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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to further reduce the probability of multiple networks being attacked, different types of network security situation prediction models have received widespread attention and in depth research from scholars both domestically and internationally. With the rapid development of situational awareness modeling technology, various novel technical solutions such as neural networks, time series, and support vector machines have been introduced into the prediction model of network security situations, deeply optimizing and improving the means and methods of situational prediction models, thereby further improving the accuracy of situational prediction models. This study reviews and sorts out the research history and development process of network security situation prediction technology, elaborates on the main principles and current development status of situation prediction models, analyzes the shortcomings and deficiencies of current technical solutions, and points out the future research directions of network security situation prediction model technology. [ABSTRACT FROM AUTHOR]

The temperature prediction of geothermal reservoirs at different depths is to determine the key parameters such as thermal energy storage,heat output capacity,and the sustainable utilization period of geothermal reservoirs. Taking the geothermal wells in the Qiabuqia area of Gonghe Basin as an example, this study proposes a temperature prediction model for heat reservoirs under different constraints based on particle swarm optimization (PSO) and long short-term memory network (LSTM). The prediction effect of this model is verified by comparing with those of the BP model and LSTM model. The results show that the RMSE value,MAPE value and MAD value in the prediction results of the model are the smallest compared with those in BP and LSTM models, and the minimum RMSE value is only 1. 192. The determination coefficient of the model is 0. 929,showing a good prediction effect. This indicates that this model could realize the prediction of reservoir temperature in geothermal system,which provides references for the efficient and long-term development of geothermal system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] This study was aimed to explore the mathematical relationship between soil detachment capacity and concentrated water flow hydraulic characteristics parameters in the typical slope orange orchard in the Three Gorges Reservoir Area, and to provide theoretical basis and data support for predicting soil erosion in slope orange orchard. [Methods] Through concentrated flow erosion tests, the soil detachment amount of a clear plowed sample plot and three green manure (Trifolium repens L, Poa annua L, and Vicia sepium L) covered plots under gradient hydrodynamic conditions of different slopes (10°～30°) and flow rates (18~126 L/min) combinations was analyzed. The quantitative relationships between soil detachment capacity of citrus orchard plots and hydrodynamic parameters (water shear force, water power), flow pattern parameters (Reynolds number, Froude number) and resistance parameters (Darcy-Weisbach resistance coefficient, Manning’s coefficient) were explored, and corresponding mathematical models were established. [Results] There was a significant correlation between the soil detachment capacity of green manure coverage and clean cultivation of citrus orchard and various hydraulic parameters. The soil detachment capacity was positively correlated with hydrodynamic and flow regime parameters in a power function (R2 was 0.86~0.99, NSE was 0.88~0.98, p

Copyright of Diamond & Abrasives Engineering is the property of Zhengzhou Research Institute for Abrasives & Grinding and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Diamond & Abrasives Engineering is the property of Zhengzhou Research Institute for Abrasives & Grinding and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To establish a depression prediction model for adolescents with Polycystic Ovary Syndrome (PCOS) and validate the model. Methods Patients' data were collected from the gynecological clinic of Affiliated Hospital of Zunyi Medical University according to the item pool of risk factors for depression in adolescents with PCOS. Data collected between October 2021 and September 2022. In this study, R software (version 4.2.1) was used to perform regression analysis by the least absolute shrinkage and selection operator (LASSO), so as to screen out strong risk factors related to depression in adolescents with PCOS. These risk factors were then incorporated into logistic regression to develop a depression warning model in adolescents with PCOS. The model has been visualized by nomogram and has been verified both internally and externally. The predicted effect of the model was evaluated through discrimination, specificity and sensitivity. Decision curve analysis was used to analyze the clinical effect of the model. Results The model was as follows: depression risk = 1/(1 + exp-(-4. 055 + 0.221 x sleep + 0.729 x hormonal contraceptive use + 0.920 x hirsutism + 0.079 x illness perception -0.058 x social support + 1.049 x (luteinizing hormone/follicle stimulating hormone) ≥2)). The area under the ROC curve for this model was 0.881. The optimal cut-off value on the ROC curve was 0.278, corresponding to a high specificity and sensitivity of 76.2% and 88.0%, respectively. The corrected area under the ROC curve obtained was 0.867. In addition, the result of decision curve analysis showed that the model could provide effective evidence support for clinical decision-making. The area under the ROC curve obtained from external validation was 0.871. Conclusion In this study, an early warning model of depression risk in adolescents with PCOS was constructed. It can effectively identify people at high risk of depression in adolescents with PCOS at an early stage, thus providing a theoretical basis for the implementation of comprehensive and effective risk prevention measures. [ABSTRACT FROM AUTHOR]

Objective To explore and compare the application value of post-stroke fatigue (PSF) risk prediction nomogram and Web calculator program in post-stroke fatigue risk prediction in convalescent stroke patients. Methods The patients with acute stroke admitted to the Department of Encephalopathy of a tertiary hospital from February 2023 to August 2023 were prospectively selected, and the nomogram and Web calculator program were used to predict the risk probability of PSF in convalescent stage. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical utility of the two models, respectively. Results A total of 282 patients were included, of which 128 had PSF, and the incidence of PSF was 46.89%. The AUC of the nomogram and Web calculator were 0.687 (95% CI: 0.624-0.749) and 0.743 (95% CI: 0.683-0.803), respectively. The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram (χ²=8.357, P=0.213) and Web calculator program (χ²=4.467, P=0.614) were well fitted in stroke patients. The clinical decision curve analysis showed that the nomogram and Web calculator program had clinical benefits in the range of threshold probability of 0.35-0.81 and 0.33-0.88, respectively. Conclusion The nomogram and Web calculator program can effectively predict the PSF risk of stroke convalescent patients, and the Web calculator program is superior to the nomogram. However, the future Web calculator program model still needs to be further updated to balance simplicity and accuracy, thereby improving its clinical applicability. [ABSTRACT FROM AUTHOR]

Background and objective Cystic lung cancer, a special type of lung cancer, has been paid more and more attention. The most common pathological type of cystic lung cancer is adenocarcinoma. The invasiveness of cystic lung adenocarcinoma is vital for the selection of clinical treatment and prognosis. The aim of this study is to analyze the multiple clinical features of cystic lung adenocarcinoma, explore the independent risk factors of its invasiveness, and establish a risk prediction model. Methods A total of 129 cases of cystic lung adenocarcinoma admitted to the Department of Thoracic Surgery of the First Affiliated Hospital of Nanjing Medical University from January 2021 to July 2022 were retrospectively analyzed and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA)] and invasive group [invasive adenocarcinoma (IAC)] according to pathological findings. There were 47 cases in the pre-invasive group, including 19 males and 28 females, with an average age of (51.23±14.96) years. There were 82 cases in the invasive group, including 60 males and 22 females, with an average age of (61.27±11.74) years. Multiple clinical features of the two groups were collected, including baseline data, imaging data and tumor markers. Univariate analysis, LASSO regression and multivariate Logistic regression analysis were used to screen out the independent risk factors of the invasiveness of cystic lung adenocarcinoma, and the risk prediction model was established. Results In univariate analysis, age, gender, smoking history, history of emphysema, neuron-specific enolase (NSE), number of cystic airspaces, lesion diameter, cystic cavity diameter, nodule diameter, solid components diameter, cyst wall nodule, smoothness of cyst wall, shape of cystic airspace, lobulation, short burr sign, pleural retraction, vascular penetration and bronchial penetration were statistically different between the pre-invasive group and invasive groups (P<0.05). The above variables were processed by LASSO regression dimensionality reduction and screened as follows: age, gender, smoking history, NSE, number of cystic airspaces, lesion diameter, cystic cavity diameter, cyst wall nodule, smoothness of cyst wall and lobulation. Then the above variables were included in multivariate Logistic regression analysis. Cyst wall nodule (P=0.035) and lobulation (P=0.001) were found to be independent risk factors for the invasiveness of cystic lung adenocarcinoma (P<0.05). The prediction model was established as follows: P=e^x/(1+e^x), x=-7.927+1.476* cyst wall nodule+2.407* lobulation, and area under the curve (AUC) was 0.950. Conclusion Cyst wall nodule and lobulation are independent risk factors for the invasiveness of cystic lung adenocarcinoma, which have certain guiding significance for the prediction of the invasiveness of cystic lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To investigate the prevalence and influencing factors of abnormal bone mass in postmenopausal women of Dong and Miao ethnic groups in Guizhou Province, and to establish a nomogram prediction model and verify it, so as to provide theoretical basis for the prevention of abnormal bone mass in ethnic minority postmenopausal women in Guizhou Province. Methods This study was a cross-sectional study. In total 3 208 postmenopausal women of Dong and Miao ethnic groups were selected from the cohort study database of natural population of ethnic minorities in southwest China, and ultrasonic bone densitometer was used to measure calcaneal bone mineral density (BMD). The concentrations of cadmium, cobalt, copper, chromium, iron, mercury, manganese, molybdenum, nickel, lead, strontium, vanadium, and zinc in urine were determined by inductively coupled plasma mass spectrometer, and the relationship between urine metal, demographic characteristics, reproductive characteristics, ethnic diet, and BMD was analyzed by binary logistic regression, and the risk prediction model of bone mass abnormality was established. Results A total of 3 208 people were included in the study, and a total of 1 520 people with abnormal bone mass were detected. The detection rate of abnormal bone mass was 47.38%. Based on age (OR=1.098, 95%CI: 1.083-1.113), history of bone fracture (OR=0.560, 95%CI: 0.395-0.794), age of menopause (OR=0.965, 95%CI: 0.949-0.982), number of live births (OR=0.900, 95%CI: 0.818-0.990), use of contraceptive (OR=0.708, 95%CI: 0.555-0.901), total physical activity (OR=0.989, 95%CI: 0.984-0.994), urinary cadmium (OR=1.060, 95%CI:1.009-1.113), and urinary chromium (OR=1.004, 95%CI: 1.001-1.008) were independent influencing factors of abnormal bone mass. To establish a predictive model Logit (P) for predicting the risk of abnormal bone mass in postmenopausal women: Logit (") =- 2.170+0.093 * (age)-0.580 * (history of bone fracture)-0.035 * (menopausal age)-0.105 * (number of live births)-0.346* (whether or not you have taken contraceptive)-0.011 * (total physical activity) + 0.058 * (urinary cadmium) + 0.004 * (urinary chromium). The area under the receiver operating characteristic curve of the model was 0.722. The nomogram was drawn and verified internally. The results showed that the calibration curve predicted by the model for abnormal bone mass was basically consistent with the actual curve, and the fitting effect was good. Conclusion Cadmium and chromium are independent risk factors of abnormal bone mass, indicating that metal exposure can affect the occurrence and development of abnormal bone mass. The prediction model and score diagram of bone mass abnormality have good predictive ability, which provides a reference for screening abnormal bone mass of minority postmenopausal women in Guizhou Province. [ABSTRACT FROM AUTHOR]

Objective To explore the risk factors of re-admission of schizophrenic patients within one year after discharge and to establish a risk prediction model of re-admission. Methods By using cluster sampling, 353 inpatients with schizophrenia in a large mental hospital from July to December 2020 were collected from the in-patient electronic medical record and dis¬charge follow-up system. Based on their demographic and clinical data, Logistic regression analysis was used to analyze the risk factors of re-admission within one year after discharge. Results Four predictive factors were included: course of disease ≤ 20 years (OA =2.373, 95%CI: 1.242-4.533), combination of antipsychotics (OA =2.07, 95%CI: 1.211-3.538), underlying disease (OA = 1.893, 95%CI: 1.111-3.226), and harmful behavior (OA=11.219, 95%CI: 5.544-22.701). The prediction model fitted well (x² = 3.444, P=0.903): the area under the receiver operating characteristic curve of the subjects was 0.805, the sensitivity was 82.6%, and the specificity was 65.6%. The results of Bootstrap re-sampling (n=1 000 times) showed that the model was stable. Conclusion The predictive model based on demographic and clinical data can be used as a tool to identify the schizophrenic patients at high risk of re-admission in the early stage. [ABSTRACT FROM AUTHOR]

A quantitative method of main components (total sugar, reducing sugar, total acid, ammonia nitrogen, anthocyanins, procyanidins, total phenols, flavonoids and polysaccharides) in Lycium ruthenicum Murr was developed using Fourier transform near infrared spectroscopy (FT-NIR) combined with chemometric analysis. Firstly, 11 pretreatment methods were compared for the original spectra, and the optimal pretreatment method of each component was selected. Then, the model results established by partial least squares (PLS), interval partial least squares (iPLS) and synergistic interval partial least squares (siPLS) algorithms were compared, and finally siPLS was adopted for modeling. The results showed that the correlation coefficient of calibration (Rc) and prediction (RP) of total acids, ammonia nitrogen, anthocyanins, procyanidins, total phenols and flavonoids were all greater than 0.9818, and the relative analysis error (RPD) was more than 2.5, indicating an excellent model performance. The modeling effect of total sugar, reducing sugar, and polysaccharide was also good. The established calibration models could be used for actual detection. The predicted values of the verification samples did not significantly differ from the measured values, with a prediction error of only ±0.1%. Therefore, the prediction model had high reliability. The prediction model established in this study can enable nondestructive, rapid and accurate main components in dried and fresh fruits of Lycium ruthenicum Murr. [ABSTRACT FROM AUTHOR]

To resolve the issues of thickness variation and thickness deviation at head and tail for ultra-high strength steels in continuous cold tandem rolling, a thickness deviation prediction model and a roll gap adjustment estimation model were established based on the metal mass flow equation firstly. In addition, a thickness control system was exploited, and optimal roll gap adjustment model was established. Finally, an optimization model for rolling speed and tension in the head and tail rolling processes of the strip steels was built. The first stand of continues cold tandem rolling unit was taken as the technical application objects to test the two typical ultra-high strength steels. The results demonstrate that the length of thickness deviation for the ultra-high strength steel AR4146E1 and DU6220A1 decreased from 70.3 m and 36.89 m to 16.85 m and 16.33 m respectively. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim To investigate the risk factors of restenosis after stent implantation in patients with vertebral artery stenosis and construct the prediction model of nomogram. Methods The clinical data of 272 patients with digital subtraction angiography (DSA) confirmed vertebral artery stenosis and stent implantation admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2023 were collected and retrospectively studied. According to the time of stent implantation, 272 patients were divided into modeling group (from January 2016 to December 2021, 220 cases) and verification group (from January 2022 to June 2023, 52 cases). In the modeling group, patients were divided into in-stent restenosis (ISR) group (50 cases) and non-ISR group (170 cases) according to CT angiography (CTA) or DSA results. Based on the independent risk factors of ISR analyzed by LASSO regression and multiple Logistic regression, a nomogram prediction model was constructed. The predictive ability of the prediction model was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical application value of the prediction model was evaluated by clinical decision curve. Results In the modeling group, ISR occurred in 50 of 220 patients undergoing vertebral artery stenting, and the incidence of ISR was 22. 72%. LASSO regression and multivariate Logistic regression analysis suggested that high ESSEN stroke risk score(ESRS), hyperhomocysteinemia (HHcy), moderate or higher stenosis of internal carotid artery and/ or contralateral vertebral artery, low density lipoprotein cholesterol (LDLC) ≥1.8 mmol/L, low postoperative peak systolic velocity (PSV) of vertebral artery and small stent diameter were risk factors for ISR. A nomogram prediction model was built based on the above six variable factors. The nomogram predicted that the AUC of ISR after vertebral artery stenting was 0.857 (95%CI: 0.799-0.915) in the modeling group, and the AUC of the verification group was 0.847 (95%CI: 0.732-0.961), which suggested that the model had a good degree of differentiation. Conclusion The prediction model established in this study can better predict the risk degree of ISR in patients with metal stent implantation of vertebral artery, which is helpful for clinicians to find high-risk patients with ISR and make timely intervention, so that patients can get greater benefits. [ABSTRACT FROM AUTHOR]

Background and objective The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters. Methods 389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI). Results In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs. Conclusion AI quantitative parameters were valuable in distinguishing the pathological types of GGNs. [ABSTRACT FROM AUTHOR]

The traditional monitoring model for concrete dam displacement often ignores analyses of pertinent information within the residual sequence, leading to suboptimal predictive performance for dam displacement. The purpose of this paper is to improve the accuracy of prediction models. To address this issue, a dual-layer displacement prediction model for concrete dam, which considers signal residual amendment, is proposed. Firstly, influential factors affecting dam displacement are selected based on the statistical model of traditional concrete dam displacement. Secondly, the sparrow search algorithm (SSA) is employed to determine the hyperparameters of extreme learning machines (ELM) in order to establish the single-layer prediction model SSA-ELM, which yields single-layer model displacement prediction values for dams. Next, the Variational Mode Decomposition (VMD) technique along with the Minimum Sample Entropy (SE) and correlation analysis is utilized, the residual sequence is decomposed and reconstructed. Finally, by using the SSA-ELM model to correct the reconstructed sequence and combining the corrected values with the single-layer model displacement prediction values, the dual-layer prediction model SSA-ELM-VMDr+ is constructed, allowing for the final displacement prediction values. The engineering validation of the model demonstrates that the dual-layer prediction model exhibits higher prediction accuracy and stronger generalization ability compared with other models. Moreover, the model effectively extracts valuable information from the residual and overcomes noise interference. This paper provides a new reference for dam safety monitoring, health service diagnosis and management operations. [ABSTRACT FROM AUTHOR]

In order to better obtain wind energy, this article constructs a maximum power tracking control system based on model predictive torque active disturbance rejection, which is used for direct-drive permanent magnet synchronous generator (PMSG) to achieve maximum output power tracking of the system. In order to improve the response performance of PMSG and enhance the anti-interference performance of the system, this article first designed model predictive torque control (MPTC) for the current loop, and proposed a parameter compensation improved active disturbance rejection control (ADRC) strategy for the speed loop. This method inherits the advantages of ADRC and MPTC, and could effectively handle uncertainties such as internal and external interference, modeling errors, and wind speed variations to achieve maximum power tracking performance at different wind speeds.From the simulation results, it can be seen that when the wind speed changes slightly, the static error of the proposed control strategy speed is almost zero, while the error of the traditional MPTC control strategy is 0.2 rad/s. When the wind speed changes significantly, the error is small, and the error of the traditional MPTC control strategy reaches 5rad/s. The research results show that the proposed control method has better dynamic response performance and anti-interference performance than traditional controllers in maximum power tracking. [ABSTRACT FROM AUTHOR]

Objective To explore the risk factors of early neurological deterioration (END) in patients with acute ischemie stroke (AIS) and establish a predictive model. Methods In total 622 AIS patients treated in Baiyun Hospital affiliated to Guizhou Medical University from January 2018 to December 2022 were selected and divided into END group (n=127) and non-END group (n=495) according to the diagnostic criteria of outcome events. The clinical baseline data, laboratory data, and imaging data of the patients were retrospectively collected. Based on the Logistic regression algorithm, risk factors for the development of END were investigated. The prediction model was constructed and nomogram was drawn, and the prediction value of the model was evaluated by discrimination, calibration, and clinical applicability. Results Multivariate Logistic regression analysis showed that heart rhythm, middle cerebral artery stenosis, carotid artery stenosis, admission NHISS score, fibrinogen, albumin, and brain natriuretic peptide (BNP) were independent risk factors for END. The prediction model was developed using Stata software, and a nomogram was created. Verification set analysis revealed that the predictive model predicted the risk of END with good discrimination, accuracy, and clinical applicability, with an AUC of 0.888 (95%CI: 0.821- 0.955), slope of the calibration curve which was close to 1, and net benefit of the clinical decision curve that exceeded the two reference lines. Conclusion The model constructed in this study has high degree of discrimination, calibration, and clinical applicability and has good comprehensive prediction efficiency, which has a certain clinical application value for the early prediction of END. [ABSTRACT FROM AUTHOR]

Objective To analyze the incidence of gestational hypertension in hospital delivery of obstetrics and gynecology from 2016 to 2022, understand its characteristics and changes, to explore the influencing factors of gestational hypertension, and to construct a predictive model of gestational hypertension, to provide basis for clinical prediction of gestational hyper- tension. Methods A total of 101 583 medical records of hospitalized parturient in a hospital of gynecology and obstetrics and children in Sichuan Province from 2016 to 2022 were collected. The test was used for univariate analysis, logistic regression analysis was used to establish the prediction model of influencing factors, and ROC curve was used to evaluate the dis- crimination of the prediction model. Results The incidence of gestational hypertension in hospital delivery of obstetrics and gynecology from 2016 to 2022 was 0.053. The results of logistic regression model showed that older age (OR=1.392, 95%CI: 1.243-1.559), local household registration (OR=0.760, 95%CI: 0.689-0.839), hypothyroidism (OR=1.443, 95%CI: 1.160- 1.794), intrahepatic cholestasis of pregnancy (ICP) (OR=1.874, 95%CI: 1.570-2.237), and diabetes mellitus (OR=1.367, 95% Cl: 1.230-1.519), multipara (OR=0.693, 95%CI: 0.623-0.770), multiple births (OR=2.443, 95%CI: 2.135-2.795), family his- tory of hypertension (OR=3.347, 95%CI: 2.666-4.203), and in vitro fertilization-embryo transfer (IVF-ET) assisted reproduction (OR=1.821, 95% CI: 1.594-2.080) were the influencing factors of gestational hypertension. The area under the ROC curve on structed by the predictive model of gestational hypertension was AUC=0.655 (95%CI: 0.645-0.665). Conclusion The risk factors of gestational hypertension are older age, hypothyroidism, ICP, diabetes, multiple births, family history of hypertension, and IVF-ET assisted reproduction. The protective factors of gestational hypertension are local household registration. The predctive model of gestationall hypertension established in this study has good discrimination ability and has centain predictive value. [ABSTRACT FROM AUTHOR]

Objective To investigate the risk factors related to the recurrence in patients with hemorrhagic stroke and to establish a corresponding risk prediction model to provide reference for reducing the recurrence rate of hemorrhagic stroke. Methods The patients with hemorrhagic stroke treated in the Department of Encephalopathy, the First Affiliated Hospital of Henan University of Chinese Medicine from January 1, 2020 to December 31, 2022 were included in the study. The clinical data of patients with recurrent hemorrhagic stroke were collected to explore the risk factors related to recurrence of hemorrhagic stroke. The original data were divided into modeling sequence and verification sequence by random grouping method, and the modeling sequence was analyzed by univariate and multivariate analyses and model construction. The C index, receiver operator characteristic (ROC) curve, standard curve, and clinical decision curve were used to evaluate the discrimination, accuracy, and clinical utility of the model, and the prediction accuracy of the model was verified by verification sequence. Results A total of 1 571 patients with hemorrhagic stroke were included, including 253 patients with recurrent hemorrhagic stroke and 1 318 patients with initial hemorrhagic stroke. The original data set was constructed and randomly divided into modeling sequence and verification sequence according to 7:3 ratio. Logistic regression analysis showed that course of disease (OR=3.548, 95%CI: 2.852-4.415), smoking (OR=1.499, 95%CI: 1.136-1.978), drinking (OR=3.330, 95%CI: 2.356-4.709), male ( OR = 1 1.424, 95%CI: 1.006-2.016), hyperlipidemia ( OR = 4.372 95%CI: 2.227-8.584), cerebral infarction (OR= 2.254, 95%CI: 1.294-3.928), high density lipoprotein cholesterol ( OR = 2.034, 95%CI: 1.220-3.389), prothrombin time (OR= 1.103, 95%CI: 1.004-1.211), and homocysteine OR = 1.03 95%CI: 1.014-1.047) were the influencing factors of recurrence in patients with hemorrhagic stroke, and a risk prediction model was established. The area under the ROC curve of the modeling sequence and the verification sequence was 0.834 (95%CI: 0.810-0.859) and 0.842 (95%CI: 0.804-0.880), respectively, and the standard curves were highly consistent. The results of Hosmer Lemeshow goodness-of-fit test were P = 0.9 and P = 0.736 respectively, and the thresholds of clinical decision curves were < 0.9 and <0.95, respectively, indicating that the model had high discrimination, calibration, and clinical effectiveness. Conclusion Course of disease, smoking, drinking, sex, hyperlipidemia, cerebral infarction, high density lipoprotein cholesterol, prothrombin time and homocysteine are independent risk factors of hemorrhagic stroke. The risk prediction model established in this study can timely identify high-risk patients with re- current hemorrhagic stroke and prevent the occurrence of adverse events. [ABSTRACT FROM AUTHOR]

Background and objective Invasive mucinous adenocarcinoma (IMA) was a rare and specific type of lung adenocarcinoma, which was often characterized by fewer lymphatic metastases. Therefore, it was difficult to evaluate the prognosis of these tumors based on the existing tumor-node-metastasis (TNM) staging. So, this study aimed to develop Nomograms to predict outcomes of patients with pathologic N0 in resected IMA. Methods According to the inclusion criteria and exclusion criteria, IMA patients with pathologic N0 in The Affiliated Lihuili Hospital of Ningbo University (training cohort, n=78) and Ningbo No.2 Hospital (validation cohort, n=66) were reviewed between July 2012 and May 2017. The prognostic value of the clinicopathological features in the training cohort was analyzed and prognostic prediction models were established, and the performances of models were evaluated. Finally, the validation cohort data was put in for external validation. Results Univariate analysis showed that pneumonic type, larger tumor size, mixed mucinous/non-mucinous component, and higher overall stage were significant influence factors of 5-year progression-free survival (PFS) and overall survival (OS). Multivariate analysis further indicated that type of imaging, tumor size, mucinous component were the independent prognostic factors for poor 5-year PFS and OS. Moreover, the 5-year PFS and OS rates were 62.82% and 75.64%, respectively. In subgroups, the survival analysis also showed that the pneumonic type and mixed mucinous/non-mucinous patients had significantly poorer 5-year PFS and OS compared with solitary type and pure mucinous patients, respectively. The C-index of Nomograms with 5-year PFS and OS were 0.815 (95%CI: 0.741-0.889) and 0.767 (95%CI: 0.669-0.865). The calibration curve and decision curve analysis (DCA) of both models showed good predictive performances in both cohorts. Conclusion The Nomograms based on clinicopathological characteristics in a certain extent, can be used as an effective prognostic tool for patients with pathologic N0 after IMA resection. [ABSTRACT FROM AUTHOR]

Background and objective Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model. Methods The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA). Results Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cutoff value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit. Conclusion Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making. [ABSTRACT FROM AUTHOR]

In phase TMF tests on nickel-based high-temperature alloy GH4169, a commonly used material for engine hot end components, under conditions of 200 - 450 ° C and 400 - 650 ° C is conducted. Considering the micro damage strain energy of polycrystaline materials in the elastic stages under the condition of TMF, a TMF life prediction model suitable for polycrystaline materials was proposed, model parameters were determined by combining the TMF test data. Three superalloys, GH4169, IN718, DD8, were used to evaluate the TMF life prediction ability of this model. The prediction accuracy of the life model is higher than that of the Manson-Coffin model and Ostergen model commonly used for TMF life prediction. [ABSTRACT FROM AUTHOR]

The prediction of urban river water level is of great significance to the risk management of urban waterlogging. Traditional numerical simulation models have low computational efficiency and are unable to perform real-time calculations. In response to the above issues, this paper proposes a channel water level prediction model based on Gaussian function improved BP neural network, which solves the problems of low prediction accuracy and slow convergence speed in error flat areas of the BP neural network model. This method utilizes the Gaussian function to improve the gradient descent algorithm of the BP neural network, sets different learning rates for different weights and threshold values of the model, and optimizes each parameter accordingly, which can effectively accelerate the training efficiency of the BP neural network model. In response to the problem of slow convergence speed of the model in error flat areas, the paper uses the Gaussian function to increase the learning rate of the gradient descent algorithm in error flat areas, and to control the learning rate of the gradient descent algorithm when the error is serious, which can effectively accelerate the convergence speed of the BP neural network model in error flat areas. This paper takes 6 river water level measurement stations in Jin an District, Fuzhou City as the research object, constructs a GA-BP neural network river water level prediction model for urban river water level prediction, and explores the impact of different rainfall input forms on the accuracy of river water level prediction. The results show that the GA-BP neural network can effectively improve the convergence speed and model prediction accuracy of the BP neural network in error flat areas. The Nash Efficiency Coefficient (NSE) of the experimental set prediction is above 0.8, and the relative error of the predicted peak water level can be controlled within 5%. The input of rainfall in the form of hourly rainfall can increase the predicted NSE to over 0.9. Research has shown that using Gaussian function to improve the BP neural network model can effectively improve the prediction accuracy of the model, which is of great significance to improving urban river water level prediction. [ABSTRACT FROM AUTHOR]

ObjectiveTo analyze the methodological and reporting quality of systematic reviews of prediction models published in Chinese journals, with the aim of providing reference for enhancing the overall quality of Chinese systematic reviews of prediction models.MethodsWe searched the CNKI, WanFang Data, CBM, and VIP databases for Chinese systematic reviews of prediction models from inception to July 20, 2023. After two independent reviewers screened literature and extracted data, the AMSTAR(A Measurement Tool to Assess Systematic Reviews) and PRISMA 2020(Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020) tools were used to assess the methodological and reporting quality of the included reviews.ResultsA total of 55 systematic reviews published between 2015 and 2023 were included, 12 of which were meta-analysis. The reviews covered various topics, mainly including cardiovascular diseases, stroke, and diabetes. The identified systematic reviews exhibited obvious deficiencies: items 1, 4, 5, 6, and 10 of AMSTAR showed poor methodological quality, and items 7, 10a, 12, 13a-f, 14, 15, 16a-b, 17, 20b-d, 21, 22, 23d, 24a-c, 25 and 26 of PRISMA 2020 needed improvement in reporting quality. Furthermore, a moderate positive correlation (r=0.58, P < 0.001) was observed between the methodological and reporting quality. Multiple linear regression analysis revealed that a greater number of pages, more recent publications, and funding support were associated with higher methodological quality (P < 0.05). Similarly, a greater number of pages, more recent publications, qualitative systematic reviews, and funding support were associated with higher reporting quality, but the number of authors showed a negative association (P < 0.05).ConclusionThe methodological and reporting quality of existing systematic reviews of prediction models published in Chinese journals is relatively poor and demands improvement.

Aim To discuss the construction of the nomogram model of coronary atherosclerotic plaque (CAP) shown on chest computed tomography (CT) and the influencing factors of plaque stability. Methods The data of patients at high risk of cardiovascular disease that received CT examination from January 2020 to October 2021 were collected for a retrospective cross-sectional study. Basic information (including gender and age), medical history, complications, blood pressure, blood biochemical indicators, etc.of the patients were collected. According to their coronary artery conditions shown by CT imaging, the patients were divided into CAP group and CAP-free group. The data were then reduced by using LASSO regression cross-validation. The nomogram model was built and validated, and its application value was evaluated by the decision curve. In the end, the influencing factors of CAP-CT value were evaluated by optimal scaling regression. Results There were 240 patients with CAP and 52 patients without CAP included in the research. After adjusting the parameter (λ+1se), age, uric acid, estimated glomerular filtration rate (eGFR) were non-zero variables. The statistically significant age and eGFR were used to build the nomogram model. Area under ROC curve was 0.759 (95%CI: 0.691 ~ 0.828), sensitivity was 73.8%, specificity was 71.2%. Hosmer-Lemeshow test indicated good model fit (χ² =11.846, P =0.158). The prediction accuracy was 82.2%. Mean absolute error of Bootstrap resampling internal verification was 0.029. The calibration curve basically fits the ideal curve. According to the decision curve, the nomogram model shows good net benefits within the risk threshold of 0.10 ~ 0.40. Combined diabetes mellitus and β²-microglobulin (β²-MG) were independent risk factors for CAP-CT values (both P<0.05), with the importance of 0.121 and 0.564, respectively. Conclusion Emphasis on the protection of renal function and glycemic compliance will help to reduce the formation of CAP and increase CAP stability. In particular, more attention should be paid to renal function when selecting medications. [ABSTRACT FROM AUTHOR]

Coal resources are one of the important mineral resources in China. In the process of coal mining, due to the complex hydrogeological conditions in the mining area, and ineffective water exploration and discharge, accidents of mine water inrush occasionally occur, which may seriously restrict the safe production of coal resources. According to statistics, from 2000 to 2017, there were 1,173 accidents of coal mine flood in China, with 4,760 deaths. Therefore, the prediction reliability of mine water inflow plays a vital role in the safety of coal mining. A time series model is specifically designed to simulate and predict a time-sequential, time-varying, and interrelated data series. Most time series models require that the data must be stationary and the time series must follow a normal distribution. Taking Laoyingshan coal mine as an example, this study establishes a model of Seasonal Auto-Regressive Integrated Moving Average (SARIMAX model) and a model of Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA model), compares the fitting and prediction results of these two models, and evaluates their adaptability in prediction of mine water inflow in karst mountainous areas. Based on the monthly average rainfall and monthly average water inflow from October 1994 to December 2014, a SARIMA model for univariate seasonal time series and a SARIMAX model for multivariate seasonal time series have been established. To establish a corresponding mathematical model, it is necessary to perform a parameter significance test on each model, analyze the model fitting goodness and model fitting accuracy, and determine the optimal model. The test parameters can be selected from the coefficient of determination R2 of the sample, the Nash efficiency coefficient (NSE), the mean absolute percentage error (MAPE), the deviation, the root mean square error (RMSE), the AIC value, the BIC value and other indicators to test. Since NSE, RMSE, R2, and MAPE standards are correlated in some degree, the NSE, AIC and BIC values are selected as the criteria for validating the quality of the model. The above two different models are used to predict the average monthly water inflow of the mine in 2015. The model prediction results show that, except for the SARIMA model in November 2015 and the SARIMAX model in July 2015, the MAPE is greater than 40%. According to Qian Xuepu's classification of the prediction accuracy of mine water inflow, these prediction results can reach the mine water inflow of Level B. According to the relative error of the prediction results, the MAPE errors of the SARIMA model in the first 4 months and those of the SARIMAX model in the first 5 months are both within 25%, and the subsequent errors experience a maximum value or a large fluctuation with the step size, indicating good short-term prediction but poor adaptability for long-term prediction of these two models. Even so their prediction accuracy can still reach the mine water inflow controlled by Level C. In terms of the prediction accuracy, the SARIMAX model is more accurate in prediction than the SARIMA model. The main reason is that the SARIMA model is a univariate prediction model, which predicts the later changes only based on the time series changes of the water inflow itself, but ignores the external factors caused by the water inflow. The SARIMAX model only introduces the influence of rainfall on water inflow, but as one of the important factors affecting water inflow, rainfall plays an obvious role in improving the prediction accuracy. The correlation between rainfall and mine water inflow indicates that atmospheric precipitation is the main source for water recharge, and mining fissures are the main recharges channels in the mining area. The change of water inflow has a certain hysteresis effect relative to rainfall. With the extension of mining level, the increase of the mining area and the backfilling of the water-conducting fissures caused by the goaf collapse, the hysteresis effect of rainfall becomes increasingly obvious. The correlation between rainfall and inflow in the same month and the first previous month radually decreases, while it gradually increases from the second and fifth months. Based on the mine water inflow and rainfall, the SARIMA model for univariate seasonal time series and the SARIMAX model for multivariate seasonal time series have been established to predict the mine water inflow. The prediction results show that 91.7% of the predicted values of the two models reach the mine water inflow of Level B, and the prediction accuracy is high. The MAPE of SARIMAX model is 18.57%, less than that of SARIMA model (25.27%), indicating higher accuracy of SARIMAX model. [ABSTRACT FROM AUTHOR]

Objective To investigate the influencing factors of abnormal pulmonary function in workers exposed to dust in Yinchuan, and to establish and verify a risk prediction model of abnormal pulmonary function. Methods A total of 923 dust workers who underwent occupational health examination in Yinchuan from March to July 2022 were investigated by questionnaire, physical examination, and laboratory examination. The influencing factors of abnormal pulmonary function in dust exposed workers were analyzed by Logistic regression, and the risk prediction model of abnormal pulmonary function was established> The Receiver Operator Characteristic (ROC) curve, calibration curve, and decision analysis curve were used to evaluate the model. Results The results of multivariate Logistic regression showed that no personal protection (OR =7.806, 95%CI: 5,803-11.989), length of service at work (compared with 0 to 5 years, 6 to 10 years: OR =3.025, 95%CI: 1.764-5.18& > 10 years: OR =2.546: 95%CI: L545-4.193), hypertension (OR =3.909: 95%CI: 2.223-6.873), and BMI (3 24.0 kg/m2) (OR=1.553, 95%CI: 1.004-2.401) were independent risk factors of pulmonary dysfunction, while education (college) (OR= 0,503, 95%CI: 0.299-0.847) was the protective factor of pulmonary dysfunction. The area under the ROC curve of the risk prediction line chart was 0.806, and the calibration curve showed good consistency. The results of clinical decision curve showed that the intervention of abnormal lung function had greater benefits when the threshold probability of the line chart model was 10% to 88%. Conclusion The nomogram constructed by Logistic regression visually demonstrates the role of many influencing factors and has good accuracy in predicting the risk of abnormal pulmonary function in workers exposed to dust. [ABSTRACT FROM AUTHOR]

Background and objective In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value. Methods Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopatho-logical information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups. Results Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (x2=0.040, P=0.979), the internal validation group (x2=2.605, P=0.271), and the external validation group (x2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66). Conclusion The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients. [ABSTRACT FROM AUTHOR]

In order to overcome the dilemma of insufficient effective sample number for subsurface microcrack depth in the lapping of brittle materials with fixed abrasives and achieve accurate prediction, a co-training SVR was used to construct the prediction model. The effects of different labeled training set partitioning methods on the mean square error of the test set were compared. Then the predictive performance of supervised learning PSO-SVR model was compared with that of the model. Finally, brittle materials such as microcrystalline glass and calcium fluoride, which were not included in the labeled training set, were taken as processing objects for lapping and angular polishing experiments to examine crack depth values. The examined subsurface microcrack depths of four groups were compared with the predicted values of the co-training SVR model. The results show that the co-training SVR model under the separate partitioning method has a smaller mean square error. Compared with the PSO-SVR model, the mean square error and the mean absolute percentage error of the co-training SVR model are reduced by 9% and 17%, respectively. The prediction error of the model for the four groups of verification experiments is between 1.2% and 13.8%. The above results show that the co-training SVR model can predict the subsurface microcrack depth accurately when lapping brittle materials with fixed abrasives. [ABSTRACT FROM AUTHOR]

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Objective To explore the clinical application of quantitative electroencephalography (qEEG) in predicting the early prognosis of acute ischemic stroke after mechanical thrombectomy. Methods A total of 71 patients who underwent acute ischemic stroke mechanical thrombectomy from April 2021 to September 2022 were enrolled in He'nan Provincial People's Hospital, all of them performed qEEG after surgery. Modified Rankin Scale (mRS) was used to evaluate the prognosis on the 90 d after the surgery. Univariate and multivariate backward Logistic regression analyses were used to screen risk factors for prognosis in acute ischemic stroke after mechanical thrombectomy. Receiver operating characteristic (ROC) curve was used to predict the value of qEEG for the prognosis of neural function. Results Patients in dismal prognosis group (mRS score 3-5, n = 28) had higher NIHSS score at admission (t = 2.686, P = 0.009), higher proportion of thrombectomy performed more than 3 times (χ² = 4.201, P = 0.040), higher values of DTABR on the affected side (t = 2.183, P = 0.032), higher ratio of DTABR on affected side/ unaffected side (t = 6.230, P = 0.000), and higher proportion of immediately Thrombolysis in Cerebral Infarction (TICI) < 2b after surgery (χ² = 5.420, P = 0.020) compared to favorable prognosis group (mRS score 0-2, n = 43). Logistic regression analysis showed higher NIHSS score at admission (OR = 1.542, 95%CI: 1.368-1.725; P = 0.012), DTABR on affected side/unaffected side (OR = 3.428, 95%CI: 2.673-7.314; P = 0.008), percent alpha variability (PAV) grade II (OR = 2.983, 95%CI: 2.625-4.682; P = 0.003) and grade III (OR = 4.088, 95%CI: 3.825-5.349; P = 0.002), amplitude electroencephalography (aEEG) grade II (OR = 2.536, 95%CI: 1.942-5.287; P = 0.005) and grade III (OR = 3.924, 95%CI: 2.012-6.378; P = 0.003) were risk factors of dismal prognosis in acute ischemic stroke after mechanical thrombectomy. ROC curve showed the area under the curve (AUC) of NIHSS score was 0.676 (95%CI: 0.554-0.782, P = 0.000), DTABR on affected side/unaffected side was 0.887 (95%CI: 0.789-0.950, P = 0.000), the PAV was 0.760 (95%CI: 0.643-0.853, P = 0.000), aEEG was 0.778 (95%CI: 0.664-0.868, P = 0.000), and the combined indicators classification was 0.943 (95%CI: 0.861-0.984, P = 0.000). Among them, the predictive power of the combined indicators is higher than that of the NIHSS score at admission (Z = 4.150, P = 0.000), PAV (Z = 4.006, P = 0.000) and aEEG (Z = 3.462, P = 0.001). Conclusions qEEG is an effective method to predict the early prognosis of acute ischemic stroke after mechanical thrombectomy. [ABSTRACT FROM AUTHOR]

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Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aimed at the problem that valve regulated lead-acid (VRLA) battery aging threatening power supply reliability in cloud data centers, a method of an online battery state prediction method based on machine learning algorithms was proposed. Predicting battery status through classic machine learning algorithm models, comparative experiments were conducted on the prediction accuracy between models without extracted feature values and models with extracted feature values. Compare the prediction accuracy of the model using unrefined feature values with the model extracting feature values. The prediction accuracy of the model without extracted feature values ranges from 71. 72% to 81. 82%, and after extracting feature values, the prediction accuracy has improved by 10. 10% to 20. 20%, extracting feature values can improve prediction accuracy. The prediction model based on linear SVM method for extracting feature values is superior to other algorithms, with an accuracy of 96. 46%. Experiment results show that machine learning algorithm based prediction models can be used for online VRLA battery state prediction in cloud data centers. [ABSTRACT FROM AUTHOR]

Aim To develop and validate a risk prediction model for fundus arteriosclerosis. Methods Patients without fundus arteriosclerosis who underwent physical examination in Huadong Sanatorium from 2006 to 2013 were selected as the derivation cohort. Lasso method was used to screen the predictors, and Cox regression method was used to establish the prediction model. The model was presented as an online calculator. The Bootstrap method was used for internal validation, and the physical examination subjects in the hospital from 2015 to 2021 were selected for temporal validation. The concordance (C) statistic was used to quantify discrimination, and the calibration of the model was evaluated by comparing the predicted survival probability with the observed survival probability using calibration plots and the KaplanMeier method. Results The derivation cohort included 33 218 participants and external validation cohort included 53 863 participants. The final model included nine predictors: age, body mass index, alcohol consumption, diastolic blood pressure, hypertension, fasting blood glucose, diabetes, triglyceride, and serum uric acid. Online calculator site at: https: / / rui2022. shinyapps. io / DynNomapp / . The C statistic of internal validation was 0. 841, and the C statistic of external validation was 0. 856. Calibration performed well in both the derivation and external validation cohorts. Conclusion The established risk prediction model of fundus arteriosclerosis has a good predictive ability in the physical examination population. The model only needs the variables routinely obtained by the hospital, so it can be applied to the individualized management of physical examination population and the decision support of the management of high-risk people. [ABSTRACT FROM AUTHOR]

As the basis for maintaining the operation and analysis of the power grid system, short-term power load forecasting provides judgment basis and information for the economic dispatch and safety analysis of the power grid system, and plays an important role in maintaining the normal operation of the power grid system. In this study, the FA(Firefly Algorithm) is used to optimize the penalty factor c, nuclear parameter g of SVR(Support Vector Regression) model and the number of neurons m and learning rate lr of LSTM(Long Short-Term Memory) model. The FA-SVR-LSTM combined prediction model is established using the optimal parameters, and the sample data are predicted. Taking the historical data of power load of Florida as an example, four reference models of LSTM, SVR, FA-SVR and FA-LSTM are established to predict the power load of 360 h in 15 days, and the results are compared with those of FA-SVR-LSTM. The experimental results show that compared with LSTM and SVR model, the prediction accuracy of FA-SVR-LSTM model is improved by 33.184 9% and 30.326 5%, respectively. The evaluation values of MAPE and RMSE are significantly lower than those of the other four models. These results indicate that the prediction effect of FA-SVR-LSTM combined model is significantly improved when compared with other models. [ABSTRACT FROM AUTHOR]