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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]

Background: The safety and efficacy of robotic liver resection (RLR) for patients with hepatocellular carcinoma (HCC) have been reported worldwide. However, the exact role of RLR in HCC patients with liver cirrhosis is not sufficiently determined. Methods: We conducted a retrospective study on consecutive patients with cirrhosis or non-cirrhosis who received RLR for HCC from 2018 to 2023. Data on patients' demographics and perioperative outcomes were collected and analyzed. Propensity score matching (PSM) analysis was performed. Multivariate logistic regression analysis was performed to determine the risk factors of prolonged postoperative length of stay (LOS) and morbidity. Results: Of the 571 patients included, 364 (64%) had cirrhosis. Among the cirrhotic patients, 48 (13%) were classified as Child–Pugh B. After PSM, the cirrhosis and non-cirrhosis group (n = 183) had similar operative time, estimated blood loss, postoperative blood transfusion, LOS, overall morbidity (p > 0.05). In addition, the intraoperative and postoperative outcomes were similar between the two groups in the subgroup analyses of patients with tumor size ≥ 5 cm, major hepatectomy, and high/expert IWATE difficulty grade. However, patients with Child–Pugh B cirrhosis had longer LOS and more overall morbidity than that of Child–Pugh A. Child–Pugh B cirrhosis, ASA score > 2, longer operative time, and multiple tumors were risk factors of prolonged LOS or morbidity in patients with cirrhosis. Conclusion: The presence of Child–Pugh A cirrhosis didn't significantly influence the difficulty and perioperative outcomes of RLR for selected patients with HCC. However, even in high-volume center, Child–Pugh B cirrhosis was a risk factor for poor postoperative outcomes. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the prevalence of insomnia among nurses with long COVID-19, analyze the potential risk factors and establish a nomogram model. Methods: Nurses in Ningbo, China, were recruited for this study. General demographic information and insomnia, burnout, and stress assessment scores were collected through a face-to face questionnaire survey administered at a single center from March to May 2023. We used LASSO regression to identify potential factors contributing to insomnia. Then, a nomogram was plotted based on the model chosen to visualize the results and evaluated by receiver operating characteristic curves and calibration curves. Results: A total of 437 nurses were recruited. 54% of the nurses had insomnia according to the Insomnia Severity Index (ISI) score. Eleven variables, including family structure, years of work experience, relaxation time, respiratory system sequelae, nervous system sequelae, others sequelae, attitudes toward COVID-19, sleep duration before infection, previous sleep problems, stress, and job burnout, were independently associated with insomnia. The R-squared value was 0.464, and the area under the curve was 0.866. The derived nomogram showed that neurological sequelae, stress, job burnout, sleep duration before infection, and previous sleep problems contributed the most to insomnia. The calibration curves showed significant agreement between the nomogram models and actual observations. Conclusion: This study focused on insomnia among nurses with long COVID-19 and identified eleven risk factors related to nurses' insomnia. A nomogram model was established to illustrate and visualize these factors, which will be instrumental in future research for identifying nurses with insomnia amid pandemic normalization and may increase awareness of the health status of healthcare workers with long COVID-19. [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]

The GaoFen (GF)-5-02 satellite is one of the new generations of hyperspectral observation satellites launched by China in 2021. The directional polarimetric camera (DPC) is an optical sensor onboard the GF-5-02 satellite. The precipitable water vapor (PWV) is a key detection parameter of DPC. However, the existing PWV data developed using DPC data have significant errors due to the lack of the timely calibration of the two bands (865, 910 nm) of DPC used for PWV retrieval. In order to acquire DPC PWV data with smaller errors, a calibration method is developed for these two bands. The method consists of two parts: (1) calibrate the 865 nm band of the DPC using the cross-calibration method, (2) calibrate the 910 nm band of the DPC according to the calibrated 865 nm band of the DPC. This method effectively addresses the issue of the absence of a calibration method for the water vapor absorption band (910 nm) of the DPC. Regardless of whether AERONET PWV data or SuomiNET PWV data are used as the reference data, the accuracy of the DPC PWV data developed using calibrated DPC data is significantly superior to that of the DPC PWV data retrieved using data before recalibration. This means that the calibration method for the NIR bands of the DPC can effectively enhance the quality of DPC PWV data. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Suite-Nadir (OMS-N) instrument is the first hyperspectral remote sensor in the ultraviolet band of China's Fengyun series satellites. It can be used to detect several kinds of atmospheric constituents. This paper describes the prelaunch spectral calibration of the OMS-N onboard FengYun 3F. Several critical spectral parameters including the spectral resolution, spectral dispersion, and the instrument spectral response function were determined through laser-based measurements. A secondary peak of the instrument spectral response function from the short wavelength side of the ultraviolet band was found, and the possible influence on data applications was analyzed using a reference solar model and radiative transfer model. The results indicate that the spectral resolution and spectral accuracy of OMS-N meet the mission requirements. However, the asymmetries in the instrument spectral response function in the ultraviolet band were found near nadir rows, which are expressed as the "asymmetric central peak" and "secondary peak". The analysis results show that if the influences of the instrument spectral response function "asymmetric central peak" and "secondary peak" in the ultraviolet band are ignored, they will bring an error as large as 5% at the center of the absorption line. [ABSTRACT FROM AUTHOR]

Passive differential optical absorption spectroscopy (DOAS) is widely used to monitor the three-dimensional distribution of atmospheric pollutants. However, the observational and retrieval accuracy of this technique is significantly influenced by the precise wavelength calibration of solar spectra. Current calibration methods face challenges in automation when dealing with complex remote-sensing conditions. We introduce a novel automatic wavelength calibration algorithm for passive DOAS based on sequence-matching technology to estimate the spectral parameters of the spectrometer channels, integrating advanced processing measures such as feature structure enhancement and sub-pixel interpolation. These measures significantly reduce the dependency on reference spectrum resolution and accurately correct even minor spectral shifts. We perform sensitivity experiments using synthetic spectra to determine optimal retrieval configurations, followed by field tests at four cities on the Yangtze River Delta, China, to calibrate and compare passive DOAS instruments of various resolutions. Comparative verification in these field studies demonstrated that our algorithm was suitable for rapid spectral calibration within a wider resolution range of 0.03 nm to 0.1 nm with a wavelength inversion error < 0.01 nm. This highlights the applicability and calibration precision of our algorithm. [ABSTRACT FROM AUTHOR]

INTRODUCTION In recent years, e-cigarettes as an emerging tobacco product have been favored by college students. Our study aims to explore the factors affecting the use of e-cigarettes among college students and to put forward feasible suggestions for effectively controlling the use of e-cigarettes among college students. METHODS The participating students were from three undergraduate and three specialized colleges in Guangdong Province, surveyed from January to March 2022. The Fuzzy-set Qualitative Comparative Analysis (fsQCA) method was used to analyze the influence mechanism and path of five antecedents: self-efficacy, social environment, cognition, sales environment, and negative outcome expectation, on the use of e-cigarettes. The fsQCA used in this study is a novel research methodology that combines the strengths of qualitative and quantitative analyses, through which we can determine which conditions are essential to the outcomes that lead to e-cigarette use among college students, and which combinations of conditions are more important than others. RESULTS The interaction of self-efficacy, social environment, cognition, sales environment, and negative outcome expectation, affected college students' use of e-cigarettes. Through the fsQCA method, it was found that self-efficacy alone constitutes a necessary condition for college students not to use e-cigarettes. There are four possible pathways for college students not to use e-cigarettes, with higher self-efficacy, correct cognition, and a healthy social environment influencing the most important combination of conditions for college students to use e-cigarettes. CONCLUSIONS The use of e-cigarettes by students in Guangdong Province is the result of the synergistic effect of multiple factors. Tobacco control action suggestions focus on improving students' self-efficacy and paying attention to the combination of different factors to achieve more effective tobacco control. [ABSTRACT FROM AUTHOR]

Background: To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. Methods: In this cross‐sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis. Results: A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high‐ and low‐risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility. Conclusions: The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment. [ABSTRACT FROM AUTHOR]

Background: Endoscopic submucosal dissection (ESD) was widely used for the removal of esophageal tumors, and post-endoscopic submucosal dissection electrocoagulation syndrome (PEECS) was one of the postoperative adverse events. The aim of this research was to develop and validate a model to predict electrocoagulation syndrome after endoscopic submucosal dissection of esophageal tumors. Materials and methods: Patients who underwent esophageal ESD in our hospital were retrospectively included. A predictive nomogram was established based on the results of multivariate logistic regression analysis, and bootstrapping resampling was used for internal validation. Besides, the clinical usefulness of the nomogram was evaluated using decision curve analysis (DCA) and clinical impact curve. Results: A total of 552 patients who underwent esophageal ESD were included in the study, and the incidence of PPECS was 12.5% (69/552). Risk factors associated with PEECS (p < 0.1) were analyzed by multivariate logistic regression analysis, and the final model included four variables, namely gender, diabetes, tumor size and operation time. The predictive nomogram was constructed based on the above four variables, and the area under the ROC curve (AUC) was 0.811 (95% CI 0.767–0.855). The calibration curve of the nomogram presented good agreement between the predicted and actual probabilities. DCA showed that the model improved patient outcomes by helping to assess the risk of PEECS in patients compared to an all-or-no treatment strategy. In addition, the clinical impact curve of the model also indicates that the nomogram has a high clinical net benefit. Conclusion: In conclusion, we have developed a predictive nomogram for PEECS after ESD for esophageal tumors with good predictive accuracy and discrimination. This predictive nomogram can be effectively used to identify high-risk patients with PEECS, which will help clinicians in clinical decision-making and early intervention. [ABSTRACT FROM AUTHOR]

Due to the considerable depth of the salt layers and the lack of calibration by exploratory drilling, the interpretation of the Middle and Lower Cambrian salt formations in the central Tarim Basin poses a challenge. In this paper, we apply the coupling and decoupling deformation theory in salt tectonics to analyze the No.7 fault mapped in the seismic datasets by the response characteristics of the Middle and Lower Cambrian layers. By quantifying the stratigraphic framework of the Middle and Lower Cambrian strata, we define the position of the salt layer with the seismic data. Structural decoupling is observed in the Middle and Lower Cambrian sequences in the Shuntuoguole Low Uplift, while deformation coupling is observed in these two sequences in the Shaya Uplift. [ABSTRACT FROM AUTHOR]

Measurements collected from ground monitoring stations have gained popularity as a valuable data source for calibrating numerical models and correcting model errors through data assimilation. Both model calibration and assimilation are driven by the penalty quantified by simulation-minus-observations. However, the penal forces are challenged by the existence of a spatial scale disparity between model simulations and observations. The Chemical Transport Models (CTMs) allow the division of the atmosphere into grid cells, yet their spatial resolution may not align with the limited range of in-situ measurements, particularly for short-lived air pollutants. Within a broad grid pattern, air pollutant concentrations can exhibit significant heterogeneity due to their rapid generation and dissipation. Ground observations with traditional methods (including nearest search and grid mean) are less representative when compared to model simulations. This study develops a new land-use-based representative (LUBR) observational operator to generate spatially representative gridded observation for model calibration and evaluation. It incorporates high-resolution urban-rural land use data to address intra-grid variability. The LUBR operator is validated to consistently provide insights that align with satellite OMI measurements. It is an effective solution to accurately quantify these spatial scale mismatches and further resolve them via assimilation. Model calibrations with 2015-2017 NO2 measurement in China demonstrates biases and errors differed substantially when the LUBR and other operator are used, respectively. The results highlight the importance of considering fine-scale urban-rural differences when comparing models and observations, especially for short-lived pollutants like NO2. [ABSTRACT FROM AUTHOR]

As an important elemental analysis method, laser-induced breakdown spectroscopy (LIBS) enables the qualitative and quantitative determination of the compositions of geological targets on Mars. In order to analyze the chemical compositions of Martian rocks and soils at Gale crater, the ChemCam team built a LIBS spectral library of 408 geological standards using a ChemCam's ground replica under a simulated Martian atmosphere. However, the lack of spectra data with component labels makes it difficult to establish a composition inversion model for predicting the collected spectra. The Mars Surface Composition Detector (MarSCoDe) carried by China's Mars rover, Zhurong, also employs LIBS technology and has successfully conducted related composition detection work. Therefore, this paper proposes a domain-adaptive fully connected neural network (DAFCN) capable of transferring prior knowledge between ChemCam and SDU-LIBS (a LIBS system developed by Shandong University). The model extracts the features of the source domain and target domain through a three-layer fully connected network and uses a domain adaptation function to reduce the difference in feature distribution between the source domain and target domain. Experimental results show that the proposed method is superior to the traditional method without migration. Further, we tried this method to establish the connection between MarSCoDe and other LIBS devices, and then quantitatively analyzed the spectra collected by MarSCoDe. [ABSTRACT FROM AUTHOR]

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets. [ABSTRACT FROM AUTHOR]

In this work, a reliable and robust in situ non-matrix-matched calibration method is proposed for element composition determination in scheelite samples. With external calibration against the silicate glass standard reference material NIST SRM 610, the concentrations of both major elements (Ca and W) and trace elements (Si, Fe, Mo, Y, rare earth elements, etc.) in scheelite are determined using an ArF 193 nm excimer nanosecond laser ablation-inductively coupled plasma mass spectrometer (LA-ICP-MS). Here, the ablation was performed by hole drilling under a helium (He) environment using a laser spot size of 35 μm and a laser repetition of 5 Hz, and the aerosols were then transported to a quadrupole ICP-MS by a mixture of He and make-up gas argon (Ar) with a total gas flow rate of 1.6 L/min. Results showed that there was no apparent matrix effect between the NIST SRM 610 and scheelite by this proposed method. With internal standardization against W, the obtained concentrations of CaO and WO3 were found to yield an average matrix CaO/WO3 mass fraction ratio of 0.245 (2σ = 0.003, n = 19), which agreed well with the value of 0.243 (2σ = 0.002, n = 15) from electron probe microanalysis (EPMA). Furthermore, the accuracy of trace element analyses with this proposed non-matrix-matched calibration in situ method was evaluated by comparing the concentration results with those from bulk analysis by solution nebulizer ICP-MS (SN-ICP-MS). It was found that the quantification results from LA-ICP-MS and SN-ICP-MS were comparable, in particular showing a relative concentration bias of the total ∑REE+Y contents of less than 2%. This confirmed that scheelites can be accurately analyzed in situ by LA-ICP-MS without matrix-matched calibration standards. By using this developed in situ method, the element compositions in a series of scheelite samples from different W-associated deposits in China were successfully quantified, promising further genetic process investigation and associated geologic activities of the polymetallic resources. [ABSTRACT FROM AUTHOR]

Background Predicting mortality in stroke patients using information available before endovascular treatment (EVT) is an essential component for supporting clinical decision-making. Although the mortality rate of acute basilar artery occlusion (ABAO) after EVT has reached 40%, few studies have focused on predicting mortality in these individuals. Thus, we aimed to develop and validate a machine learning-based mortality prediction tool based on preoperative information for ABAO patients receiving EVT. Methods The derivation cohort comprised patients from southern provinces of China in the BASILAR registry. The model (POSITIVE: Predicting mOrtality of baSilar artery occlusion patIents Treated wIth EVT) was trained and optimized using a fivefold cross-validation method in which hyperparameters were selected and fine-tuned. This model was retrospectively tested in patients from the northern provinces of China from the BASILAR registry. A prospective test of POSITIVE was performed on consecutive patients from two hospitals between January 2020 and June 2022. Results Extreme gradient boosting was employed to construct the POSITIVE model, which achieved the best predictive performance among the eight machine learning algorithms and showed excellent discrimination (area under the curve (AUC) 0.83, 95% confidence interval (95% CI) 0.80 to 0.87) and calibration (Hosmer-Lemeshow test, P>0.05) in the development cohort. AUC yielded by the POSITIVE model for the retrospective test was 0.79 (95% CI 0.71 to 0.85), higher than that obtained by traditional models. Prospective comparisons showed that the POSITIVE model achieved the highest AUC (0.82, 95% CI 0.74 to 0.90) among all prediction models. Conclusion We developed a machine learning algorithm and retrospective and prospective testing with multicentric cohorts, which exhibited a solid predictive performance and may act as a convenient reference to guide decision-making for ABAO patients. The POSITIVE model is presented online for user-friendly access. [ABSTRACT FROM AUTHOR]

To accurately evaluate the size and distribution characteristics of the emission particles exhausted from in-use motor vehicle engines, we independently developed a condensation particle counter (CPC) known as BHCPC. It was calibrated by conducting the calibration procedures stated in the International Standard ISO 27981. After calibration, we conducted on-site measurements and offline sampling analysis of soot particles exhausted from different engines at a motor vehicle inspection center. The calibration results show that the detection efficiency is 90% when the particle diameter is 20.6 nm and the startup response time of the instrument is 3.53 s. The experiment results show that the self-developed BHCPC demonstrates good consistency in measuring particle-number concentration (PNC) in motor vehicle exhaust, with significant count fluctuations only occurring when PNC is higher than 25,000 P/cc. Under idle conditions, motor vehicles compliant with China VI emission regulations exhibit markedly lower exhaust emission PNC compared to those adhering to China IV emission regulations. Moreover, the results obtained from the offline electron microscope analysis show that fuel content in particle samples significantly decreases as engine speed increases, and a similar variation was also found in particle size. The micro-characteristics of the particle can give potential support to the combustion diagnostics. [ABSTRACT FROM AUTHOR]

With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings. [ABSTRACT FROM AUTHOR]

The GaoFen-14 (GF-14) satellite is China's most recent high-resolution earth observation satellite system. It is equipped with a two linear-array stereo camera and is intend for topographic mapping at 1:10,000-scale without ground control points (GCPs). The technical parameters of payloads will change once the satellite enters orbit. As a result, strict on-orbit geometric calibration is necessary. This study performs the on-orbit geometric calibration of the GF-14 stereo camera using ground calibration data. The exterior orientation errors are corrected by a comprehensive bias matrix, internal orientation errors are described by a fifth order polynomial. The results of Yinchuan calibration field show that the planar accuracy is better than 286 m (RMS) before calibration, and improved to 2.11 m (front camera) and 1.51 m (back camera) by external orientation, and further improved to 1.46 m (front camera) and 0.99 m (back camera) after internal calibration. The front intersection accuracy reaches 0.67 m in plane (RMS) and 1.10 m in elevation (RMS), respectively. Checking by multiple global check points (CKPs), the planar and elevation RMS reaches 2.38 m (5.09 m in CE90) and 2.08 m (3.43 m in LE90), respectively. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we try to investigate the risk factors of postoperative surgical site infection (SSI) in closed pilon fractures and establish a nomogram prediction model. Methods: From January 2012 to June 2021, 516 closed pilon fracture patients were included in this study. Of these, 387 patients were randomly assigned to the training group and 129 patients were assigned to the validation group (3:1). By univariate and multivariate Cox analysis, we identified independent risk factors for postoperative SSI after Pilon fracture. We established a nomogram model and used receiver operating characteristic (ROC) and calibration chart to evaluate its discriminant and calibration. Results: SSI occurred in 71 patients in the training group and 23 patients in the validation group. Ultimately, age, preoperative blood sugar, operative time, Tscherne classification and fracture classification were identified as independent risk factors for SSI. The AUC values for SSI of the training and validation group were 0.898 and 0.880, and the P value of the Hosmer–Lemeshow test was 0.125. We established a nomogram prediction model based on age, preoperative blood sugar, operative time, Tscherne classification and fracture classification. Conclusion: Our nomogram model had good discrimination and calibration power, so it could be used to predict SSI risk in patients with pilon fracture. [ABSTRACT FROM AUTHOR]

Background: Migraine is a common disabling neurological disorder with severe physical and psychological damage, but there is a lack of convenient and effective non-invasive early prediction methods. This study aimed to develop a new series of non-invasive prediction models for migraine with external validation. Methods: A total of 188 and 94 subjects were included in the training and validation sets, respectively. A standardized professional questionnaire was used to collect the subjects' 9-item traditional Chinese medicine constitution (TCMC) scores, Pittsburgh Sleep Quality Index (PSQI) score, Zung's Self-rating Anxiety Scale and Self-rating Depression Scale scores. Logistic regression was used to analyze the risk predictors of migraine, and a series of prediction models for migraine were developed. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the discrimination and calibration of the models. The predictive performance of the models were further validated using external datasets and subgroup analyses were conducted. Results: PSQI score and Qi-depression score were significantly and positively associated with the risk of migraine, with the area of the ROC curves (AUCs) predicting migraine of 0.83 (95% CI:0.77–0.89) and 0.76 (95% CI:0.68–0.84), respectively. Eight non-invasive predictive models for migraine containing one to eight variables were developed using logistic regression, with AUCs ranging from 0.83 (95% CI: 0.77–0.89) to 0.92 (95% CI: 0.89–0.96) for the training set and from 0.76 (95% CI: 0.66–0.85) to 0.83 (95% CI: 0.75–0.91) for the validation set. Subgroup analyses showed that the AUCs of the eight prediction models for predicting migraine in the training and validation sets of different gender and age subgroups ranged from 0.80 (95% CI: 0.63–0.97) to 0.95 (95% CI: 0.91–1.00) and 0.73 (95% CI: 0.64–0.84) to 0.93 (95% CI: 0.82–1.00), respectively. Conclusions: This study developed and validated a series of convenient and novel non-invasive prediction models for migraine, which have good predictive ability for migraine in Chinese adults of different genders and ages. It is of great significance for the early prevention, screening, and diagnosis of migraine. [ABSTRACT FROM AUTHOR]

Objective: Anemia is one of the common adverse reactions after hip fracture surgery. The traditional method to solve anemia is allogeneic transfusion. However, the transfusion may lead to some complications such as septicemia and fever. So far, few studies have reported roles of machine learning in predicting whether blood transfusion is needed or not after hip fracture surgery. Therefore, the purpose of this study is to develop machine learning models to predict the likelihood of postoperative blood transfusion in patients undergoing hip fracture surgery. Methods: This study enrolled 1355 patients who underwent hip fracture surgery at the Affiliated Hospital of Qingdao University from January 2016 to December 2021. Among all patients, 210 cases received postoperative blood transfusion. All patients were randomly divided into a training group and a testing group at a ratio of 7:3. In the training group, univariate and multivariate logistic regression analyses were used to determine independent risk factors for the postoperative transfusion. Then, based on these independent risk factors, tenfold cross-validation method was utilized to develop five machine learning models, including logistic, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and Matthews correlation coefficient (MCC) were generated to evaluate the performance of the models. Calibration plot and decision curve analysis (DCA) were used to test the performance, stability, and clinical applicability of the models. The models were validated using the testing group; and the ROC curve, MCC, calibration plot, and DCA curves were also generated to validate the performance, stability, and clinical applicability of the models. To further verify the robustness of the model, we randomly grabbed 70% of the samples in the testing set, performed 1000 iterations, and calculated the AUC and confidence interval of the five models. Finally, we used SHapley Additive exPlanations (SHAP) to explain these models. Results: Multivariate logistic regression analysis showed that there were 8 independent risk factors, including age, blood transfusion history, albumin (ALB), globulin (GLO), total bilirubin (TBIL), indirect bilirubin (IBIL), hemoglobin (HB), and blood loss > 200 ml. We finally selected five independent risk factors including HB, GLO, age, IBIL, and blood loss > 200 ml. Based on these five independent risk factors, we generated six characteristic variables, namely HB, HB × HB, HB × blood loss, GLO × HB, age, age × IBIL, and established five machine learning models using a tenfold cross-validation method. In the training group, the AUC values of logistic, RF, MLP, SVM, and XGB were 0.9320, 0.8911, 0.9327, 0.9225, and 0.8825, respectively, and the average AUC was 0.9122 ± 0.0212. The MCC values were 0.65, 0.77, 0.65, 0.66, and 0.68, respectively, and the calibration plot and DCA performed well. In the testing group the AUC values of logistic, RF, MLP, SVM, and XGB were 0.8483, 0.7978, 0.8576, 0.8598, and 0.8216, respectively. The average AUC was 0.8370 ± 0.0238, and the MCC values were 0.41, 0.35, 0.40, 0.41, and 0.41, respectively. The calibration plot and DCA in the testing group also showed good performance. The AUC values and confidence intervals of the 1000-iteration model were: logistic (AUC, min confidence interval [CI]–max confidence interval [CI] 0.848, 0.804–0.903), RF (AUC, minCI–maxCI 0.797, 0.734–0.857), MLP (AUC, minCI–maxCI 0.858, 0.812–0.902), SVM (AUC, minCI–maxCI 0.859, 0.819–0.910), and XGB (AUC, minCI–maxCI 0.821, 0.764–0.894). The model performed well. Finally, according to SHAP, among all five models, HB played the most important role in model prediction and interpretation. Conclusion: The five models we developed all performed well in predicting the likelihood of blood transfusion after hip fracture surgery. Therefore, we believed that the prediction model based on machine learning had great application prospects in clinical practice, which could help clinicians better predict the risk of blood transfusion after hip fracture surgery. [ABSTRACT FROM AUTHOR]

The CSES high precision magnetometer (HPM), consisting of two fluxgate magnetometers (FGM) and one coupled dark state magnetometer (CDSM), has worked successfully for more than 5 years providing continuous magnetic field measurements since the launch of the CSES in February 2018. After rechecking almost every year's data, it has become possible to make an improvement to the in-flight intrinsic calibration (to estimate offsets, scale values and non-orthogonality) and alignment (to estimate three Euler angles for the rotation between the orthogonalized sensor coordinates and the coordinate system of the star tracker) of the FGM. The following efforts have been made to achieve this goal: For the sensor calibration, FGM sensor temperature corrections on offsets and scale values have been taken into account to remove seasonal effects. Based on these results, Euler angles have been estimated along with global geomagnetic field modeling to improve the alignment of the FGM sensor. With this, a latitudinal effect in the east component of the originally calibrated data could be reduced. Furthermore, it has become possible to prolong the updating period of all calibration parameters from daily to 10 days, without the separation of dayside and nightside data. The new algorithms optimize routine HPM data processing efficiency and data quality. [ABSTRACT FROM AUTHOR]

Quinoa is an ancient crop that is nutritious, balanced, and suitable for most people. However, it has not been known and sought by the public until modern times. In recent years, quinoa has been intensively known and widely grown in China. Quinoa mechanized production machines have also been developed. With the help of DEM, we design and optimize the quinoa combined seeder, improving seeding efficiency and quality. In this paper, the basic physical and contact mechanics parameters required for the Hertz-Mindlin (no slip) of quinoa seeds in EDEM software were determined. The coefficient of rolling friction of quinoa seeds was calibrated on DEM, and which of Long Quinoa 4 is 0.043, and that of Meng Quinoa 4 was 0.016. Meanwhile, we can find that the coefficient of rolling friction strengthens the shape to restrict rolling and weakens the shape to increase rolling. The above research provides simulation parameters for the design and optimization of quinoa seed seeding machinery. [ABSTRACT FROM AUTHOR]

The GaoFen-14 satellite is primarily utilized for global high-precision positioning and generating 1:10,000 scale geographic information products, making it one of the most accurate stereo mapping satellites in China. With a long-stitched CCD (charge couple device) consisting of nine chips for the forward view camera and six chips for the backward view camera, it is crucial to validate the satellite's geometric performance and achieve high uncontrolled global positioning precision. In this study, two calibration models were proposed for the on-orbit calibration of the GaoFen-14 satellite: one accounting for the inter-chip geometry constraint and another without the constraint. Correspondingly, five calibration schemes (A, B, C, D, and E) were designed, with varying settings of the external calibration parameters, interior parameters, and utilization of the optical axis measurement data. Schemes A and B utilized the same set of alignment angles for the forward and backward images and differed in the set of interior calibration parameters. Scheme A took a single set of interior calibration parameters for all chips within the same view, while Scheme B set independent interior calibration parameters for each chip in both the forward and backward views considering the inter-chip geometry constraint. Schemes C, D, and E set two groups of alignment angles for the forward and backward views, respectively. In addition, Schemes C, D, and E utilized GaoFen-14′s specific real-time recording data of the optical axis, incorporating a slight revision of the alignment angle calibration value using dichotomous search. The interior models of Schemes A and C were identical, and those of Schemes B and D were the same. In comparison with scheme D, scheme E only lacks the inter-chip geometry constraint. These five schemes were taken to validate the most suitable exterior model and interior model for the GaoFen-14 satellite. Calibration experiments were conducted using original multichip images and the stitched images from the Zhongwei, Ningxia, and Songshan (Henan) test fields. Additionally, four sets of data from around the world are used to verify the calibration effect. The results demonstrated significant improvements in uncontrolled positioning accuracy for all five calibration schemes, transitioning from the 100 m level to the meter or even the submeter level. However, Schemes A and B exhibited unstable controlled positioning accuracy, with fluctuations ranging from 1 m to 5 m. In contrast, Schemes C, D, and E achieved substantial enhancements in uncontrolled positioning accuracy, maintaining stability with variations ranging from 0.94 to 1.14 m in the X direction, 0.41 to 0.48 m in the Y direction, and 0.77 to 0.81 m in the Z direction. The positions of the CCD probes obtained from Schemes C and D demonstrated consistency, with over 80% of the probes falling within 0.1 pixels, and all of the sampled probes within 0.3 pixels. However, Scheme D behaves better in describing the geometric deformation of each CCD chip and keeping the integrity of the long-stitched CCD simultaneously. These experimental results validate the GaoFen-14 satellite's ability to achieve stable uncontrolled positioning accuracy worldwide and a stable geometric structure between multichips. For the GaoFen-14 satellite, it is more appropriate to adopt two sets of alignment angles for the forward and backward views, respectively. As the satellite's on-orbit operation time increases, it is recommended to employ Scheme D for monitoring inconsistent local geometric deformation in future calibration work. [ABSTRACT FROM AUTHOR]

The Mars Surface Composition Detector (MarSCoDe) is a remote sensing instrument mounted on the front deck of the Zhurong rover in China's Tianwen-1 mission. The MarSCoDe adopts Laser-Induced Breakdown Spectroscopy (LIBS), along with Short Wave Infrared Spectroscopy (SWIR) and a telescopic micro-imager, to perform in situ detection of the chemical composition of soils, rocks, and minerals on the Martian surface. Since the MarSCoDe LIBS system works in extraterrestrial environments, it is important to equip the system with a set of onboard calibration targets, which are used for assessing the real-time performance of the instrument under various environmental conditions and conducting instrumental response calibration. Twelve dedicated LIBS reference samples were embedded as the MarSCoDe calibration target (MCCT) set, which plays a critical role in LIBS calibration before conducting LIBS analysis. This paper elaborates on the selection, development, characterization and testing of the MCCT set. The underlying scientific reasons and technical requirements that determine the selection of MCCT samples are introduced. The development procedures and mechanical performance test of both the calibration samples and the assembly holder are presented. Then, a comparison of the MCCTs and the characterization and scientific testing are described. The LIBS spectra of the MCCTs collected in three different atmospheric scenarios, namely laboratory-simulated Martian, normal terrestrial, and in situ Martian atmosphere, were investigated. The laboratory results and in situ behaviour show that the MarSCoDe instrument and the MCCT set can soundly adapt to the Martian environment with sufficient performance, as indicated by the fact that the spectral lines of the main elements in the calibration targets can be well identified and distinguished, including Ti, Si, Al, Fe, Mg, P, Ca, Na, K, O, C, H, S, etc. The MCCT samples provide a good reference for analysing Martian surface material composition and formulating the transfer relationship between the LIBS spectra measured in different atmospheric environments. [ABSTRACT FROM AUTHOR]

Aims and objectives: To investigate and analyse the prevalence of depression among patients with lung cancer, identify risk factors of depression, and develop a visual, non‐invasive, and straightforward clinical prediction model that can be used to predict the risk probability of depression in patients with lung cancer quantitatively. Background: Depression is one of the common concomitant symptoms of patients with lung cancer, which can increase the risk of suicide. However, the current assessment tools cannot combine multiple risk factors to predict the risk probability of depression in patients. Design: A cross‐sectional study. Methods: The clinical data from 297 patients with lung cancer in China were collected and analysed in this cross‐sectional study. The clinical prediction model was constructed according to the results of the Chi‐square test and the logistic regression analysis, evaluated by discrimination, calibration, and decision curve analysis, and visualised by a nomogram. This study was reported using the TRIPOD checklist. Results: 130 patients with lung cancer had depressive symptoms with a prevalence of 43.77%. A visual prediction model was constructed based on age, disease duration, exercise, stigma, and resilience. This model showed good discrimination at an AUC of 0.842. Calibration curve analysis indicated a good agreement between experimental and predicted values, and the decision curve analysis showed a high clinical utility. Conclusions: The visual prediction model developed in this study has excellent performance, which can accurately predict the occurrence of depression in patients with lung cancer at an early stage and assist the medical staff in taking targeted preventative measures. Relevance to clinical practice: The visual, non‐invasive, and simple nomogram can help clinical medical staff to calculate the risk probability of depression among patients with lung cancer, formulate personalised preventive care measures for high‐risk groups as soon as possible, and improve the quality of life of patients. [ABSTRACT FROM AUTHOR]

Aims This study aimed to develop a nomogram model for predicting prolonged mechanical ventilation (PMV) in patients undergoing cardiovascular surgery. Methods and results In total, 693 patients undergoing cardiovascular surgery at an Affiliated Hospital of Nantong University between January 2018 and June 2020 were studied. Postoperative PMV was required in 147 patients (21.2%). Logistic regression analysis showed that delirium [odds ratio (OR), 3.063; 95% confidence interval (CI), 1.991–4.713; P  < 0.001], intraoperative blood transfusion (OR, 2.489; 95% CI, 1.565–3.960; P  < 0.001), obesity (OR, 2.789; 95% CI, 1.543–5.040; P  = 0.001), postoperative serum creatinine level (mmol/L; OR, 1.012; 95% CI, 1.007–1.017; P  < 0.001), postoperative serum albumin level (g/L; OR, 0.937; 95% CI, 0.902–0.973; P  = 0.001), and postoperative total bilirubin level (μmol/L; OR, 1.020; 95% CI, 1.005–1.034; P  = 0.008) were independent risk factors for PMV. The area under the receiver operating characteristic curve for our nomogram was found to be 0.770 (95% CI, 0.727–0.813). The goodness-of-fit test indicated that the model fitted the data well (χ2 = 12.480, P  = 0.131). After the model was internally validated, the calibration plot demonstrated good performance of the nomogram, as supported by the Harrell concordance index of 0.760. Decision curve analysis demonstrated that the nomogram was clinically useful in identifying patients at risk for PMV. Conclusion We established a new nomogram model that may provide an individual prediction of PMV. This model may provide nurses, social workers, physicians, and administrators with an accurate and objective assessment tool to identify patients at high risk for PMV after cardiovascular surgery. [ABSTRACT FROM AUTHOR]

Objective: The implementation of more active anticoagulant prevention and treatment measures has indeed led to a significant reduction in the incidence of perioperative deep vein thrombosis (DVT) among patients with bone trauma. However, it is important to note that despite these efforts, the incidence of DVT still remains relatively high. According to the Caprini score, all patients undergoing major orthopedic surgery were defined as the high-risk group for DVT. Stratifying the risk further within high-risk groups for DVT continues to present challenges. As a result, the commonly used Caprini score during the perioperative period is not applicable to orthopedic patients. We attempt to establish a specialized model to predict postoperative DVT risk in patients with femoral fracture. Methods: We collected the clinical data of 513 patients undergoing femoral fracture surgery in our hospital from May 2018 to December 2019. According to the independent risk factors of DVT obtained by univariate and multivariate logistic regression analysis, the corresponding nomogram model was established and verified internally. The discriminative capacity of nomogram was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The calibration curve used to verify model consistency was the fitted line between predicted and actual incidences. The clinical validity of the nomogram model was assessed using decision curve analysis (DCA) which could quantify the net benefit of different risk threshold probabilities. Bootstrap method was applied to the internal validation of the nomogram model. Furthermore, a comparison was made between the Caprini score and the developed nomogram model. Results: The Caprini scores of subjects ranged from 5 to 17 points. The incidence of DVT was not positively correlated with the Caprini score. The predictors of the nomogram model included 10 risk factors such as age, hypoalbuminemia, multiple trauma, perioperative red blood cell infusion, etc. Compared with the Caprini scale (AUC = 0.571, 95% CI 0.479–0.623), the calibration accuracy and identification ability of nomogram were higher (AUC = 0.865,95% CI 0.780–0.935). The decision curve analysis (DCA) indicated the clinical effectiveness of nomogram was higher than the Caprini score. Conclusions: The nomogram was established to effectively predict postoperative DVT in patients with femoral fracture. To further reduce the incidence, more specialized risk assessment models for DVT should take into account the unique risk factors and characteristics associated with specific patient populations. [ABSTRACT FROM AUTHOR]

Heartwood, sapwood, and bark constitute the main components of the tree stem. The stem is the main component of the tree and plays an important role in supporting the tree and transporting nutrients and water. Therefore, quantifying the profiles of heartwood, sapwood, and bark is fundamental to understanding the different components of the tree stem. A seemingly unrelated mixed-effect model system was developed based on 179 destructively sampled trees for 31 permanent sample plots in Korean larch plantation in Northeast China. The heartwood radius and sapwood width were estimated and calibrated only by the observed bark thickness or by all response variables considering the correlations of submodel random effects. The results indicated that the model system achieved good fitting performance and prediction. In addition, after including one to ten bark thickness points and all response variables of sampling below the 2 m height of the tree, the estimated best linear predictor (EBLUP) for local calibration improved the prediction performance, indicating that the heartwood radius and sapwood width could be effectively calibrated by bark thickness while keeping intact the complete inner structure inside the stem. The results provided important information for forest managers and ecologists when selecting appropriate approaches for quantifying the profiles of heartwood, sapwood, and bark. [ABSTRACT FROM AUTHOR]

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world's first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data. [ABSTRACT FROM AUTHOR]

Adopting new technologies to calibrate hydrological models to produce good simulations for water resources or flood risk management is one of the important research topics in hydrology field. Incorporating hydrological signatures and concepts in hydrological model calibration becomes prevalent in recent years. A good realistic simulation of hydrological processes within the catchment is more significant than only achieving good performance at watershed outlet with single-statistical-objective function. This research adopts the concept of flood scaling property, which describes the statistical relationship between flood peak, its contributing areas and additional catchment attributes, to behave as one of the constraints under multi-objective model calibration framework. Several designed calibration scenarios are tested by employing the soil and water assessment tool hydrological model. In comparison with single-objective calibration scenarios, multi-objective calibration method is recommended to obtain both good simulation at catchment outlet and sub-basins reaches. The multi-objective calibration method assists in improving the model performance in terms of flow duration curve and also in reducing the bias of long-term runoff ratio. The proposed model calibration approach reflects the hydrological interaction of flood peaks across sub-basins and takes into account the catchment antecedent wetness and climatic condition for each flood event. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to modify and test the psychometric properties of the Chinese version of the stressor scale for emergency nurses. Design: The methodological design was carried out in two phases: (a) form the Chinese version by Delphi method and (b) test the psychometric properties by cross‐sectional survey. Methods: The translated scale was administered to 420 nurses in Qingdao. Validity was assessed in terms of content validity, calibration correlation validity and construct validity using exploratory factor analysis and confirmatory factor analysis. Internal consistency was estimated using Cronbach α coefficients. Results: The Chinese version of the SSEN retains 27 items, four common factors were extracted by exploratory factor analysis, and the factor cumulative variance contribution rate was 78.463%. The fitting indexes of the four‐factor model of CFA were all in the acceptable range[χ2 = 711.30, df = 312, p < 0.001, χ2/df = 2.280, CFI = 0.933, TLI = 0.924, IFI = 0.933, RMSEA = 0.079 (90% confidence interval = 0.071–0.086)].The item‐level content validity index of the Chinese SSEN is 0.89 ~ 1.00; the scale‐level content validity index is 0.98; the Cronbach α coefficient of the total table is 0.971 and the split‐half reliability is 0.877. Patient or Public Contribution: The C‐SSEN can be used to help nursing managers accurately formulate management measures to improve the stress coping ability of nurses in the ED, stabilize the nursing team and ensure nursing safety. [ABSTRACT FROM AUTHOR]

Objective To validate the World Health Organization (WHO) non-laboratory-based cardiovascular disease risk prediction model in regions of China. Methods We performed an external validation of the WHO model for East Asia using the data set of China Kadoorie Biobank, an ongoing cohort study with 512 725 participants recruited from 10 regions of China from 2004-2008. We also recalculated the recalibration parameters for the WHO model in each region and evaluated the predictive performance of the model before and after recalibration. We assessed discrimination performance by Harrell's C index. Findings We included 412 225 participants aged 40-79 years. During a median follow-up of 11 years, 58 035 and 41 262 incident cardiovascular disease cases were recorded in women and men, respectively. Harrell's C of the WHO model was 0.682 in women and 0.700 in men but varied among regions. The WHO model underestimated the 10-year cardiovascular disease risk in most regions. After recalibration in each region, discrimination and calibration were both improved in the overall population. Harrell's C increased from 0.674 to 0.749 in women and from 0.698 to 0.753 in men. The ratios of predicted to observed cases before and after recalibration were 0.189 and 1.027 in women and 0.543 and 1.089 in men. Conclusion The WHO model for East Asia yielded moderate discrimination for cardiovascular disease in the Chinese population and had limited prediction for cardiovascular disease risk in different regions in China. Recalibration for diverse regions greatly improved discrimination and calibration in the overall population. [ABSTRACT FROM AUTHOR]

Objective: To analyze the factors related to postoperative recurrence in patients with localized laryngeal amyloidosis (LocLA) and to construct a nomogram prediction model (NPM). Methods: We collected the data for LocLA patients diagnosed from March 2000 to May 2019 and clinical characteristics data were extracted. Factors related to recurrence were analyzed using multivariate logistic regression. The NPM was constructed for predicting the recurrence risk of LocLA. The receiver operating characteristic (ROC) curve evaluated the distinguishing ability using the area under curve (AUC). The calibration curve was created to evaluate the consistency of the NPM. Results: A total of 226 confirmed LocLA cases were included. One hundred seventy-five cases (77.4%) had localized single nodule, and 51 cases had more than one lesions. Sixty-three (27.9%) cases had no multinucleated giant cell (MGC) around amyloid, and 163 (72.1%) cases had MGC around amyloid. Multivariate logistic regression analysis showed that more than one lesions (odds ratio [OR] = 3.206 and 95% confidence interval [CI]: 1.492-6.888; P value:.003), subglottic involvement (OR = 2.926 and 95% CI: 1.300-6.585; P =.010), and no multinucleated giant cell (MGC) around amyloid (OR = 2.503 and 95% CI: 1.173-5.342; P =.018) had a statistically significant effect on postoperative LocLA recurrence (P <.05). The AUC of the ROC curve was 0.753 (95% CI: 0.667-0.832). The bias-corrected curve approached the ideal curve, with an average absolute error of 0.037. Conclusions: More than one lesions, subglottic involvement, and no MGC around amyloid are risk factors for postoperative recurrence of LocLA. The NPM constructed has good applicability. [ABSTRACT FROM AUTHOR]

This research and collaboration work aims at the calibration and validation (Cal/Val) of the Chinese HY-2B satellite altimeter based upon two permanent Cal/Val facilities: (1) the China Altimetry Calibration Cooperation Plan in Qingdao, Bohai Sea and the Wanshan islands, China and (2) the permanent facility for altimetry calibration established by the European Space Agency in Crete, Greece. The HY-2B satellite altimeter and its radiometer have been calibrated and monitored using uniform, standardized procedures, as well as protocols and best practices, and they also built upon trusted and indisputable reference standards at both Cal/Val infrastructures in Europe and China. The HY-2B altimeter is thus monitored in a coordinated, absolute, homogeneous, long-term and worldwide manner. Calibration of altimeters is accomplished by examining satellite observations in open seas against reference measurements. Comparisons are established through precise satellite positioning, water level observations, GPS buoys and reference models (geoid, mean dynamic topography, earth tides, troposphere and ionosphere), all defined at the Cal/Val sites. In this work, the final uncertainty for the altimeter bias will be attributed to several individual sources of uncertainty, coming from observations in water level, atmosphere, absolute positioning, reference surface models, transfer of heights from Cal/Val sites to satellite observations, etc. Through this project, the procedures, protocols and best practices, originally developed in the course of the ESA FRM4ALT project, are updated, upgraded and followed at both Cal/Val facilities in Europe and China. All in all, the HY-2B satellite altimeter observes the sea level quite well and within its specifications. [ABSTRACT FROM AUTHOR]

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

Gravity Anomaly Correction (GAC) is a very important term in leveling data processing. In most cases, it is troublesome for field surveyors to measure gravity when leveling. In this paper, based on the complete Bouguer Gravity Anomaly (BGA) map of WGM2012, the feasibility of replacing in-situ gravity surveying in China is investigated. For leveling application, that is to evaluate the accuracy of WGM2012 in China. Because WGM2012 is organized with a standard rectangle grid, two interpolation methods, bilinear interpolating and Inverse Distance Weighted (IDW) interpolating, are proposed. Four sample areas in China, i. e., Hanzhong, Chengdu, Linzhi and Shantou, are selected to evaluate the systems bias and precision of WGM2012. Numerical results show the average system bias of WGM2012 BGA in west China is about -100.1 mGal (1 mGal = 10 5 m/s2) and the standard deviation is about 30.7 mGal. Tests in Shantou indicate the system bias in plain areas is about -130.4 mGal and standard deviation is about 6.8 mGal. All these experiments means the accuracy of WGM2012 is limited in high mountain areas of western China, but in plain areas, such as Shantou, WGM2012 BGA map is quite good for most leveling applications after calibrating the system bias. [ABSTRACT FROM AUTHOR]

Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies. [ABSTRACT FROM AUTHOR]

The classic Nihewan Fauna, as the representative of early Pleistocene Asian Land Mammal Age Nihewanian, has long been cited for bio- and chronostratigraphic correlation. However, its precise provenance and stratigraphic horizon have remained unsolved till now. The authors successfully extracted the vital information by rediscovering 30 of the original excavation localities. The fossils were catalogued with their provenance through an extensive field survey and comprehensive inspection of the Tianjin Natural History Museum collection. A review of the original description of these fossil localities, using satellite images, and subsequent lithological examination of the Xiashagou strata in the field verified the new findings. The survey produced the first stratigraphic profile calibrated with fossil horizons of the Nihewan formation in the Xiashagou section. Correlated with the published magnetostratigraphical profile of the section, an age of ∼2.4–1.8 Ma is estimated for the classic Nihewan Fauna. [ABSTRACT FROM AUTHOR]

The Zhurong rover of the China's first Mars Exploration Mission successfully landed on the northern plain of Mars and measured near‐infrared (NIR) reflectance spectra with the Mars Surface Composition Detector (MarSCoDe). There was only orbital NIR hyperspectral data before the success of the Zhurong and Perseverance rovers in 2021. The in situ spectral measurements by the rover face different problems when compared with orbital measurements in data analysis, and thus need a new process pipeline. We analyzed the factors affecting the quality of the NIR spectra acquired by the Zhurong rover, including temperature variation of the detector, non‐homogeneous properties of the calibration panels, the atmosphere transmission and Martian dust buildup on the calibration panels. We then identified that the Martian dust buildup on the calibration panels is the main factor affecting the spectra, causing significant discrepancies between the reflectance calibrated with the white and gray panels, respectively, and the systematically higher reflectance values of the Martian soils measured by the Zhurong rover than those by other missions. We proposed a spectral unmixing strategy to restore the radiance of clean calibration panels on the rover and then derived the reflectance spectra with an uncertainty of ∼14%. The method has been tested by simulation experiments in laboratory. The data quality of the in situ NIR spectra of the Zhurong rover is significantly improved after the calibrations based on our pipeline, which is critical for mineral identification on small scales and calibrations of the orbital remote sensing data. Plain Language Summary: The Zhurong rover successfully landed on the shoreline‐like boundaries in the northern lowlands of Mars, and has the capability of exploring the compositions there using near‐infrared reflectance spectra. This is the first attempt to obtain the in situ hyperspectral reflectance data of surface in the northern lowlands. So, the accurate data is very critical for compositional analysis, which provide an unprecedented opportunity to explore if the ancient ocean existed or not. We systematically analyzed the factors on the quality of in situ measured spectra and built a data process pipeline, which lays the foundation for the massive data analysis of the Zhurong rover. Key Points: The Zhurong rover in situ measured the near‐infrared hyperspectral reflectance data of surface in the northern lowlands of Mars for the first timeThe Martian dust buildup on the calibration panels significantly affect the shapes and intensities of reflectance spectra of Martian surfaceA data process pipeline for near‐infrared reflectance spectra acquired by the Zhurong rover is built [ABSTRACT FROM AUTHOR]

The Greenhouse Gas Monitoring Instrument (GMI), carried by Gaofen 5 (GF5-01), and the Hyperspectral Observation Satellite (GF5-02) were successfully launched on 9 May 2018, and September 7, 2021, respectively, and are the only passive greenhouse gas payloads in China that can regularly obtain effective detection data in-orbit at this stage. Before launch, the research team carried out much laboratory calibration work and designed an on-board calibration system based on solar radiation sources which guarantees the quantitative accuracy of the payload data to the greatest extent. In order to more effectively meet the high frequency calibration requirements over the whole life cycle of the payload, the research team carried out research using the on-track site calibration method based on digital calibration field network technology, and the obtained calibration coefficient effectively complements the laboratory and on-board calibration results. The working principle of the GMI is quite different from that of a traditional imaging payload. Spatial heterodyne spectroscopy (SHS) is used to detect the absorption spectrum of greenhouse gases, has a large field of view and is non-imaging and hyperspectral. The existing fixed-site alternative calibration methods cannot fully meet the requirements of calibration tasks. In this paper, we propose a set of global digital calibration radiation field screening criteria that can meet the characteristics of the GMI and design a method to calculate the site calibration coefficients of non-absorption spectral channels according to the characteristics of hyperspectral data. Based on the historical observation data of the GMI, the initial calibration calculation of the payload launch was carried out, and the calibration results of four spectral channels of the GMI were obtained: The calibration coefficient range of the O2 channel is 1.05–1.15, the mean value is 1.10 and the standard deviation is 2.72%; the calibration coefficient of the CO2-1 channel is 1.05–1.13, the mean value is 1.09 and the standard deviation is 2.64%; the calibration coefficient of the CH4 channel is 1.08–1.10, the mean value is 1.11 and the standard deviation is 2.73%; the calibration coefficient of the CO2-2 channel is 1.09–1.14, the mean value is 1.12 and the standard deviation is 2.93%. The above results show that the radiation performance of each channel of the GMI shows no significant attenuation during this period, that the site calibration coefficient has no significant fluctuation and that the in-orbit operation state is stable. [ABSTRACT FROM AUTHOR]

In recent years, with rapid increases in the number of vehicles in China, the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent. To achieve the precise control of emissions, on-road remote sensing (RS) technology has been developed and applied for law enforcement and supervision. However, data quality is still an existing issue affecting the development and application of RS. In this study, the RS data from a cross-road RS system used at a single site (from 2012 to 2015) were collected, the data screening process was reviewed, the issues with data quality were summarized, a new method of data screening and calibration was proposed, and the effectiveness of the improved data quality control methods was finally evaluated. The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%, which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles. The annual variability of emission factors of nitric oxide decreases by 60% – on average – eliminating the annual drift of fleet emissions and improving data reliability. [Display omitted] [ABSTRACT FROM AUTHOR]

The Approximate Bayesian Computation (ABC) provides a powerful tool for signature-domain calibration of hydrological models where hydrological signatures are incorporated into calibration objectives. However, the efficiency of ABC relies strongly on the use of a vector of sufficient signatures that can fully represent relevant information in raw data. The application of ABC with randomly chosen signatures can result in inaccurate calibration results. To fill this gap, a hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models is proposed. In this framework, a set of approximately sufficient signatures is pursued through simultaneous consideration of the information redundancy analysis (IRA) and discriminatory power analysis (DPA) procedures. While the IRA deals with the information redundancy inherent in the pool of available signatures, DPA quantifies the discriminatory power of a given signature as the reliability and sharpness of the associated probabilistic predictions generated by ABC. The verified residual error scheme in time-domain inference is approximated as the probabilistic model in the acceptance test of ABC. The proposed framework is then tested on the Xin'anjiang rainfall-runoff model applied to the Ren River basin (RRB) of China. The use of IRA and DPA provides a probabilistic model prediction statistically equivalent to that of classical time-domain inference in terms of the reliability and sharpness. The comparison to signature-domain inference using the complete set of hydrological signatures further demonstrates the importance of IRA and DPA in improving the quality of Bayesian model calibration in the signature domain and reducing the total predictive uncertainty. The framework makes it practically possible to maintain adequate accuracy of model predictions produced by signature-domain inference, improving the efficiency of ABC in solving the model calibration problems and consequently promotes the use of ABC in signature-domain model calibration. [ABSTRACT FROM AUTHOR]

C-SAR/01, the successor of China's Gaofen-3 Satellite, which launched on 23 November 2021, is the first synthetic aperture radar (SAR) satellite to be launched in China's civil space infrastructure plan and has served as an invaluable data resource. Radiometric calibration and validation are prerequisites for the quantitative application of SAR data. In this study, the radiometric calibration experiments of C-SAR/01 data of the ultra-fine strip (UFS) and fine strip I (FSI) modes were conducted applying flexible automatic triangular trihedral corner reflectors deployed in Xilinhot SAR satellite calibration and validation site. Accordingly, the image quality and radiometric calibration accuracy were evaluated. The results show that the spatial resolution, peak sidelobe ratio, and integrated sidelobe ratio of UFS and FSI mode data of C-SAR/01 are better than those of the design indexes, and the calibration results from the integral method are more stable than those from the peak method. Furthermore, the standard deviation of the calibration constant for UFS mode data is 0.234 dB, with the relative and absolute calibration accuracies obtained as 0.233 and 0.532 dB, respectively, whereas the standard deviation calibration constant for FSI mode data is 0.198 dB, with its relative and absolute calibration accuracies evaluated as 0.199 and 0.333 dB, respectively. [ABSTRACT FROM AUTHOR]

Background: Little is known about patients' intention for participation in end-of-life decisions (EOLD) in three coastal provinces in southern China. This study aimed to explore the willingness of patients with cancer pain to participate in EOLD and potential influencing factors. Methods: A multi-center cross-sectional study was performed in three coastal provinces in southern China. Two hundred and thirty patients with cancer pain were recruited and consented to fill out the questionnaires. The patients' willingness to participate in EOLD, demographic and disease-related data was surveyed. Results: In total, 223 patients completed and returned the survey (response rate = 96.95%). One hundred four cases (46.64%) were willing to participate in EOLD. 119 (54.36%) cases not willing to participate in EOLD, respectively. Multivariate logistic regression analysis shows that educational level (OR: 0.683, 95% CI: 0.482–0.966), history of alcoholism (OR: 8.353, 95%CI: 2.535–27.525), Eastern Cooperative Oncology Group (ECOG) score (OR: 0.645, 95% CI: 0.450–0.925) and experience of explosive pain (OR: 6.367, 95% CI: 3.103–13.062) and clinical rescue (OR: 3.844, 95% CI: 1.722–8.577) had significant effects on EOLD intention (P < 0.05). Finally, a predictive model combined above five factors was established, which showed a good discrimination (area under receiver operating characteristic curve: 0.849, 95% CI: 0.796–0.899, P < 0.001) and calibration (Hosmer-Lemeshow Test: Chi-square = 10.103, P = 0.258) for which patients more willing to participate in EOLD. Conclusions: The willingness of patients with cancer pain to participate in EOLD is at a modest level in three coastal provinces in southern China. Patients with lower educational level, history of alcoholism, better health status and experience of explosive pain and clinical rescue may be more prone to participate in EOLD. [ABSTRACT FROM AUTHOR]

Simple Summary: Endometrial cancer is a common gynecological malignancy, many patients have early clinical symptoms. However, disease progression and even death can occur in some cases after initial treatment. Identifying convenient and effective markers to predict the prognosis of patients with endometrial cancer and guide the selection of treatment options and patient management is urgently needed. In this study, the fibrinogen, albumin, NLR, and CA125 levels as well as other clinicopathological features of patients with endometrial cancer were recorded. Nomograms for predicting endometrial cancer prognosis created by combining fibrinogen, albumin, NLR, and CA125 levels were constructed, verified and evaluated, and the nomograms are highly accurate and have clinical application value, which provided a convenient method for predicting the prognosis of patients undergoing surgical treatment for endometrial cancer. Background: This study aimed to determine the prognostic value of the preoperative levels of fibrinogen, albumin (ALB), neutrophil–lymphocyte ratio (NLR), and carbohydrate antigen 125 (CA125) in endometrial cancer and to establish nomograms for predicting patient survival. Methods: Patients with endometrial cancer (n = 1483) who underwent surgery were included in this study, and their preoperative fibrinogen, ALB, NLR, and CA125 levels and clinicopathological characteristics were collected. Patients were randomized into a training cohort (70%, n = 1038) and an external validation cohort (30%, n = 445). The Cox regression analysis was performed using the data for the patients in the training cohort to identify independent prognostic factors; nomograms for predicting prognosis were established and validated. Results: High fibrinogen (≥3.185 g/L), NLR (≥2.521 g/L), and CA125 (≥35 U/mL) levels and low ALB (<4.185 g/L) levels were independently associated with poor progression-free survival (PFS) and poor overall survival (OS) in patients with endometrial cancer. Prognostic prediction model nomograms were developed and validated based on these results. Calibration curves and C-indexes underscored the good predictive power of the nomograms, and both the net reclassification index (NRI) and integrated discrimination improvement (IDI) values of the prognostic prediction model nomograms were improved. Conclusions: Nomograms that are developed based on preoperative fibrinogen, ALB, NLR, and CA125 levels accurately predict PFS and OS in patients with endometrial cancer. [ABSTRACT FROM AUTHOR]

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring. [ABSTRACT FROM AUTHOR]

Chang'E-3 is China's first soft landing mission on an extraterrestrial celestial body. The laser Three-Dimensional Imaging (TDI) sensor is one of the key payloads of the Chang'E-3 lander. Its main task is to provide accurate 3D lunar surface information of the target landing area in real time for the selection of safe landing sites. Here, a simplified positioning model was constructed, to meet the accuracy and processing timeline requirements of the TDI sensor of Chang'E-3. By analyzing the influence of TDI intrinsic parameters, a permanent outdoor calibration field based on flat plates was specially designed and constructed, and a robust solution of the geometric calibration adjustment was realized by introducing virtual observation equations for unknowns. The geometric calibration and its absolute and relative positioning accuracy verification were carried out using multi-measurement and multi-angle imaging data. The results show that the error of TDI intrinsic parameters will produce a false obstacle with a maximum height of about 1.4 m on the plane, which will cause the obstacle avoidance system of Chang'E-3 to fail to find a suitable landing area or find a false flat area. Furthermore, the intrinsic parameters of the TDI have good stability and the accuracy of the reconstructed three-dimensional surface can reach about 4 cm after error calibration, which provides a reliable terrain guarantee for the autonomous obstacle avoidance of the Chang'E-3 lander. [ABSTRACT FROM AUTHOR]

In order to estimate the adaptability of Agricultural Production Systems Simulator (APSIM)-Maize model under the special climate environment in tropic, 10 field experiments were conducted at different seasons in three sites (Longchuan, Mangshi and Ruili) of tropic in Yunnan Province, China. The parameters of APSIM model were calibrated and its adaptability was validated. The results showed that the days from sowing to flowering and sowing to maturity were predicted accurately for all sites with mean errors of 2.0 ± 0.4, and 3.2 ± 0.7 d respectively. The normalized root mean square errors (NRMSE) of the model for yield prediction were 2%, 3% and 5% for each of three sites, respectively, which indicated that the APSIM model had good accuracy and sensitivity in predicting phenological phases and yield of maize (Zea mays L.) grown in different seasons, and the model had good adaptability in the tropic of Southwest China. This study provided the basis and technical support for evaluating maize production potential based on the model. [ABSTRACT FROM AUTHOR]