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Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation. [ABSTRACT FROM AUTHOR]

Objectives: Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model. Methods: This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University. Results: The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility. Conclusions: An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients. [ABSTRACT FROM AUTHOR]

Purpose: To identify the key risk factors for venous thromboembolism (VTE) in urological inpatients based on the Caprini scale using an interpretable machine learning method. Methods: VTE risk data of urological inpatients were obtained based on the Caprini scale in the case hospital. Based on the data, the Boruta method was used to further select the key variables from the 37 variables in the Caprini scale. Furthermore, decision rules corresponding to each risk level were generated using the rough set (RS) method. Finally, random forest (RF), support vector machine (SVM), and backpropagation artificial neural network (BPANN) were used to verify the data accuracy and were compared with the RS method. Results: Following the screening, the key risk factors for VTE in urology were "(C1) Age," "(C2) Minor Surgery planned," "(C3) Obesity (BMI > 25)," "(C8) Varicose veins," "(C9) Sepsis (< 1 month)," (C10) "Serious lung disease incl. pneumonia (< 1month) " (C11) COPD," "(C16) Other risk," "(C18) Major surgery (> 45 min)," "(C19) Laparoscopic surgery (> 45 min)," "(C20) Patient confined to bed (> 72 h)," "(C18) Malignancy (present or previous)," "(C23) Central venous access," "(C31) History of DVT/PE," "(C32) Other congenital or acquired thrombophilia," and "(C34) Stroke (< 1 month." According to the decision rules of different risk levels obtained using the RS method, "(C1) Age," "(C18) Major surgery (> 45 minutes)," and "(C21) Malignancy (present or previous)" were the main factors influencing mid- and high-risk levels, and some suggestions on VTE prevention were indicated based on these three factors. The average accuracies of the RS, RF, SVM, and BPANN models were 79.5%, 87.9%, 92.6%, and 97.2%, respectively. In addition, BPANN had the highest accuracy, recall, F1-score, and precision. Conclusions: The RS model achieved poorer accuracy than the other three common machine learning models. However, the RS model provides strong interpretability and allows for the identification of high-risk factors and decision rules influencing high-risk assessments of VTE in urology. This transparency is very important for clinicians in the risk assessment process. [ABSTRACT FROM AUTHOR]

Background: Oral aripiprazole exhibits favorable clinical efficacy and safety in the suppression of tics in children and adolescents with tic disorders. This study aims to evaluate and compare the cost-effectiveness of high-dose and low-dose aripiprazole in children and adolescents with tic disorders from the perspective of the Chinese healthcare system. Methods: A questionnaire survey was conducted on 146 patients with tic disorders, of whom 144 completed EQ-5D-Y and YGTSS. Four models were built to convert YGTSS onto EQ-5D-Y utility using two mapping algorithms. We constructed a decision tree model containing efficacy and safety to compare the cost-effectiveness of high-dose and low-dose aripiprazole based on our mapping function. Results: The GLM with model 1 (YGTSS total tic scores) was selected as the preferred function in our decision tree model. The base case cost-effectiveness analysis showed that compared to low-dose aripiprazole, high-dose aripiprazole improves effectiveness by 0.001QALYs and increases the overall cost by $197.99, resulting in an ICER of $174339.22 per QALY, which exceeds three times the gross domestic product per capita. Hence, high-dose aripiprazole is not likely to be a cost-effective option for child patients with tic disorders. One-way sensitivity analysis and probabilistic sensitivity analysis showed that these results is robust. Conclusion: On the basis of currently available data, low-dose aripiprazole may be a safe, effective, and economical dosage for children and adolescents with tic disorders. Limitations: The main limitation of our study is the lack of utility directly used for cost-effectiveness analysis. We obtained the utility of patients with tic disorders indirectly by the mapping function. This may introduce some bias and uncertainty. And it is a limitation to use the direct medical costs of Germany in our model. Although we converted it to the equivalent value of China using purchasing power parities, caution should be exercised when interpreting the results of this study. [ABSTRACT FROM AUTHOR]

Background: Periodontitis is associated with poor pregnancy outcomes, indicating periodontal health as an important health concern for pregnant women. Herein, this study identified risk indicators for periodontitis and developed a nomogram for predicting the risk of periodontitis in pregnancy by analyzing periodontitis and associated factors in pregnant women. Materials and methods: A community-based cross-sectional study was conducted by including 438 pregnant women at 10–36 gestational weeks from Langfang, China. Pregnant women were examined for periodontal status, and their demographic, socioeconomic, and oral health behavior data were collected. Potential influencing factors of periodontitis were analyzed with univariate and multivariate logistic regression analyses. A nomogram was developed, followed by the assessment of its validation and discriminatory abilities. Results: The prevalence of periodontitis was 59.8% in pregnant women. Periodontitis-associated variables in pregnant women were gestational age, non-first pregnancy, daily tooth brushing frequency of ≤ 1 before pregnancy, and annual frequency of periodontal treatment < 1 (including no periodontal treatment). The risk of periodontitis was positively associated with gestational age (OR = 1.28, 95% CI = 1.17–1.39, p < 0.001). Pregnancy history showed a strong positive association (OR = 6.57, 95% CI = 1.22–35.43, p = 0.03). Daily tooth brushing frequency before pregnancy was also positively associated with periodontitis (OR = 1.54, 95% CI = 1.03–2.79, p = 0.05). Additionally, the annual frequency of periodontal treatment exhibited a positive association, with higher odds observed for those with less frequent treatment (OR = 2.28, 95% CI = 1.25–4.14, p = 0.05; OR = 7.37, 95% CI = 3.04–22.06, p < 0.001). These four factors were used to develop a nomogram for predicting periodontitis in pregnant women. The area under the receiver operating characteristic curve of the nomogram was 0.855 and 0.831 in the training and testing cohorts, respectively, reflecting the superior prediction accuracy of the nomogram. The calibration curve and decision curve analysis demonstrated the good performance and net benefit of the nomogram. Conclusion: Risk factors for periodontitis in pregnant Chinese women include gestational age, non-first pregnancy, lower frequency of daily tooth brushing before pregnancy, and lower frequency of periodontal treatment. An easy-to-use nomogram with acceptable accuracy can allow for the prediction of periodontitis risk in pregnant Chinese women. Clinical relevance: With the assistance of this nomogram, clinicians can evaluate the risk of periodontitis in pregnancy, thereby offering more tailored oral health education to women of reproductive age. [ABSTRACT FROM AUTHOR]

Cyclostratigraphy is an important observational window into the history of the Earth-Moon system. However, there is limited information from the Mesoproterozoic era (1.0 to 1.6 billion years ago); accordingly, only weak constraints on Earth-Moon separation and tidal dissipation are available for this time. To close this knowledge gap, we analyze cyclostratigraphy from the Yemahe Formation (~1.2 billion years ago), Wumishan Formation (~1.5 billion years ago), and Chuanlinggou Formation (~1.6 billion years ago) in China. We use a Bayesian inversion method to analyze the three cyclostratigraphic sections. We combine previous results with these three estimates to construct an updated Earth-Moon system evolution and tidal dissipation history after 2.5 billion years ago. The results show a tidal dissipation peak that is consistent with the model predictions within the error range but also that there may be an additional resonance fluctuation in the Mesoproterozoic era. [ABSTRACT FROM AUTHOR]

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

Objectives: Parkinson disease (PD) is the third leading cause of mortality among middle‐aged and older individuals in China. This study aimed to explore the trends and distribution features of PD mortality in China from 2013 to 2021 and make predictions for the next few decades. Methods: Relevant data were obtained from the Chinese Center for Disease Control and Prevention Disease Surveillance Point system. The joinpoint regression model was used to evaluate trends. The R software was used to predict future trends. Results: Age‐standardized mortality rate (ASMR) of PD increased from 0.59 per 100,000 individuals to 1.22 per 100,000 individuals from 2013 to 2021, with an average annual percent change (AAPC) of 9.50 (95% CI: 8.24–10.78). The all‐age ASMR of PD were higher in male individuals than in female individuals, and ASMR increased with age. The number of deaths and ASMR increased gradually from west to east and from rural to urban areas. Furthermore, ASMR is expected to increase to 2.66 per 100,000 individuals by 2040. Conclusions: The heightened focus on the ASMR of PD among male individuals, urban areas, eastern China, and individuals aged ≥85 years has become a key determinant in further decreasing mortality, thereby exhibiting novel challenges to effective strategies for disease prevention and control. [ABSTRACT FROM AUTHOR]

Background: Congenital pneumonia is a common respiratory disease in newborns, often influenced by obstetric factors. Clinical diagnosis can be delayed, prompting interest in using systemic inflammatory indicators to predict various diseases. Objective: Our aim was to evaluate the predictive value of maternal systemic inflammatory indicators before delivery for congenital pneumonia in newborns. Methods: This retrospective study included full-term, singleton infants born at the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University between January 2017 and December 2022. Infants admitted to the neonatal department within 48 h of birth were divided into two groups: 46 with clinical congenital pneumonia (the observation group) and 65 without congenital pneumonia (the control group). Maternal peripheral blood, complete blood cell count, and general condition within 72 h before delivery, as well as neonatal admission data were recorded. Systemic inflammatory indicators were assessed. Receiver operating characteristic curves were used to evaluate the predictive value of the maternal systemic inflammatory indicators before delivery for congenital pneumonia. A prediction model for neonatal pneumonia was constructed by combining systemic inflammatory indicators before delivery with logistic regression. The association between this prediction model and the prognosis of neonatal congenital pneumonia was examined. Results: Maternal systemic inflammatory indicators before delivery as predictive markers for congenital pneumonia and the regression model jointly constructed by NLR, MLR, SII, SIRI, and PIV before delivery predicted the occurrence of congenital pneumonia better. Maternal systemic inflammatory indicators correlated with the severity of congenital pneumonia in neonates. Conclusions: Maternal systemic inflammatory indicators before delivery have predictive value for congenital pneumonia in neonates, aiding early identification and treatment. [ABSTRACT FROM AUTHOR]

Background PREDICT is a web-based tool for forecasting breast cancer outcomes. PREDICT version 3.0 was recently released. This study aimed to validate this tool for a large population in mainland China and compare v3.0 with v2.2. Methods Women who underwent surgery for nonmetastatic primary invasive breast cancer between 2010 and 2020 from the First Affiliated Hospital of Wenzhou Medical University were selected. Predicted and observed 5-year overall survival (OS) for both v3.0 and v2.2 were compared. Discrimination was compared using receiver-operator curves and DeLong test. Calibration was evaluated using calibration plots and chi-squared test. A difference greater than 5% was deemed clinically relevant. Results A total of 5424 patients were included, with median follow-up time of 58 months (IQR 38-89 months). Compared to v2.2, v3.0 did not show improved discriminatory accuracy for 5-year OS (AUC: 0.756 vs 0.771), same as ER-positive and ER-negative patients. However, calibration was significantly improved in v3.0, with predicted 5-year OS deviated from observed by −2.0% for the entire cohort, −2.9% for ER-positive and −0.0% for ER-negative patients, compared to −7.3%, −4.7% and −13.7% in v2.2. In v3.0, 5-year OS was underestimated by 9.0% for patients older than 75 years, and 5.8% for patients with micrometastases. Patients with distant metastases postdiagnosis was overestimated by 10.6%. Conclusions PREDICT v3.0 reliably predicts 5-year OS for the majority of Chinese patients with breast cancer. PREDICT v3.0 significantly improved the predictive accuracy for ER-negative groups. Furthermore, caution is advised when interpreting 5-year OS for patients aged over 70, those with micrometastases or metastases postdiagnosis. [ABSTRACT FROM AUTHOR]

Background Few studies have assessed the comprehensive associations among comorbid diseases in elderly patients with nasopharyngeal carcinoma (NPC). This study sought to identify potential comorbidity patterns and explore the relationship of comorbidity patterns with the mortality risk in elderly patients with NPC. Methods A total of 452 elderly patients with NPC were enrolled in the study. The network analysis and latent class analysis were applied to mine comorbidity patterns. Propensity score matching was used for adjusting confounders. A restricted cubic spline model was used to analyze the nonlinear association between age and the risk of all-cause mortality. Results We identified 2 comorbidity patterns, metabolic disease-related comorbidity (MDRC) and organ disease-related comorbidity (ODRC) in elderly patients with NPC. Patients in MDRC showed a significantly higher risk of all-cause mortality (71.41% vs 87.97%, HR 1.819 [95% CI, 1.106-2.994], P  = .031) and locoregional relapse (68.73% vs 80.88%, HR 1.689 [95% CI, 1.055-2.704], P  = .042). Moreover, in patients with MDRC pattern, we observed an intriguing inverted S-shaped relationship between age and all-cause mortality among patients aged 68 years and older. The risk of mortality up perpetually with age increasing in ODRC group, specifically within the age range of 68-77 years (HR 4.371, 1.958-9.757). Conclusion Our study shed light on the potential comorbidity patterns in elderly patients with NPC, thereby providing valuable insights into the development of comprehensive health management strategies for this specific population. [ABSTRACT FROM AUTHOR]

Background: Deviant behaviors are common during adolescence. Despite the diversity of juvenile delinquency, the patterns of deviant behaviors remain unclear in ethnic minorities. The present study aimed to evaluate the latent heterogeneity of deviant behaviors and associated factors in ethnic minority Yi adolescents. Methods: The present study recruited a large sample of 1931 ethnic minority Yi adolescents (53.4% females, mean age = 14.7 years, SD 1.10) in five secondary schools in 2022 in Sichuan, China. The participants completed measures on 13 deviant behaviors and demographic characteristics, attitudinal self-control, and psychological distress. Sample heterogeneity of deviant behaviors was analyzed via latent class analysis using class as the cluster variable. Results: The data supported three latent classes with measurement invariance by sex. 68.2%, 28.0%, and 3.8% of the sample were in the Normative, Borderline, and Deviant class, with minimal, occasional, and extensive deviant behaviors, respectively. The Deviant class was more prevalent in males (6.5%) than females (1.6%). There were significant class differences in domestic violence, school belonging, self-control, anxiety, and depressive symptoms. Males, domestic violence, low school belonging, and impaired self-control significantly predicted higher odds of the Deviant and Borderline classes compared to the normative class. Conclusion: This study provided the first results on three latent classes of deviant behaviors with distinct profiles in ethnic minority adolescents in rural China. These results have practical implications to formulate targeted interventions to improve the psycho-behavioral functioning of the at-risk adolescents in ethnic minorities. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to develop and validate a nomogram for predicting the risk of an acute complicated course in pediatric patients with Acute Hematogenous Osteomyelitis (AHO). Methods: A predictive model was developed based on a dataset of 82 pediatric AHO patients. Clinical data, imaging findings, and laboratory results were systematically collected for all patients. Subsequently, biomarker indices were calculated based on the laboratory results to facilitate a comprehensive evaluation. Univariate and multivariate logistic regression analyses were conducted to identify factors influencing early adverse outcomes in AHO. A nomogram model was constructed based on independent factors and validated internally through bootstrap methods. The discriminative ability, calibration, and clinical utility of the nomogram model were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), respectively. The developed nomogram model was compared with previously published A-score and Gouveia scoring systems. Results: Logistic regression analysis identified delayed source control, suppurative arthritis, albumin on admission, and platelet to lymphocyte ratio (PLR) as independent predictors of early adverse outcomes in pediatric AHO patients. The logistic regression model was formulated as: Log(P) = 7. 667–1.752 × delayed source control − 1.956 × suppurative arthritis − 0.154 × albumin on admission + 0.009 × PLR. The nomogram's AUC obtained through Bootstrap validation was 0.829 (95% CI: 0.740–0.918). Calibration plots showed good agreement between predictions and observations. Decision curve analysis demonstrated that the model achieved net benefits across all threshold probabilities. The predictive efficacy of our nomogram model for acute complicated course in pediatric AHO patients surpassed that of the A-score and Gouveia scores. Conclusions: A predictive model for the acute complicated course of pediatric AHO was established based on four variables: delayed source control, suppurative arthritis, albumin on admission, and PLR. This model is practical, easy to use for clinicians, and can aid in guiding clinical treatment decisions. [ABSTRACT FROM AUTHOR]

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area. [ABSTRACT FROM AUTHOR]

Background: The global COVID-19 pandemic has resulted in over seven million deaths, and IFI can further complicate the clinical course of COVID-19. Coinfection of COVID-19 and IFI (secondary IFI) pose significant threats not only to healthcare systems but also to patient lives. After the control measures for COVID-19 were lifted in China, we observed a substantial number of ICU patients developing COVID-19-associated IFI. This creates an urgent need for predictive assessment of COVID-19 patients in the ICU environment for early detection of suspected fungal infection cases. Methods: This study is a single-center, retrospective research endeavor. We conducted a case-control study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. The cases consisted of patients who developed any secondary IFI during their ICU stay at Jilin University China-Japan Union Hospital in Changchun, Jilin Province, China, from December 1st, 2022, to August 31st, 2023. The control group consisted of SARS-CoV-2 positive patients without secondary IFI. Descriptive and comparative analyses were performed, and a logistic regression prediction model for secondary IFI in COVID-19 patients was established. Additionally, we observed an increased incidence of COVID-19-associated pulmonary aspergillosis (CAPA) during this pandemic. Therefore, we conducted a univariate subgroup analysis on top of IFI, using non-CAPA patients as the control subgroup. Results: From multivariate analysis, the prediction model identified 6 factors that are significantly associated with IFI, including the use of broad-spectrum antibiotics for more than 2 weeks (aOR=4.14, 95% CI 2.03-8.67), fever (aOR=2.3, 95%CI 1.16-4.55), elevated log IL-6 levels (aOR=1.22, 95% CI 1.04-1.43) and prone position ventilation (aOR=2.38, 95%CI 1.15-4.97) as independent risk factors for COVID-19 secondary IFI. High BMI (BMI = 28 kg/m²) (aOR=0.85, 95% CI 0.75-0.94) and the use of COVID-19 immunoglobulin (aOR=0.45, 95% CI 0.2-0.97) were identified as independent protective factors against COVID-19 secondary IFI. The Receiver Operating Curve (ROC) area under the curve (AUC) of this model was 0.81, indicating good classification. Conclusion: We recommend paying special attention for the occurrence of secondary IFI in COVID-19 patients with low BMI (BMI < 28 kg/m²), elevated log IL-6 levels and fever. Additionally, during the treatment of COVID-19 patients, we emphasize the importance of minimizing the duration of broad-spectrum antibiotic use and highlight the potential of immunoglobulin application in reducing the incidence of IFI. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common fatal neurodegenerative disease among the elderly worldwide, characterized by memory and cognitive impairment. The identification of biomarkers for AD is crucial and urgent to facilitate the diagnosis and intervention. The aim of this study was to evaluate the diagnostic value of acyl-Coenzyme A thioesterase 7 (ACOT7) as a serum biomarker for the prediction of AD. In our study, we observed a significant increase in ACOT7 expression in patients (n = 366) with AD and animal (n = 8-12) models of AD, compared to the control group. A significant negative correlation was found between ACOT7 levels and Mini-Mental State Examination (MMSE) scores (r = -0.85; p < 0.001). The analysis of the receiver operating characteristic curve (ROC) showed that the area under the curve (AUC) for ACOT7 was 0.83 (95% confidence intervals: 0.80-0.86). The optimal cut-off point of 62.5 pg./mL was selected with the highest sum of sensitivity and specificity. The diagnostic accuracy of serum ACOT7 for AD was 77% (95% confidence intervals: 72-82%), with a sensitivity of 80% (95% confidence intervals: 75-84%) and a specificity of 74% (95% confidence intervals: 69-79%). Moreover, the ROC analysis showed that the AUC of Aβ42/40 ratio is 0.70, and the diagnostic accuracy was 72%, with 69% sensitivity and 76% specificity. Compared with the AD traditional marker Aβ42/40 ratio, ACOT7 shows better superiority as a new serum candidate biomarker of AD. By suppressing the ACOT7 gene, our study provides evidence of the involvement of ACOT7 in the metabolism of amyloid precursor protein (APP), resulting in alterations in the expression levels of Aβ42, BACE1 and βCTF. ACOT7 has the ability to modulate the amyloidogenic pathway of APP metabolism, while it does not have an impact on the non-amyloidogenic pathway. In conclusion, the findings of our study suggest that serum ACOT7 may serve as a promising and non-invasive biomarker for AD. [ABSTRACT FROM AUTHOR]

Accurate prediction of significant wave height is crucial for ocean engineering. Traditional time series prediction models fail to achieve satisfactory results due to the non-stationarity of significant wave height. Decomposition algorithms are adopted to address the problem of non-stationarity, but the traditional direct decomposition method exists information leakage. In this study, a hybrid VMDLSTM- rolling model is proposed for non-stationary wave height prediction. In this model, time series are generated by a rolling method, after which each time series is decomposed, trained and predicted, then the predictions of each time series are combined to generate the final prediction of significant wave height. The performance of the LSTM model, the VMD-LSTM-direct model and the VMD-LSTM-rolling model are compared in terms of multi-step prediction. It is found that the error of the VMD-LSTM-direct model and the VMD-LSTM-rolling model is lower than that of the LSTM model. Due to the decomposition of the testing set, the VMD-LSTM-direct model has a slightly higher accuracy than the VMD-LSTM-rolling model. However, given the issue of information leakage, the accuracy of the VMD-LSTM-direct model is considered false. Thus, it has been proved that the VMD-LSTM-rolling model exhibits superiority in predicting significant wave height and can be applied in practice. [ABSTRACT FROM AUTHOR]

Objectives: To investigate risk factors for severe maternal morbidity (SMM) in pregnant women with hypertensive disorders of pregnancy (HDP) and to develop a risk prediction model. Methods: A prospective observational cohort study was conducted among pregnant women who were hospitalized for hypertensive disorders of pregnancy (HDP) between January 2016 and December 2020 in Fujian College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Province, China (a training set), and a risk predictive model was constructed. Pregnant women with HDP who were hospitalized between January 2021 and December 2021 were selected as a validation set. Concordance index (C-index) and calibration curves were used to test predictive model discrimination and calibration. Results: We included 970 pregnant women (790 in the training set and 180 in the validation set). Least absolute shrinkage and selection operator regression was used to screen for nine related variables such as intra-uterine growth retardation (IUGR), diastolic blood pressure (DBP) and systolic blood pressure (SBP) at suspected diagnosis, total bilirubin, albumin (ALB), uric acid, total cholesterol, serum magnesium, and suspected gestational age. SBP at suspected diagnosis (OR =1.22, 95%CI:1.08-1.42) and total cholesterol (OR = 1.78, 95% CI:1.17-2.80) were independent risk factors of severe maternal morbidity in pregnant women with HDP. A nomogram was constructed, and internal validation of the nomogram model was done using the bootstrap self-sampling method. C-index in the training and the validation set was 0.798 and 0.909, respectively. Conclusion: Our prediction model can be used to determine gestational hypertension severity in pregnant women. [ABSTRACT FROM AUTHOR]

Background: Malignant mesothelioma (MM) is an exceedingly rare tumor with poor prognosis due to the limited availability of effective treatment. Immunotherapy has emerged as a novel treatment approach for MM, but less than 40% of the patients benefit from it. Thus, it is necessary to identify accurate and effective biomarkers that can predict the overall survival (OS) and immunotherapy efficacy for MM. Methods: DNA sequencing was used to identify the genomic landscape based on the data from 86 Chinese patients. T cell receptor (TCR) sequencing was used to characterize MM TCR repertoires of 28 patients between October 2016 and April 2023. Results: Patients with TP53, NF2, or CDKN2A variants at the genomic level, as well as those exhibiting lower Shannon index (<6.637), lower evenness (<0.028), or higher clonality (≥0.194) according to baseline tumor tissue TCR indexes, demonstrated poorer OS. Furthermore, patients with TP53, CDKN2A, or CDKN2B variants and those with a lower evenness (<0.030) in baseline tumor tissue showed worse immunotherapy efficacy. The present study is the first to identify five special TCR Vβ‐Jβ rearrangements associated with MM immunotherapy efficacy. Conclusions: The present study reported the largest‐scale genomic landscape and TCR repertoire of MM in Chinese patients and identified genomic and TCR biomarkers for the prognosis and immunotherapy efficacy in MM. The study results might provide new insights for prospective MM trials using specific genes, TCR indexes, and TCR clones as biomarkers and offer a reference for future antitumor drugs based on TCR‐specific clones. [ABSTRACT FROM AUTHOR]

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

Introduction: Graft failure is a major challenge in femtosecond laser-assisted penetrating keratoplasty (Fs-PKP). This study focuses on the development and validation of a clinical predictive model aimed at identifying the risk of graft failure in individuals undergoing Fs-PKP in China, offering a tailored approach to improve surgical outcomes. Methods: This retrospective cohort study at Nanjing First Hospital involved 238 patients and followed the TRIPOD statement. The cohort was divided into a training set (n = 166) and a validation set (n = 72) in a 7:3 ratio. It analyzed 23 predictor variables related to recipient, donor, and surgical factors, defining graft failure as "visually significant and irreversible corneal stromal edema, haze, or scarring." A comprehensive nomogram was created using univariate and multivariate Cox regression analyses and assessed by concordance index (C-index), time-dependent receiver operating characteristics (ROC) curve, calibration plots, and decision curve analysis (DCA). Results: Five critical risk factors were identified: recipients' history of systemic autoimmune disorders, ocular trauma, prior penetrating keratoplasty (PKP) history, donors' diabetes history, and the endothelial cell density of the donor cornea. The nomogram showed a C-index of 0.72 (95% CI 0.65–0.79) in the training group and 0.66 (95% CI 0.55–0.76) in the validation group, indicating robust predictive accuracy. Time-dependent ROC curves, calibration plots, and DCA consistently validated the model's reliability, predictive power, and clinical utility across both training and validation cohorts. Conclusions: Our study developed and validated a model incorporating five key factors, enhancing preoperative prediction and management for Chinese patients with Fs-PKP graft failure. [ABSTRACT FROM AUTHOR]

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China's photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNNXGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods. [ABSTRACT FROM AUTHOR]

Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment. A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model. The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%. Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions. [ABSTRACT FROM AUTHOR]

Background: Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy. Methods: From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. Results: A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for overall PLGC (95% confidence interval (CI) 0.71–0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82–0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone. In addition, AITonguequiry has superior performance compared to existing PLGC screening methodologies, with the AUC value enhancing 45% in terms of PLGC screening (0.74 vs. 0.51, p < 0.05) and 52% in terms of high-risk PLGC screening (0.82 vs. 0.54, p < 0.05). In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC. Conclusion: Our AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden. [ABSTRACT FROM AUTHOR]

Introduction: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 + 6 weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia. Methods: This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation. Results: We ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively). Conclusions: Our models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes. [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]

Introduction: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet. Material and methods: The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs. Results: The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714–0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440–0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693–0.949). Conclusion: The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy. [ABSTRACT FROM AUTHOR]

It has been widely accepted that China is one of the biggest natural gas consumers. Related to the imports of LNG, China stands in a very uncomfortable situation. Most domestic gas reservoirs fall within deep water drive gas reservoirs inordinately, which has entered the production depletion stage. Accurate estimation of SEC recoverable reserves of deep water drive gas reservoirs is of great significance for gas consumption planning and peak shaving. The existing calculation methods of recoverable reserves mainly consist of static methods and dynamic methods. In the early stage of exploration and development, the volumetric method has often been utilized to calculate the recoverable reserves. With the continuous development of gas reservoirs, the main methods for evaluation are dynamic methods, including the successive subtraction of production method, water drive curve method, prediction model method, attenuation curve method, improved virtual curve method, and material balance method for deep gas reservoirs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the present study was to identify predictors of severe white matter hyperintensity (WMH) with obesity (SWO), and to build a prediction model for screening obese people with severe WMH without Nuclear Magnetic Resonance Imaging (MRI) examination. Patients subjects and methods: From September 2020 to October 2021, 650 patients with WMH were recruited consecutively. The subjects were divided into two groups, SWO group and non-SWO group. Univariate and Logistic regression analysis were was applied to explore the potential predictors of SWO. The Youden index method was adopted to determine the best cut-off value in the establishment of the prediction model of SWO. Each parameter had two options, low and high. The score table of the prediction model and nomogram based on the logistic regression were constructed. Of the 650 subjects, 487 subjects (75%) were randomly assigned to the training group and 163 subjects (25%) to the validation group. By resampling the area under the curve (AUC) of the subject's operating characteristics and calibration curves 1,000 times, nomogram performance was verified. A decision curve analysis (DCA) was used to evaluate the nomogram's clinical usefulness. By resampling the area under the curve (AUC) of the subject's operating characteristics and calibration curves 1,000 times, nomogram performance was verified. A decision curve analysis (DCA) was used to evaluate the nomogram's clinical usefulness. Results: Logistic regression demonstrated that hypertension, uric acid (UA), complement 3 (C3) and Interleukin 8 (IL-8) were independent risk factors for SWO. Hypertension, UA, C3, IL-8, folic acid (FA), fasting C-peptide (FCP) and eosinophil could be used to predict the occurrence of SWO in the prediction models, with a good diagnostic performance, Areas Under Curves (AUC) of Total score was 0.823 (95% CI: 0.760--0.885, p < 0.001), sensitivity of 60.0%, specificity of 91.4%. In the development group, the nomogram's AUC (C statistic) was 0.829 (95% CI: 0.760--0.899), while in the validation group, it was 0.835 (95% CI: 0.696, 0.975). In both the development and validation groups, the calibration curves following 1,000 bootstraps showed a satisfactory fit between the observed and predicted probabilities. DCA showed that the nomogram had great clinical utility. Conclusion: Hypertension, UA, C3, IL-8, FA, FCP and eosinophil models had the potential to predict the incidence of SWO. When the total score of the model exceeded 9 points, the risk of SWO would increase significantly, and the nomogram enabled visualization of the patient's WMH risk. The application prospect of our models mainly lied in the convenient screening of SWO without MRI examination in order to detect SWO and control the WMH hazards early. [ABSTRACT FROM AUTHOR]

Background: Patients diagnosed with advanced stage cancer face an elevated risk of suicide. We aimed to develop a suicidal ideation (SI) risk prediction model in patients with advanced cancer for early warning of their SI and facilitate suicide prevention in this population. Patients and Methods: We consecutively enrolled patients with multiple types of advanced cancers from 10 cancer institutes in China from August 2019 to December 2020. Demographic characteristics, clinicopathological data, and clinical treatment history were extracted from medical records. Symptom burden, psychological status, and SI were assessed using the MD Anderson Symptom Inventory (MDASI), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire‐9 (PHQ‐9), respectively. A multivariable logistic regression model was employed to establish the model structure. Results: In total, 2814 participants were included in the final analysis. Nine predictors including age, sex, number of household members, history of previous chemotherapy, history of previous surgery, MDASI score, HADS‐A score, HADS‐D score, and life satisfaction were retained in the final SI prediction model. The model achieved an area under the curve (AUC) of 0.85 (95% confidential interval: 0.82–0.87), with AUCs ranging from 0.75 to 0.95 across 10 hospitals and higher than 0.83 for all cancer types. Conclusion: This study built an easy‐to‐use, good‐performance predictive model for SI. Implementation of this model could facilitate the incorporation of psychosocial support for suicide prevention into the standard care of patients with advanced cancer. [ABSTRACT FROM AUTHOR]

This paper aims to address the problems of safety and durability in China's ballastless track structures, particularly the lack of accurate analysis and methods for predicting the reliability of the new type of prefabricated track structure during the design phase. We propose a reliability prediction method for a new prefabricated track structure, the modular assembled track structure with built-in position retention. By adopting the fuzzy Failure Modes, Effects, and Criticality Analysis (fuzzy FMECA) method, a comprehensive assessment of fault severity, fault occurrence probability, and fault detection difficulty is conducted on the CRTS II slab track structure and the modular assembled track structure with built-in position retention. Consequently, a fault mode hazard assessment model for the new prefabricated track structure is constructed. Based on the assessment model and using a similar product method, a reliability prediction model for the new prefabricated track structure is established, and reliability prediction for the track structure is conducted. The research results indicate that the modular assembled track structure with built-in position retention has lower hazard levels and higher reliability compared to the CRTS II slab track structure. This study provides a scientific basis for the design optimization of new prefabricated track structures, helping to improve their safety and reliability, reduce operating and maintenance costs, and thereby promote the green and low-carbon development of the railway. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have developed or validated prediction models aimed at estimating the likelihood of amputation in diabetic foot (DF) patients. However, the quality and applicability of these models in clinical practice and future research remain uncertain. This study conducts a systematic review and assessment of the risk of bias and applicability of amputation prediction models among individuals with DF. Methods: A comprehensive search was conducted across multiple databases, including PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), and Weipu (VIP) from their inception to December 24, 2023. Two investigators independently screened the literature and extracted data using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability. Results: A total of 20 studies were included in this analysis, comprising 17 development studies and three validation studies, encompassing 20 prediction models and 11 classification systems. The incidence of amputation in patients with DF ranged from 5.9 to 58.5%. Machine learning-based methods were employed in more than half of the studies. The reported area under the curve (AUC) varied from 0.560 to 0.939. Independent predictors consistently identified by multivariate models included age, gender, HbA1c, hemoglobin, white blood cell count, low-density lipoprotein cholesterol, diabetes duration, and Wagner's Classification. All studies were found to exhibit a high risk of bias, primarily attributed to inadequate handling of outcome events and missing data, lack of model performance assessment, and overfitting. Conclusions: The assessment using PROBAST revealed a notable risk of bias in the existing prediction models for amputation in patients with DF. It is imperative for future studies to concentrate on enhancing the robustness of current prediction models or constructing new models with stringent methodologies. [ABSTRACT FROM AUTHOR]

With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)- based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations. [ABSTRACT FROM AUTHOR]

During the restoration of iron cultural relics, the removal of rust from these artifacts is necessary. However, this rust removal process may lead to inconsistent local color on the iron relics. To address this, mending materials are applied to treat the surface, ensuring consistent local color. In the surface treatment of iron cultural relics, a significant challenge lies in modulating the color of these mending materials. The corrosion products of Yuquan Iron Pagoda are mainly Fe3O4, γ-FeO(OH), α-FeO(OH) and α-Fe2O3, with contents of 13.1, 16.1, 40.2 and 30.6%, respectively. Due to their structural stability and suitable color characteristics, Fe3O4 and α-Fe2O3 are selected as the primary raw materials for the repair material. This study employs machine learning methods to predict the color of mending materials corresponding to varying contents of α-Fe2O3, Fe3O4, and epoxy resin. The Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boost Machine (LightGBM) algorithms are utilized to develop the model, and the predictive performance of these three algorithms is compared. XGBoost exhibits the best prediction performance, achieving a square correlation coefficient (R2) of 0.94238 and a mean absolute error (MAE) of 0.68485. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to analyze the most crucial raw material affecting the color of mending materials, which is identified as Fe3O4. The study illustrates the specific process of employing this model by applying it to the surface treatment of the Yuquan Iron Pagoda, demonstrating the practicality of the model. This model can be applied to assist in the surface treatment of other iron cultural relics. [ABSTRACT FROM AUTHOR]

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user's multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method. [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]

Introduction: It is appealing to accurately predict postoperative delirium (POD) before surgeries. In this study, it was hypothesized that a novel electroencephalogram-derived index, the delirium index (DELi), could extract latent information regarding the predisposing factors of POD preoperatively. This study was aimed at developing a concise model that incorporated this DELi score to predict the early POD of elderly patients after hip surgeries. Materials and Methods: Elderly patients scheduled for elective hip fracture surgeries were prospectively enrolled in a tertiary care hospital from November 2020 to June 2022. DELi scores and patient characteristics (age, sex, types of fracture and surgery, the time interval between fracture and surgery, cognitive function assessed using the Montreal Cognitive Assessment (MoCA), and frailty status assessed using the FRAIL scale) were collected preoperatively as candidate predictors. POD diagnosed using the confusion assessment method (CAM) was the outcome. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select predictors. Then, these predictors were entered into a backward logistical regression analysis to develop a prediction model. Discrimination, calibration and clinical utility were validated using the bootstrapping method. Results: All data (144 qualified patients of 170) were used for development. POD was observed in 71 patients (49.3%). Preoperative DELi scores predicted early POD (the area under the curve (AUC) =.786, 95% confidence interval (CI):.712,.860, in internal validation). A nomogram with MoCA, FRAIL scale and DELi score was constructed with excellent discrimination (AUC =.920, 95% CI:.876,.963, in internal validation), accredited calibration (P =.733, Hosmer‒Lemeshow test), and a wide range of threshold probabilities (5% to 95%). Conclusions: Preoperative DELi scores predicted the early POD of elderly patients after hip surgeries. A concise prediction model was developed and demonstrated excellent discrimination. [ABSTRACT FROM AUTHOR]

To analyze the equity of bed allocation of the department of stomatology in Chinese hospitals and predict the development in the next 5 years, so as to provide a scientific basis for promoting the development of oral health. Methods: Data on the beds of the department of stomatology in Chinese hospitals from 2017 to 2021 were obtained from the China Health Statistical Yearbook. The Gini coefficient, Lorenz curve, Theil index and agglomeration degree were used to analyze the equity of the bed allocation, and the grey prediction model GM(1,1) was used to predict the development from 2022 to 2026. Results: From 2017 to 2020, the Gini coefficient of bed allocation of the department of stomatology in Chinese hospitals was below 0.2 by population. From 2017 to 2021, the Gini coefficient of beds was above 0.6 by geography and between 0.2 and 0.3 by economy. The Theil index of beds ranged from 0.022 to 0.056 by population, from 0.532 to 0.564 by geography, and from 0.042 to 0.047 by economy. The inequity in the allocation by population was mainly from between regions, and the inequity in the allocation by geography and economy was mainly from within regions. Health resource agglomeration degree (HRAD) was greater than 2 in the eastern and central regions and less than 1 in the western region. HRAD/ population agglomeration degree (PAD) was greater than 1 in the northeast, eastern, and central regions and less than 1 in the western region. According to the prediction, the number of beds of the department of stomatology in Chinese hospitals will continue to increase, reaching 47,862.485 in 2026. Conclusion: The equity of bed allocation was better by population and economy than by geography. The equity of beds in the western region is insufficient equity by population and geography, and the equity of beds in the eastern region is insufficient equity by economy. [ABSTRACT FROM AUTHOR]

Reinforced-concrete shear walls stand as the primary construction method for urban residential structures in northern China. In alignment with national carbon neutrality goals for residential construction, this study developed a set of prediction models with which to estimate the building material carbon emissions of reinforced-concrete shear-wall urban residential buildings. Specifically, this study clarified the boundaries, content, and calculation method for carbon emissions in the stage of material production. Using consumption data for building materials from 20 reinforced-concrete shear-wall urban residential buildings in northern China, the study evaluated the composition and distribution of building material carbon emissions. Linear and ridge regression was performed to fit the coupling relationship between spatial design parameters and building material carbon emissions. Adopting two technical approaches of direct and indirect prediction, 10 carbon emission prediction models based on residential design parameters were established and validated. The results indicate that, although the indirect prediction models, based on concrete, steel, cement mortar, and the transparent envelope, had relatively low accuracy in estimating carbon emissions from cement mortar and the transparent envelope, they performed well overall. Additionally, the prediction performance of the four models was similar. In contrast, except for M1 and M3, the other direct prediction models, based on the number of building stories, number of basement levels, number of primary rooms on the standard floor or in the unit, and building width and depth, also had good fitting and prediction performance. These models effectively predicted the total building material carbon emissions in the phases of conceptual design, schematic design, preliminary design, and working drawing. Three prediction models could produce fast and effective data support for the low-carbon design of urban residential buildings. [ABSTRACT FROM AUTHOR]

Scientific and technological innovation and ecological progress are important forces for China's high-quality development. Nevertheless, the current body of research seldom explores the mutual dependence and evolutionary patterns of the two regional systems when considering both historical and future perspectives. This paper aims to enhance the current theoretical research framework by focusing on the coastal region of China. It analyzes and predicts the co-evolution and space–time distribution process of two systems by constructing a coupled model and a gray prediction model. The results show the following: (1) the coupling and coordinated development level of the two coastal systems has basically reached the stage of good coupling and coordinated development, and the level has increased, but the gap has gradually increased, and Jiangsu and Guangdong have reached the leading level; (2) the spatial distribution in coastal areas is uneven, showing a convex spatial pattern of "high in the middle and low at both ends"; (3) during the forecast period, the overall coupling and coordination level of the two systems will continue to improve, but individual cities still cannot jump out of the development state of slow development processes. With the aim of fostering the harmonious growth of both systems at both regional and national levels, this paper proposes practical recommendations concerning the establishment of a sustainable interaction mechanism, the evolution of the industrial structure towards ecological sustainability, and the economic contribution to scientific and technological innovation. Furthermore, this article serves as a valuable reference for advancing the sustainable development of other nations. [ABSTRACT FROM AUTHOR]

Background: To examine the risk factors associated with mortality in individuals suffering from acute diquat poisoning and to develop an effective prediction model using clinical data. Methods: A retrospective review was conducted on the clinical records of 107 individuals who were hospitalized for acute diquat poisoning at a tertiary hospital in Sichuan Province between January 2017 and September 30, 2023, and further categorized into survivor and nonsurvivor groups based on their mortality status within 30 days of poisoning. The patient's demographic information, symptoms within 24 h of admission, and details of the initial clinical ancillary examination, as well as the APACHE II score, were documented. The model was developed using backward stepwise logistic regression, and its performance was assessed using receiver operating characteristic curves, calibration curves, Brier scores, decision curve analysis curves, and bootstrap replicates for internal validation. Results: Multifactorial logistic regression analysis revealed that blood pressure (hypertension, OR 19.73, 95% CI 5.71–68.16; hypotension, OR 61.38, 95% CI 7.40–509.51), white blood count (OR 1.35, 95% CI 1.20–1.52), red cell distribution width-standard deviation (OR 1.22, 95% CI 1.08–1.38), and glomerular filtration rate (OR 0.96, 95% CI 0.94–0.97) were identified as independent risk factors for mortality in patients with diquat. Subsequently, a nomogram with an area under the curve of 0.97 (95% CI: 0.93–1) was developed. Internal bootstrap resampling (1000 repetitions) confirmed the model's adequate discriminatory power, with an area under the curve of 0.97. Decision curve analysis demonstrated greater net gains for the nomogram, while the clinical impact curves indicated greater predictive validity. Conclusion: The nomogram model developed in this study using available clinical data enhances the prediction of risk for DQ patients and has the potential to provide valuable clinical insights to guide patient treatment decisions. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese Medicine (TCM) defines constitutions which are relevant to corresponding diseases among people. As one of the common constitutions, Yin-deficiency constitution influences a number of Chinese population in the disease onset. Therefore, accurate Yin-deficiency constitution identification is significant for disease prevention and treatment. Methods: In this study, we collected participants with Yin-deficiency constitution and balanced constitution, separately. The least absolute shrinkage and selection operator (LASSO) and logistic regression were used to analyze genetic predictors. Four machine learning models for Yin-deficiency constitution classification with multiple combined genetic indicators were integrated to analyze and identify the optimal model and features. The Shapley Additive exPlanations (SHAP) interpretation was developed for model explanation. Results: The results showed that, NFKBIA, BCL2A1 and CCL4 were the most associated genetic indicators with Yin-deficiency constitution. Random forest with three genetic predictors including NFKBIA, BCL2A1 and CCL4 was the optimal model, area under curve (AUC): 0.937 (95% CI 0.844–1.000), sensitivity: 0.870, specificity: 0.900. The SHAP method provided an intuitive explanation of risk leading to individual predictions. Conclusion: We constructed a Yin-deficiency constitution classification model based on machine learning and explained it with the SHAP method, providing an objective Yin-deficiency constitution identification system in TCM and the guidance for clinicians. [ABSTRACT FROM AUTHOR]

The H shale gas block in the Yangtze plate of China has undergone multistage tectonic evolution, which has resulted in the development of faults and natural fractures that are multistage, multi-scale, multi-type, and multi-strike in nature. To precisely characterize the natural fractures, this study used the seismic attribute analysis method to conduct a multi-scale prediction and natural fracture modelling. Initially, manual fault interpretation and the conventional seismic attributes including variance attribute, curvature and edge detection are used to interpret large-to medium-scale faults. Subsequently, the ant-tracking attribute based on the divided-frequency seismic cubes is used to predict and quantitatively interpret the mid- and small-scale fractures; The third step is to quantitatively divide the ant-tracking attribute value into multiple scales and convert them linearly into fracture intensity. The fourth step is to establish a multi-scale fracture model. This approach has established a comprehensive prediction workflow, progressing from large-scale to small-scale analysis, and shifting from qualitative to quantitative assessment. Compared with traditional fracture prediction and modeling methods, this method not only enhances the accuracy, but also satisfies the engineering requirements of multi-scale modeling. By applying the method in the H shale gas block, we gained a microscopic understanding of the fractures, which are predominantly NW, NE, and nearly NS trending, with a minimal occurrence of nearly EW trending faults/fractures. This method can be effectively applied to characterize multiscale fractures in areas similar to the geological background of the H shale gas block. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults. Methods: This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators. Results: 3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75–0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73–0.79). Hosmer–Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties. Conclusions: The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults. [ABSTRACT FROM AUTHOR]

Objective: The objective of this scoping review was to investigate the effectiveness and limitations of risk prediction models for postpartum glucose intolerance in women with gestational diabetes mellitus (GDM). The aim was to provide valuable insights for healthcare professionals in the development of robust risk prediction models. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, EBSCO, Web of Science Core Collection, Ovid Full-Text Medical Journal Database, ProQuest, Elsevier ClinicalKey, China National Knowledge Infrastructure, China Biology Medicine, and WanFang Database, spanning from January 1990 to July 2023. To assess the quality of the included models, the Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed. Results: Fourteen relevant studies were identified and included in the final review, all focusing on model development. The discrimination ability of the included models ranged from 0.725 to 0.940, indicating satisfactory prediction accuracy. However, a notable limitation was that nine of these models (64.3%) did not provide clear guidelines on the selection of potential predictors. Furthermore, only six models (42.86%) underwent internal validation, with none undergoing external validation. A high risk of bias was observed across the included models. Logistic regression, Cox regression, and machine learning were the primary methods employed in the construction of these models. Conclusion: The risk prediction models included in this review demonstrated favorable prediction accuracy. However, due to variations in construction methodologies, direct comparison of their performance is challenging. These models exhibited certain shortcomings, such as inadequate handling of missing data and a lack of internal and external validation, resulting in a high risk of bias. Therefore, it is recommended that these models be updated and externally validated. The development of prospective, multi-center studies is encouraged to construct predictive models with low risk of bias and high clinical applicability, ultimately guiding evidence-based clinical practice. [ABSTRACT FROM AUTHOR]

In the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs. [ABSTRACT FROM AUTHOR]

To solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention. [ABSTRACT FROM AUTHOR]

To solve the problems of large prediction error, slow convergence speed, and poor generalisation ability of traditional models in predicting surface deformation of open-pit mine slopes, this paper proposes a new intelligent prediction model based on the Mayfly algorithm-optimised support vector machine (MA-SVM). In this method, the MA is used to optimise the SVM parameters to reduce the uncertainty of the model and avoid time-consuming parameter adjustment. To evaluate the proposed prediction model, real-world deformation data of the north slope of the Anjialing open-pit mine in Pingshuo city, China, are collected using the microdeformation monitoring radar and used to investigate the deformation prediction performance of the proposed method. The results of the analysis demonstrate that the proposed method is able to accurately predict the deformation of the surface of the mine slope and outperforms three existing popular methods, including SVM, genetic algorithm (GA)-SVM, and particle swarm optimisation (PSO)-SVM). The mean absolute error (MAE) of the proposed MA⁃SVM is 2.52 % while 6.56 %, 4.95 %, and 5.16 % for the SVM, GA-SVM, and PSO-SVM, respectively; the root mean square error (RMSE) of the proposed MA⁃SVM is 10.21 % while 30.79 %, 17.38 %, and 22.54 % for the other three methods. Because the proposed MA⁃SVM model is able to predict slope deformation using actual monitoring data, it is of practical importance in real-world applications for early warning on landslides of mine slopes. [ABSTRACT FROM AUTHOR]

The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel on the forest surface is a critical challenge in forest fire management. Previous research on fine surface fuel moisture content has been mainly focused on coniferous forests in cold temperate zones, but there has been less attention given to understanding the fuel moisture dynamics in subtropical forests, which limits the development of regional forest fire warning models. Here, we consider the coupled influence of multiple meteorological, terrain, forest stand, and other characteristic factors on the fine dead fuel moisture content within the subtropical evergreen broadleaved forest region of southern China. The ability of five machine learning algorithms to predict the moisture content of fine dead fuel on the forest surface is assessed, and the key factors affecting the model accuracy are identified. Results show that when a single meteorological factor is used as a forecasting model, its forecasting accuracy is less than that of the combined model with multiple characteristic factors. However, the prediction accuracy of the model is improved after the addition of forest stand factors and terrain factors. The model prediction ability is the best for the combination of all feature factors including meteorology, forest stand, and terrain. The overall prediction accuracy of the model is ordered as follows: random forest > extreme gradient boosting > support vector machine > stepwise linear regression > k-nearest neighbor. Canopy density in forest stand factors, slope position and altitude in terrain factors, and average relative air humidity and light intensity in the previous 15 days are the key meteorological factors affecting the prediction accuracy of fuel moisture content. Our results provide scientific guidance and support for understanding the variability of forest surface fuel moisture content and improved regional forest fire warnings. [ABSTRACT FROM AUTHOR]

The prevailing research on adaptation primarily centers around the settlement of international students and immigrants in different cultural environments. However, there is a notable gap in understanding the intra-cultural adaptation process for individuals from postcolonial areas when relocating to their home country. The primary focus of the current study lies in constructing a predictive model that delineates the psychological adaptation experienced by Macau students studying in Mainland China. In total, two hundred and fifty-five Macau students completed a questionnaire which assessed variables falling into two categories: identity-related variables, such as language proficiency and identity, and intergroup-related variables, including intergroup contact and the quality of contact, and psychological adaptation. The findings from the present study revealed that identity and quality of contact continued to make significant contributions to psychological adaptation in intra-cultural environments as in inter-cultural environments, whereas language proficiency and intergroup contact were unrelated to psychological adaptation in intra-cultural adaptation. The present study extended the adaptation research by transporting hypotheses and findings from inter-cultural adaptation and testing their validity and applicability in postcolonial contexts. The findings also provided practical implications for Chinese education institutions and policy-makers. [ABSTRACT FROM AUTHOR]