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Abstract Background Suicide was an important cause of death in prostate cancer. This study intended to investigate trends in suicide mortality among prostate cancer (PCa) survivors from 1975 to 2019 in the United States. Method We identified PCa survivors from the Surveillance, Epidemiology, and End Results (SEER) program from January 1975 to December 2019. Standardized mortality rate (SMR) was calculated d to assess the relative risk of suicide in PCa survivors compared with the general men population. Poisson regression model was performed to test for trend of SMRs. The cumulative mortality rate of suicide was calculated to assess the clinical burden of suicide mortality. Results 7108 (0.2%) cases were death from suicide cause, and 2,308,923(65.04%%) cases recorded as dying from non-suicidal causes. Overall, a slightly higher suicide mortality rate among PCa survivors was observed compared with general male population (SMR: 1.15, 95%CI: 1.09–1.2). The suicide mortality rate declined significantly relative to the general population by the calendar year of diagnosis, from an SMR of 1.74(95%CI: 1.17–2.51) in 1975–1979 to 0.99(0.89–1.1) in 2015–2019 (Ptrend 0.05). The cumulative suicide mortality during 1975–1994 was distinctly higher than in 1995–2019(P

Abstract Background This study aimed to investigate the relationship between serum testosterone levels and the risk of congestive heart failure (CHF) in adult males. Previous research has suggested a potential link between serum testosterone and cardiovascular health, but the findings have been inconclusive. Methods This study was cross-sectional, and the data were obtained from the 2011–2016 cycle of the National Health and Nutrition Examination Survey (NHANES), which included a sample of 6,841 male participants. Serum testosterone levels were measured using a standardized assay, and CHF status was assessed through self-reporting. Covariates such as age, ethnicity, lifestyle factors, and health conditions were considered in the analysis. Results Among the participants, 242 individuals had a documented history of CHF. We observed a linear correlation between serum testosterone levels and CHF occurrence, with higher serum testosterone levels associated with a decreased risk of CHF (Q4 vs. Q1, OR = 0.29, 95% CI: 0.19–0.47, P

This study aims to quantitatively assess the impact of the COVID-19 pandemic on societal electricity consumption behavior, particularly considering the interference of special events. Currently, there is insufficient quantitative analysis of the extent to which the COVID-19 pandemic affects electricity consumption behavior. Therefore, we endeavor to introduce new theories and methods to comprehensively understand the potential impact of the pandemic on electricity usage in this field. Our proposed machine learning methods demonstrate significant results and advantages in two key aspects. Firstly, we innovatively introduce an abnormality detection algorithm based on the lunar calendar, thereby establishing a detection system capable of accurately identifying abnormal fluctuations in electricity consumption, to precisely determine the onset of the pandemic. Secondly, we design an evaluation system that integrates CNN-LSTM predictive models and controlled variable strategies, enabling us to reconstruct electricity usage patterns less affected by the pandemic. By comparing actual electricity consumption with reconstructed patterns, we deeply evaluate the impact of the pandemic on different regions and industries and introduce the pandemic impact percentage as a quantification method to precisely assess the extent of the impact. The findings indicate a significant impact of the COVID-19 pandemic on commercial and industrial electricity consumption. Furthermore, our methods are not limited to pandemic research but can also be applied to investigate the effects of other events such as holidays, typhoons, and special production schedules on electricity consumption. In summary, by delving into the complex associations underlying electricity consumption, this study provides a solid theoretical and methodological foundation for future similar research and offers crucial decision support for the electricity industry and emergency management.

Abstract Purpose To investigate prognostic values of prostatic urethra involvement (PUI) and construct a prognostic model that estimates the probability of cancer-specific survival for T1 bladder cancer patients. Method and materials We investigated the national Surveillance, Epidemiology, and End Results (SEER) database (2004–2015) to get patients diagnosed with T1 bladder cancer. An external validation cohort was obtained from the First Affiliated Hospital of Nanchang University. The Kaplan–Meier method with the log-rank test was applied to assess cancer-specific survival (CSS) and overall survival (OS). Moreover, the propensity score matching (PSM) and multivariable Cox proportional hazard model were performed. All patients were randomly divided into the development cohort and validation group at the ratio of 7:3. The performance of the model was internally validated by calibration curves and the concordance index (C-index). Results The PUI group had a lower survival rate of both CSS and overall survival OS before and after PSM when compared to non-involved patients (All P

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting.

Background and aimsThe Visceral Adiposity Index (VAI) is a straightforward and gender-specific marker that combines anthropometric measurements with lipid profiles. The objective of this study was to evaluate the relationship between VAI and coronary heart disease (CHD).Methods and resultsThe study examined data collected from adults during the NHANES 1999-2018 cycle. The analyses were weighted, and multivariable logistic regression models were employed to investigate the association between VAI and CHD. Additionally, subgroup analyses stratified by age were conducted. To evaluate the impact of VAI levels on survival outcomes, the study utilized the Kaplan-Meier method and performed the log-rank test to evaluate the survival outcome of participants with different VAI levels. The study findings revealed a significant association between VAI and CHD, indicating a non-linear relationship where an increase in VAI was associated with an elevated risk of CHD. High levels of VAI were linked to an increased prevalence of CHD (Q4 vs Q1, OR 1.50, 95% CI 1.12-2.01, P=0.01). Additionally, higher levels of VAI were associated with a poorer overall prognosis in terms of survival outcomes. There were no statistically significant differences in survival outcomes among the population with CHD.ConclusionThe results of this study highlighted a significant association between VAI and CHD, with a non-linear relationship observed. High VAI levels were associated with an increased risk of CHD and poor survival outcomes, emphasizing the importance of understanding and managing this risk factor, particularly in older age groups.

Abstract Background Renal cell carcinoma (RCC) survival has improved due to recent developments in RCC treatment. Therefore, other co-morbid conditions may have a more critical role. This study aims to explore the common causes of death in patients with RCC to improve the management and survival of RCC. Method We used the Surveillance, Epidemiology, and End Results (SEER) (1992–2018) database to get patients with RCC. We calculated the percentage of total deaths of six kinds of the cause of death (COD) and the cumulative incidence of death for each selected cause over survival time. The joinpoint regression was utilized to present the trend of mortality rate by COD. Results We enrolled 107,683 cases with RCC. RCC was the leading cause of death in patients with RCC [25376(48.3%)], followed by cardiovascular diseases [9023(17.2%)], other cancers [8003 (15.2%)], other non-cancer diseases [4195 (8%)], non-disease cause [4023 (7.7%)], and respiratory diseases [1934 (3.6%)]. The proportion of patients who died of RCC decreased gradually over survival time, and this value decreased from 69.71% in 1992–1996 to 38.96% in 2012–2018. The non-RCC cause mortality rate showed an increasing trend, whereas a slight decrease was observed in RCC specific mortality rate. The distribution of such conditions varied across different patient populations. Conclusion RCC was still the primary COD of patients with RCC. However, non-RCC cause death was increasingly important among RCC patients in recent two decades. Cardiovascular disease and other cancers were crucial co-morbidities that required significant attention in the management of RCC patients.

With the large-scale development of distributed energy on the demand side, the trend of “supply exceeding demand” has gradually become prominent, and regional peer-to-peer (P2P) energy trading has become an important measure to improve the local consumption of distributed energy. However, most existing studies usually assume that prosumers behave entirely rationally with the goal of maximum benefit, which has been proved to deviate from the observed actual behaviors. Aiming at the optimal energy of prosumers participating in the P2P market, a prospect theory-based two-stage stochastic optimization model considering the bounded rationality was proposed to accurately simulate the decision-making behavior. Then, a benefit maximization model for the energy trading service provider (ETSP) was constructed considering the power flow constraint to ensure the safe operation of the system. Finally, an improved R-ADMM algorithm considering timeout was proposed to solve the above model and improve the convergence speed. The effectiveness of the proposed model and algorithm was verified via simulation.

Abstract Charging resources are scarce due to the limited capacity of charging stations and the congestion of distribution networks. Here, a hierarchical scheduling framework that considers the privacy of participants is established to optimize the charging power with a fair payment mechanism. The charging resource allocation model is established considering the scarcity of charging resources. Furthermore, the model is divided into three sub problems, including the congestion management problem of distribution network operator, the energy management problem of charging station and the charging mode problem of electric vehicle. Then the collaborative optimization algorithms are proposed to ensure the privacy of participants. Finally, a hierarchical payment mechanism based on the opportunity cost theory is proposed. The performance of the proposed hierarchical scheduling framework is evaluated using the Roy Billiton Test System with 2000 EVs. On the premise of ensuring distribution network economic operation, the proposed hierarchical scheduling strategy can satisfy the requirement for privacy protection and eliminate the overload problem caused by electric vehicle charging. The simulation results have shown that the additional capacity cost has an obvious levelling effect. With the application of the payment mechanism, charging resources is equitably allocated. Also, the equilibrium of a market and the stable operation of power system can be guaranteed.

Abstract Background Penile squamous cell carcinoma (PSCC) survival had no significant improvement since 1990 in the United States. This study aims to get insight into the changing trend and distribution of death causes of PSCC. The epidemiology of PSCC is also investigated. Methods The Surveillance, Epidemiology, and End Results (SEER) (1992–2018) database is utilized to get patients diagnosed with penile squamous cell carcinoma. The trend of incidence, distribution of age, changing trend and distribution of death cause, and survival outcome are analyzed for all PSCC patients and each race. Results Three thousand four hundred and twenty‐three male patients with PSCC are enrolled in our study. The age‐adjusted incidence rate of the white has a slight increase (Annual percent change [APC] = 0.647%). American Indian/Alaska Native men have the highest average annual incidence, while Asian /Pacific Islander men have the lowest. PSCC patients aged 70–80 are the most common, and patients over 80 years have the highest 3‐year (50%) and 5‐year (63.93%) mortality rate. Non‐cancer disease, especially circulatory system disease, is the most common cause of death, whereas the proportion of patients who died of PSCC significantly increased from 21.17% (1992–2001) to 41.3% (2012–2017) in PSCC patients (p

Background Previous studies have confirmed the higher risk of bladder cancer (BC) and rectal cancer (RC) development among prostate cancer (PCa) patients receiving radiotherapy. In this study, we intend to explore the long-term trend in second BC and RC incidence among PCa patients undergoing radiotherapy. Method We identified first primary PCa patients diagnosed between 1975 and 2014 from the Surveillance, Epidemiology, and End Results (SEER)-9 cancer registries. Standardized incidence ratios (SIRs) were calculated by calendar year of diagnosis among PCa patients receiving radiotherapy and not. P trends were evaluated using Poisson regression. 10-year cumulative incidence of BC and RC was calculated utilizing competing risk regression model. Result Of PCa patients treated with radiotherapy, SIRs of BC increased from .82 (95% CI: .35- 1.61) in 1980–1984 to 1.58 (95% CI: 1.48–1.68) in 2010-2014 ( Ptrend =.003). SIRs of RC increased from 1.01 (95% CI: .27-2.58) in 1980–1984 to 1.54 (95% CI: 1.31–1.81) in 2010-2014 ( Ptrend =.025). No statistically significant change in both BC and RC incidence was observed. The 10-year cumulative incidence of BC increased from 1975–1984 (.04%) to 2005–2014 (.15%) among PCa treated with radiotherapy. Simultaneously, the 10-year cumulative incidence of RC was demonstrated to range from 1975–1984 (.02%) to 2005–2014 (.11%). Conclusion we have observed an increasing trend in second BC and RC incidence in PCa patients receiving radiotherapy. There was no significant change in the incidence of second BC and RC in PCa without radiotherapy. These results reflect the increasing clinical burden of second malignant tumors in PCa patients undergoing radiotherapy.

ObjectiveTo compare the efficacy and safety between conservative treatment and surgery for the patients with small (1-3cm) nonfunctional adrenal incidentaloma (NFAI).MethodsThe patients with small (1-3cm) NFAI who received conservative treatment or surgery in our hospital from November 2018 to December 2019 were retrospectively collected. A total of 83 patients were included in this study. They were divided into two groups according to the treatment methods: the surgery group (n=51) and the conservative treatment group (n=32).Then patients’ demographics, tumor characteristics, functional indicators and complications were compared. Statistical analysis was performed using t-test for continuous variables and Pearson chi-square test or Fisher’s exact test for categorical variables.ResultsAt the time of diagnosis, after 3 months, after 6 months, after 12 months, and after 24 months, we found that there was no significant difference between the two groups in systolic blood pressure, diastolic blood pressure, serum potassium levels, and hormone levels. 51 patients chose to have surgery, of which 41 patients chose RLA and 10 patients chose RARLA. RARLA group patients had the highest total cost and conservative treatment group patients had the lowest cost, and the difference was significant (P < 0.001). There was no significant difference in tumor size in the conservative treatment group between at the time of diagnosis and after 24 months (P = 0.305).ConclusionSurgical treatment is more effective for 1-3cm NFAI, but conservative treatment is safer and more economical. Follow-up after conservative or surgical treatment is necessary.

BackgroundMetastatic bladder cancer (MBC) is an incurable malignancy, which is prone to early death. We aimed to establish models to evaluating the risk of early death in patients with metastatic bladder cancerMethodsThe data of 1,264 patients with MBC registered from 2010 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. We utilized X-tile software to determine the optimal cut-off points of age and tumor size in diagnosis. Univariate and multivariate logistic regression analyses were used to identify significant independent risk factors for total early death and cancer-specific early death, then we construct two practical nomograms. In order to validate our prediction models, we performed calibration plots, receiver operating characteristics (ROCs) curves, decision curve analysis (DCA) and clinical impact curve (CIC).ResultA total of 1,216 patients with MBC were included in this study. 463 patients died prematurely (≤3 months), and among them 424 patients died of cancer-specific early death. The nomogram of total premature death was created by surgery, chemotherapy, tumor size, histological type, liver metastases, and nomogram of cancer-specific early death was based on surgery, race, tumor size, histological type, chemotherapy, and metastases (liver, brain). Through the verify of calibration plots, receiver operating characteristics (ROCs) curves, decision curve analysis (DCA) and clinical impact curve (CIC), we concluded that nomogram were a valid tool with excellent clinical utility to help clinicians predict premature death in MBC patients.ConclusionsThe nomograms derived from the analysis of patients with MBC, which can provide refined prediction of premature death and furnish clinicians with useful ideas for patient-specific treatment options and follow-up scheduling.

Abstract Purpose To study prognostic values of bladder neck involvement (BNI) and survival outcomes in non‐muscle‐invasive bladder cancer (NMIBC). Method and materials The national Surveillance, Epidemiology, and End Results database (2004–2015) was applied to gain further insight into the prognostic values of BNI and 19,919 patients diagnosed with NMIBC were included in our study. We used the Kaplan–Meier method with the log‐rank test and subgroup analyses to evaluate cancer‐specific survival (CSS) and overall survival (OS). In addition, the multivariable Cox proportional hazard model and propensity score matching (PSM) were utilized. Results In all, 3446 patients with BNI and 16,473 patients with sites except for bladder neck were enrolled in our study. Compared with other sites, a tendency toward a higher proportion of higher grade (p 0.05). In addition, subgroups stratified by tumor size and grade all revealed poor prognosis of BNI in NMIBC patients. Moreover, better survival outcomes of OS were observed in BNI patients who received radical cystectomy (p = 0.02) or chemotherapy (p

ObjectiveTo investigate the association between radiotherapy and the risk of second malignant neoplasm (SMN) development among patients with bladder cancer (BC). Overall survival (OS) is compared among patients developing SMN and without.MethodWe identified patients diagnosed with BC from the Surveillance, Epidemiology, and End Results (SEER) database. The development of an SMN is defined as any SMN occurring more than 5 years after the diagnosis of BC. The Fine-Gray competing risk regression is used to estimate the probability of SMN. The radiotherapy-associated risk (RR) for SMNs is assessed by Poisson regression. The Kaplan–Meier method was used to evaluate the OS of patients with SMNs. Propensity score matching (PSM) is performed.ResultsA total of 76575 BC patients are enrolled in our study. The probability of SMNs in the radiotherapy cohort is statistically higher than in the non-radiotherapy cohort. In competing risk regression analysis, radiotherapy is proven to be associated with a higher risk of SMN (Hazard ratio: 1.23; 95% CI: 1.102–1.368). The radiotherapy-associated risks significantly increase in the radiotherapy cohort (RR: 1.28; 95% CI: 1.14–1.43). In site-specific analysis, statistically significant results are observed in lung and bronchus (LAB) cancer and hematological malignancies. The OS rate in patients developing SMN is significantly lower than that among matched patients with primary BC.ConclusionRadiotherapy for BC is associated with SMN. Radiotherapy increases the risk of secondary low-dose area cancer development, including LAB cancer or hematological malignancies. Notably, this effect is not observed in the high-dose area involving pelvic tumors. Patients developing SMN showed poorer OS.

BackgroundCancer survivorship care is an emerging and necessary component of oncology management. To explore cardiovascular disease (CVD)-specific mortality and prognostic factors among patients with penile squamous cell carcinomas (PSCC). These results aid clinicians in furtherly understand this disease’s prognosis.MethodWe analyzed Surveillance, Epidemiology and End Results Program data for 2668 PSCC cases diagnosed between 2005 to 2016. We calculated standardized mortality ratios (SMRs) of CVD and all-cause mortality, comparing PSCC patients with general population men. A cumulative mortality curve and competitive risk regression model were utilized to evaluate the prognostic factors of CVD-specific death.ResultsDeath distribution is as follows: PSCC (42.4%), other causes (21.3%) CVD (19%), and other cancers (17.3%). PSCC patients are more like to die from CVD (SMR=3.2, 95%CI: 3.1-3.3) and all-cause death compared with the general population. Meanwhile, patients undergoing surgery show a relatively higher CVD-specific mortality than the general population (SMR=2.7, 95%CI: 2.4-3.2). In the competitive risk model, higher CVD mortality is associated with age, region, year of diagnosis, stage, and marital status (all P

BackgroundThe effect of lymph node resection on the prognosis of bladder cancer (BLCA) patients receiving radical cystectomy should not be ignored. Our aim was to explore the prognostic value of the log odds of negative lymph nodes/T stage (LONT) and construct a more effective nomogram based on LONT to predict cancer-specific survival (CSS) in postoperative BLCA patients.MethodsPatients diagnosed with BLCA after radical cystectomy between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. We randomly split (7:3) these patients into the primary cohort and internal validation cohort. 86 patients from the First Affiliated Hospital of Nanchang University were collected as the external validation set. Univariate and multivariate cox regression analyses were carried out to seek prognostic factors of postoperative BLCA patients. According to these significantly prognostic factors, a simple-to-use nomogram was established for predicting CSS. Their performances were evaluated by using calibration curves, the concordance index (C-index), the receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). In addition, different risk groups were tested by Kaplan-Meier curves and log-rank tests.ResultWhether in cancer-specific survival (CSS) or overall survival (OS), LONT was an independent and significant prognostic factor. Through further screening, the ultimate nomogram of CSS was composed of nine independent prognostic factors including LONT, age, race, tumor size, histologic type, T stage, N stage, summary stage and chemotherapy. The C-index of nomogram in the primary cohort, internal and external validation cohort were 0.734, 0.720 and 0.728, respectively. The AUC of predicting CSS at 3 and 5 years were 0.783 and 0.774 in the primary cohort and 0.781 and 0.781 in the validation cohort. The results of calibration and DCA showed good concordance and clinical applicability. Significant differences (P < 0.05) were displayed in CSS among different risk groups.ConclusionLONT was regarded as a novel and reliable prognostic factor. Compared with the AJCC staging system, the established nomogram based on LONT can more effectively predict the prognosis of BLCA patients after radical cystectomy.

PurposeThe aim of this study is to investigate the trends in incidence and mortality, and explore any change in survival of penile cancer in the United States.MethodsWe obtained data from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2018) utilizing the SEER Stat software. The joinpoint regression was used to analyze the secular trend of incidence and incidence-based mortality (IBM) stratified by age, race, and summary stage. The 5-year relative survival rate was also calculated.ResultThe age-adjusted rates of penile cancer patients were 0.38 (0.37–0.39) and 0.21 (0.2–0.21) for overall incidence and IBM, respectively. The 5-year relative survival rates were 67.7%, 66.99%, and 65.67% for the calendar periods of 2000–2004, 2005–2009, and 2010–2014, respectively. No significant changes in incidence by era were observed from 2000 to 2018 [annual percentage change (APC) = 0.5%, p = 0.064]. The IBM rate of penile cancer showed an initial significant increase from 2000 to 2002 (APC = 78.6%, 95% CI, −1.7–224.6) followed by a deceleration rate of 4.6% (95% CI, 3.9–5.3) during 2002 to 2018. No significant improvement in 5-year relative survival was observed. The trends by age, race, and summary stage in incidence and IBM were significantly different.ConclusionThis study, using population-level data from the SEER database, showed an increasing trend in IBM and no significant improvement in the 5-year relative survival rate. Meanwhile, the incidence of penile cancer exhibited a relatively stable trend during the study period. These results might be due to the lack of significant progress in the treatment and management of penile cancer patients in the United States in recent decades. More efforts, like increasing awareness among the general population and doctors, and centralized management, might be needed in the future to improve the survival of this rare disease.

BackgroundMetastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC.MethodsThe SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA).ResultsThere were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively.ConclusionsThe nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

PurposeTo construct a prognostic model to predict the cancer-specific survival (CSS) for bladder cancer patients with lymph node-positive.Patients and MethodsWe enrolled 2,050 patients diagnosed with lymph node-positive bladder cancer from the Surveillance Epidemiology and End Results (SEER) database (2004–2015). All patients were randomly split into development cohort (n = 1,438) and validation cohort (n = 612) at a ratio of 7:3. The univariate and multivariate Cox regression analysis were performed to identify prognostic factors. A nomogram predicting CSS was established based on the results of multivariate Cox analysis. Its performance was evaluated by calibration curves, the receiver operating characteristic (ROC) curves, and the concordance index (C-index). Internal verification was performed in the validation cohort. The Kaplan–Meier method with the log-rank test was applied in the different risk groups.ResultsThe nomogram incorporated summary stage, tumor size, chemotherapy, regional nodes examined and positive lymph nodes. The C-index of the nomogram in the development cohort was 0.716 (0.707–0.725), while the value of the C-index was 0.691 (0.689–0.693) in the validation cohort. The AUC of the nomogram was 0.803 for 3-year and 0.854 for 5-year in the development cohort, while was 0.773 for 3-year and 0.809 for 5-year in the validation cohort. Calibration plots for 3-year and 5-year CSS showed good concordance. Significant differences were observed between high, medium, and low risk groups (P

The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is transformed into a convex optimization problem by second-order conic relaxation and Karush−Kuhn−Tucker optimality conditions. Secondly, a polyhedral approximation method is applied to construct a mixed integer linear programming, which can be solved quickly by branch and bound method. Finally, the model is solved many times to obtain the Pareto front according to the scalarization basic theorem. Based on an IEEE 33-bus distribution network model, simulation results show that the proposed algorithm can obtain an exact global optimal solution quickly compared with the heuristic method.

A solid oxide fuel cell (SOFC) is regarded as the first choice of high-efficiency and clean power generation technology in the 21st century due to its characteristics of high power generation efficiency and low pollutant emission. In this paper, hydrogen is used as a fuel for SOFCs using the EBSILON platform. A sensitivity analysis of the solid oxide fuel cell–gas turbine (SOFC–GT) system with steam reinjection is carried out to investigate the effect of the steam reinjection mass flow rate on the improvement of the electrical efficiency of the system and on the values of the other parameters. The results show that the variation in the steam reinjection mass flow rate has an effect on other parameters. Changes in several parameters affect the electrical efficiency of the system, which reaches 74.11% at a pressure ratio of 10, SOFC inlet temperature of 783.15 K, turbine back pressure of 70 kPa, and steam reinjection mass flow rate of 6.16 kg/s. Future research can optimize the overall parameter selection of the system in terms of economy and other aspects.

The three-degrees-of-freedom (3-DOF) parallel robot is commonly employed as a shipborne stabilized platform for real-time compensation of ship disturbances. Pose accuracy is one of its most critical performance indicators. Currently, neural networks have been applied to the kinematic calibration of stabilized platforms to compensate for pose errors and enhance motion accuracy. However, collecting a large amount of measured configuration data for robots entails high costs and time, which restricts the widespread use of neural networks. In this study, a “transfer network” is established by combining fine-tuning with a Back Propagation (BP) neural network. This network takes the motion transmission characteristics inherent in the ideal kinematic model as prior knowledge and transfers them to a network trained based on the actual poses. Compared with the conventional BP neural network trained by actual poses alone, the transfer network shows significant performance advantages, effectively solving the problems of low prediction accuracy and weak generalization ability in the case of small-sample measured data. Considering this, the impact pattern of the sample number of the actual pose on the effectiveness of transfer learning is revealed through the construction of multiple transfer network models under varying sample numbers of the actual pose, providing valuable marine engineering guidance. Finally, simulated sea-service experiments were conducted on the 3-UPS/S shipborne stabilized platform to validate the correctness and superiority of the proposed method.