagonal web cracking to the main girder deflection. Uncertainty and sensitivity analysis are conducted for the Dongming Huanghe River Highway Bridge. An objective function is applied to select the optimal combination of random variables, and a modified model is established and verified for accuracy. Results show that diagonal web-cracking accounts for 8.8% of total deflection and cannot be ignored, and the prestressed tension control stress, creep uncertainty coefficient, and concrete density significantly impact long-term deflection. The modified model predicts deflection with greater accuracy than the mean value model. [ABSTRACT FROM AUTHOR]

Longyan City in Fujian Province is one of China's top-quality tobacco-producing areas and plays an essential role in local economic development. To determine the correlation between heavy metal content in tobacco leaves and soil factors, soil physical and chemical properties and heavy metal contents of lead, cadmium, and copper in tobacco leaves were measured and analyzed by the correlation regression method. The content of lead, cadmium, and copper in soil was determined using hydrochloric acid extraction-AAS and graphite furnace atomic absorption spectrometry. Inductively coupled plasma-mass spectrometry was used to determine heavy metal in tobacco leaf. The findings revealed that the average concentrations of lead, cadmium, and copper in the soil were 12.1, 0.092, and 3.88 mg/kg, respectively. In contrast, the average levels of lead, cadmium, and copper in tobacco leaves were 2.33, 4.89, and 4.35 mg/kg, respectively. The cadmium enrichment coefficient of 54.3 was higher than lead and copper, indicating a greater health risk. Soil pH value was negatively correlated with lead content in tobacco leaf, while potassium and phosphorus nutrient levels were negatively correlated with copper content. In contrast, a positive correlation was established between the presence of organic matter with cadmium content in tobacco leaves. The prediction models of lead, cadmium, and copper in tobacco leaves can be expressed by the regression equation corresponding to each heavy metal as follows: Y Pb = 2.33 − 0.005 ∗ X K + 0.007 ∗ X N − 0.271 ∗ X pH + 0.065 ∗ X Pb ( R 2 = 0.787), Y Cd = 1.55 + 0.012 ∗ X OM − 0.014 ∗ X Cu + 34.6 ∗ X Cd ( R 2 = 0.891), and Y Cu = 4.64 − 0.029 ∗ X P − 0.007 ∗ X K + 0.245 ∗ X Cu ( R 2 = 0.724), respectively. The prediction models above provide an effective predictive tool for assessing heavy metal risk in tobacco leaves using soil properties in the study area. [ABSTRACT FROM AUTHOR]

Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures. [ABSTRACT FROM AUTHOR]

Reasonable production prediction of coalbed methane (CBM) is of great significance for improving the economic benefit of CBM reservoirs. Current prediction methods for CBM production focus on the later stages of development, with few studies on early production forecasting. The objective of this work is to provide a reliable new idea for the early production prediction of CBM through various analyses and demonstrations. First, the CBM development modes are classified according to the production characteristics of the Panhe demonstration block of Shaanxi Province, China. Second, an efficient and feasible early production prediction model is established based on the geological potential and development potential. Finally, using the proposed model, different modes' production characteristics and optimization strategies are analyzed. The research shows that the gas production profiles can be divided into two modes: single-peak mode (SPM) and multipeak mode (MPM). The peak production and average EUR of the SPM are 49.6% and 32.4% higher than those of the MPM, but the stable production period is only 0.2~1 year. In terms of the geological potential of CBM wells, the gas content, critical desorption pressure, and formation coefficient of the SPM are 6.7%, 13.3%, and 37.8% higher than those of the MPM, and the gas wells are mainly located in the high part of the coal seam (the average height difference is about 20 m). Besides, the concept of quasidesorption degree P dq is innovatively introduced to characterize the development potential of gas well. The P dq has an exponential relationship with CBM production, and the coefficient of the exponential term in SPM is approximately 22% larger than that in MPM. Moreover, the production of gas wells is greatly affected by the continuity of production. In the process of gas production, the influence of factors such as equipment shutdown should be minimized. To examine the applicability of the proposed method, the model is applied to an actual CBM well in Panhe, and the prediction accuracy is higher than 85%. [ABSTRACT FROM AUTHOR]

Organic carbon and total nitrogen are essential nutrients for plant growth. The presence of these nutrients at acceptable levels can create an optimal environment for the development of crops of interest. The application of spectroscopic techniques and the use of machine learning algorithms have made it possible to calibrate models capable of predicting the number of elements present in the soil. One of these techniques is hyperspectral imaging, which captures portions of the electromagnetic spectrum where the materials present in the soil can be differentiated due to the vibrations of chemical bonds. The objective of this research is to use statistical models to predict OC and N in soils from hyperspectral images. Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination ( R 2 ), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. For OC, the values found for the RF model were an R 2 of 0.87, an RMSEP of 0.10, and an RPD of 6.74; the SVM model presented an R 2 of 0.92, an RMSEP of 0.20, and an RPD of 3.56. For the variable N, the values found for the RF model were an R 2 of 0.79, an RMSEP of 0.03, and an RPD of 5.44; for the SVM model, they were an R 2 of 0.87, an RMSEP of 0.08, and an RPD of 2.76. The RF model showed a better fit for both variables. The SVM model also produced acceptable results. The results show that machine learning models are a good alternative for analysing soil-related variables. [ABSTRACT FROM AUTHOR]