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Flame-retardant science and technology are sciences developed to prevent the occurrence of fire, meet the needs of social safety production, and protect people's lives and property. Rigid polyurethane (PU) is a polymer formed by the additional polymerization reaction of a molecule with two or more isocyanate functional groups with a polyol containing two or more reactive hydroxyl groups under a suitable catalyst and in an appropriate ratio. Rigid polyurethane foam (RPUF) is a foam-like material with a large contact area with oxygen when burning, resulting in rapid combustion. At the same time, RPUF produces a lot of toxic gases when burning and endangers human health. Improving the flame-retardant properties of RPUF is an important theme in flame-retardant science and technology. This review discusses the development of flame-retardant RPUF through the lens of bibliometrics. A total of 194 articles are analyzed, spanning from 1963 to 2021. We describe the development and focus of this theme at different stages. The various directions of this theme are discussed through keyword co-occurrence and clustering analysis. Finally, we provide reasonable perspectives about the future research direction of this theme based on the bibliometric results. [ABSTRACT FROM AUTHOR]

In this paper, a soft switching DC–DC converter is presented for a hybrid energy storage system (HESS) in an electric vehicle (EV), using fixed boundary layer sliding mode control (FBLSMC) and variable switching frequency modulation. This strategy is aimed at improvement of the transient performance, energy transfer efficiency and system robustness for the HESS which is composed of a battery, an ultracapacitor (UC) and a bidirectional DC–DC converter. The state-space model of such DC–DC converter is firstly established involving all operating modes and system uncertainties. The FBLSMC scheme is proposed for the satisfactory voltage/current tracking despite system uncertainties. It can guarantee the system robustness and avoid the chattering existing in conventional sliding mode control (CSMC). In order to ensure the soft switching under transient load variations, a variable switching frequency modulation method is introduced into the controller. Finally, experimental results confirm that (1) within the full-load range the efficiency of the DC–DC converter with variable switching frequency is ∼96% in contrast to 90% efficiency at hard switching, and (2) the energy delivered by the UC follows the reference closely for EVs. [ABSTRACT FROM AUTHOR]

Purpose. Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients. Methods. A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation. Results. The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance (AUC = 0.891). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC. Conclusion. The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients. [ABSTRACT FROM AUTHOR]

Coordination of the Connected and Automated Vehicles (CAVs) is a demanding task in aspects of effective incorporation of road information with the control of vehicle motion states. Formulating a platoon composed of multiple CAVs initially located in multiple lanes requires integrated consideration of both steering incurred by the curved road and merging actions. Meanwhile, additional requirement of intervehicle safety distance keeping as well as bounded actuations is also critical for practical implementation. Considering the above problems, a comprehensive framework containing a distributed controller is proposed in this paper to systematically address the combined problems. A bi-level curvilinear vehicle coordination scheme is proposed and proven to be capable of guiding CAVs to desired platoon formation with theoretical analysis. Besides, both longitudinal and lateral acceleration limitations have been taken into consideration. The limited longitudinal acceleration is included in the design of nonlinear saturated controller of which the convergence has been proven. Adaptive merging is realized with a bounded lateral controller. The condition of limiting the initial states to guarantee safe intervehicle distance maintenance is formulated. Hardware-in-the-Loop (HiL) test demonstrations in multiple road scenarios with different curvatures validate the performance of the proposed controller over existing baseline methods. [ABSTRACT FROM AUTHOR]

Objective: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. Methods: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. Results: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. Conclusion: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans. [ABSTRACT FROM AUTHOR]

In this article, a new soft-switching auxiliary circuit structure for the synchronous rectification noninverting buck–boost converter (NIBBC) is proposed. This new auxiliary circuit, which contains a fully coupled inductor (or a transformer), a snubber inductor, a capacitor, and a power diode, will provide zero voltage switching (ZVS) turn-ON conditions for all power transistors of the proposed NIBBC. In addition, waveform ripples in the auxiliary circuit of the output side are reduced, and then the output current ripple is also reduced. Meanwhile, the current across the main inductor is not affected by the auxiliary circuit. In order to analyze the proposed NIBBC, operating principles, soft-switching implementation conditions, and main characteristics of the proposed NIBBC are presented in this article. Finally through building a 400-W experimental prototype, key waveforms and measuring efficiency curves are obtained. Experiment result shows that all transistors of the new NIBBC operate under ZVS conditions. The efficiency of the proposed NIBBC is improved by comparing to the triangular current mode soft-switching NIBBC. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA. Methods: Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier. Results: This study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility. Conclusions: This study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100β, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100β, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100β, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a severe global health problem. There is increasing evidence for the important roles of long noncoding RNAs in tumorigenesis and metastasis in HCC. In this study, we identified and characterized a novel long noncoding RNA, LINC02580, involved in HCC. LINC02580 was highly downregulated in HCC cohorts and was identified as a tumor suppressor. Low LINC02580 expression in patients with HCC was correlated with poor prognosis. Functional assays indicated that LINC02580-deficient cells show enhanced colony formation, migration, and invasion in vitro and promote subcutaneous tumor formation and distant lung metastasis in vivo. With respect to the underlying mechanism, we found that LINC02580 modulates the epithelial–mesenchymal transition (EMT) associated pathway in HCC cells by specifically binding to serine and arginine-rich splicing factor 1 (SRSF1). In summary, our findings illustrated that LINC02580 is a metastasis-suppressing lncRNA in HCC, and provided vital clues of how LINC02580 performs its biological functions. Further, this lncRNA may be a potential target in the prognosis and treatment of HCC. • LINC02580, a long noncoding RNA, is downregulated in HCC tissues. • LINC02580 correlates with poor prognosis in HCC patients. • LINC02580 deficiency promotes HCC cells proliferation and metastasis in vitro and in vivo. • LINC02580 interacts with serine and arginine-rich splicing factor 1. • Epithelial–mesenchymal transition is involved in pathways downstream of LINC02580. [ABSTRACT FROM AUTHOR]

This letter proposed a new soft-switching non-inverting buck–boost converter (NIBBC) by introducing the magnetic coupling effect to an existing soft-switching dc/dc converter. The new NIBBC obtains two superiorities: one magnetic core is removed to improve the efficiency; the magnetic coupling effect can give an adjustable soft-switching range. Operating principles and converter characteristics of the new NIBBC are presented. An experimental prototype of the new NIBBC is built, and switching waveforms and the efficiency are measured. Experimental results show that the new NIBBC can obtain soft-switching conditions and has efficiency improvement comparing to hard-switching converter and another soft-switching dc/dc converter. [ABSTRACT FROM AUTHOR]

In this paper, several new coupled-inductor-based soft-switching dc–dc converters are proposed. These converters are obtained by changing the coupled branch of several existing dc–dc converters. Meanwhile they have similar characteristics to these existing converters, and provide more choices for the practical application. Then by applying an existing efficiency improvement method, which is used in the buck-type soft-switching converter, to all abovementioned converters, several new converters with efficiency improvement characteristics are obtained. This efficiency improvement method utilizes the low conduction performance of the transistor to reduce the loss. In addition, this method has the same control complexity to original topologies, because the auxiliary transistor has the same control timing to one of main transistors. The characteristic of new converters and efficiency improvement method are presented by analyzing two new topologies (boost-type converters). Finally simulation and experimental results are given and analyzed by designing a prototype. Based on these results, soft-switching conditions and efficiency improvement capability are verified. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100β, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100β, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100β, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance. [ABSTRACT FROM AUTHOR]

Nanoscale zero-valent iron (nZVI) has received increasing attention as a new technology that can provide cost-effective solutions in the remediation and treatment of contaminated soils due to its large surface area and high surface reactivity. nZVI can remove/transfer a wide range of contaminants such as heavy metals, inorganic salts and organic matter from the soil. This bibliometric analysis focused on the application of nZVI in soil remediation. A total of 1255 research papers were traced and analyzed between 1997 and 2022. The use of nZVI for soil remediation has undergone three stages of development and has received a great deal of attention in the last decade. A range of environmental science-related journals provides a stable platform for publishing and discussing papers on this topic. The contributions of different countries, institutions and authors to this topic were analyzed. A complex cooperation network between them was constructed. Keyword analysis was used to understand the different research directions on this topic. Most of these efforts have focused on the preparation and modification of nZVI. The mechanism of its use for the remediation of different pollutants has also been described in detail. Some examples of field applications have also been reported. Finally, the current status of this topic based on bibliometrics and its future perspectives are summarized. [ABSTRACT FROM AUTHOR]

In this paper, a soft-switching topology is proposed for the bidirectional dc–dc converter. In order to achieve soft-switching conditions, the auxiliary circuits including a small inductor and two capacitors are introduced to the bidirectional dc–dc converter. By generating and keeping a recycle current in the auxiliary circuits, the proposed topology can provide soft-switching conditions for both switches of the bidirectional dc–dc converter in fixed frequency control application. Meanwhile, no auxiliary switches are introduced. Due to the existence of the auxiliary circuits, the proposed topology can also reduce the current ripple in the main inductor. Based on this soft-switching method, a family of bidirectional soft-switching dc–dc topologies is derived. These topologies have the similar structures and the same characteristics. The topology deduction, operating principles, and design guidelines are presented in detail. Finally, an experimental prototype is built to verify the analysis and characteristics of the proposed topologies. [ABSTRACT FROM PUBLISHER]

Catalytic ozonation is a non-selective mineralization technology of organic matter in water by using active free radicals generated by ozone degradation. Catalytic ozonation technology can be divided into homogeneous catalytic reactions using metal ions as catalysts and heterogeneous catalytic reactions using solid catalysts. Homogeneous catalytic ozonation technology has many problems, such as low mineralization rate, secondary pollution caused by the introduction of metal ions and low utilization efficiency of oxidants, which limit its practical application. Compared with homogeneous catalytic ozonation technology, heterogeneous catalytic ozonation technology has the advantages of easy recovery, lower cost of water treatment, higher activity and improved mineralization rate of organic matter. This overview classifies and describes catalysts for heterogeneous catalytic ozonation technology, including the different types of metal oxides, metal-free catalysts, and substrates used to immobilize catalysts. In addition, the heterogeneous catalytic ozonation process involved in the multiphase complex reaction process is discussed. The effects of different parameters on the performance of heterogeneous catalytic ozonation are also discussed. [ABSTRACT FROM AUTHOR]

To guarantee the safety, high efficiency and long lifetime for lithium-ion battery, an advanced battery management system requires a physics-meaningful yet computationally efficient battery model. The pseudo-two dimensional (P2D) electrochemical model can provide physical information about the lithium concentration and potential distributions across the cell dimension. However, the extensive computation burden caused by the temporal and spatial discretization limits its real-time application. In this research, we propose a new simplified electrochemical model (SEM) by modifying the boundary conditions for electrolyte diffusion equations, which significantly facilitates the analytical solving process. Then to obtain a reduced order transfer function, the Padé approximation method is adopted to simplify the derived transcendental impedance solution. The proposed model with the reduced order transfer function can be briefly computable and preserve physical meanings through the presence of parameters such as the solid/electrolyte diffusion coefficients (Ds&De) and particle radius. The simulation illustrates that the proposed simplified model maintains high accuracy for electrolyte phase concentration (Ce) predictions, saying 0.8% and 0.24% modeling error respectively, when compared to the rigorous model under 1C-rate pulse charge/discharge and urban dynamometer driving schedule (UDDS) profiles. Meanwhile, this simplified model yields significantly reduced computational burden, which benefits its real-time application. [ABSTRACT FROM AUTHOR]

The electrochemistry-based battery model can provide physics-meaningful knowledge about the lithium-ion battery system with extensive computation burdens. To motivate the development of reduced order battery model, three major contributions have been made throughout this paper: (1) the transfer function type of simplified electrochemical model is proposed to address the current-voltage relationship with Padé approximation method and modified boundary conditions for electrolyte diffusion equations. The model performance has been verified under pulse charge/discharge and dynamic stress test (DST) profiles with the standard derivation less than 0.021 V and the runtime 50 times faster. (2) the parametric relationship between the equivalent circuit model and simplified electrochemical model has been established, which will enhance the comprehension level of two models with more in-depth physical significance and provide new methods for electrochemical model parameter estimation. (3) four simplified electrochemical model parameters: equivalent resistance R eq , effective diffusion coefficient in electrolyte phase D e eff , electrolyte phase volume fraction ε and open circuit voltage (OCV), have been identified by the recursive least square (RLS) algorithm with the modified DST profiles under 45, 25 and 0 °C. The simulation results indicate that the proposed model coupled with RLS algorithm can achieve high accuracy for electrochemical parameter identification in dynamic scenarios. [ABSTRACT FROM AUTHOR]

(1) Background: For completely lower pole renal tumors, we compared the perioperative outcomes of robotic partial nephrectomy via transperitoneal and retroperitoneal approaches. (2) Methods: Complete lower pole renal tumors were defined as tumors that received 1 point for the "L" element of the R.E.N.A.L. and located at the lower pole of kidney. After confirming consistency in baseline characteristics, oncological and functional benefits were compared. Pentafecta achievement was used to represent the perioperative optimal outcome, followed by multivariate analysis of factors associated with the lack of pentafecta achievement. (3) Results: Among 151 patients identified, 116 (77%) underwent robotic partial nephrectomy via a transperitoneal approach and 35 (23%) via a retroperitoneal approach. Patients undergoing transperitoneal robotic partial nephrectomy experienced more blood loss than those undergoing retroperitoneal robotic partial nephrectomy (50 mL vs. 40 mL, p = 0.015). No significant differences were identified for operative time (120 min vs. 120 min), ischemia time (19 min vs. 20 min), positive surgical margins (0.0% vs. 2.86%), postoperative rate of complication (12.07% vs. 5.71%). No significant differences were identified in pathologic variables, eGFR decline in postoperative 12-month (3.9% vs. 5.4%) functional follow-up. Multivariate cox analysis showed that tumor size (OR: 0.523; 95% CI: 0.371–0.736; p < 0.001) alone was independently correlated to the achievement of pentafecta. (4) Conclusions: For completely lower pole renal tumors, transperitoneal and retroperitoneal robotic partial nephrectomy provide similar outcomes. These two surgical approaches remain feasible options for these cases. [ABSTRACT FROM AUTHOR]

Objective. To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. Method. Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model's performance. The final model was shown by a website calculator. Result. Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879–0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. Conclusion. This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES).Methods: Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model.Results: The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility.Conclusions: The tool built in this paper to predict 1- and 3-year survival in ES patients ( https://drwenleli0910.shinyapps.io/EwingApp/ ) has a good identification and predictive power. [ABSTRACT FROM AUTHOR]

For series–parallel hybrid electric vehicles (HEVs), problems of driveability are significant and difficult to solve due to clutch engagement during mode transitions. In this paper, a robust controller was designed for a series–parallel HEV to reduce vehicle jerk during mode transitions and improve vehicle driveability. First, a linear dynamic system model of the controlled plant was obtained for robust control design. Then, the robust controller was designed based on the mu-synthesis method and solved using the discrete $D-K$ iteration; parameter uncertainties in the system model were considered in the design process to ensure the robustness of the control system. Finally, a hardware-in-the-loop (HIL) simulation was performed to verify the proposed controller. The HIL platform was composed of a dynamometer-simulated engine, a real transmission, a virtual electric machine, and a virtual vehicle, which was constructed based on the nonlinear dynamics of tires, road, and vehicle body. The HIL simulation results showed the effectiveness of the proposed robust mode transition control. [ABSTRACT FROM AUTHOR]

• The use of NGS is an efficient technique to diagnose cerebral creatine deficiency syndrome precisely. • A combined mass spectrometry with gene mutation analysis is helpful in CCDS diagnosis. • Genetic diagnosis determined that all seven patients were positive for SLC6A8 or GAMT variation. • A total of 12 variants were identified in this cohort, including six novel ones. Cerebral creatine deficiency syndromes (CCDSs) are a group of rare mendelian disorders mainly characterized by intellectual disability, movement anomaly, behavior disorder and seizures. SLC6A8 , GAMT , and GATM are known genes responsible for CCDS. In this study, seven pediatric patients with developmental delay were recruited and submitted to a series of clinical evaluation, laboratory testing, and genetic analysis. The clinical manifestations and core biochemical indications of each child were basically consistent with the diagnosis of CCDS. Genetic diagnosis determined that all patients were positive for SLC6A8 or GAMT variation. A total of 12 variants were identified in this cohort, including six novel ones. The frequency of these variants, the Revel scores and the conservatism of the affected amino acids support their pathogenicity. Our findings expanded the mutation spectrum of CCDS disorders, and provided solid evidence for the counseling to affected families. [ABSTRACT FROM AUTHOR]

Background. Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms. Methods. We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient's demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator. Results. Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient. Conclusions. The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work was to investigate the risk factors for cement leakage and new-onset OVCF after Percutaneous vertebroplasty (PVP) and to develop and validate a clinical prediction model (Nomogram). Methods: Patients with Osteoporotic VCF (OVCF) treated with PVP at Liuzhou People's Hospital from June 2016 to June 2018 were reviewed and met the inclusion criteria. Relevant data affecting bone cement leakage and new onset of OVCF were collected. Predictors were screened using univariate and multi-factor logistic analysis to construct Nomogram and web calculators. The consistency of the prediction models was assessed using calibration plots, and their predictive power was assessed by tenfold cross-validation. Clinical value was assessed using Decision curve analysis (DCA) and clinical impact plots. Results: Higher BMI was associated with lower bone mineral density (BMD). Higher BMI, lower BMD, multiple vertebral fractures, no previous anti-osteoporosis treatment, and steroid use were independent risk factors for new vertebral fractures. Cement injection volume, time to surgery, and multiple vertebral fractures were risk factors for cement leakage after PVP. The development and validation of the Nomogram also demonstrated the predictive ability and clinical value of the model. Conclusions: The established Nomogram and web calculator (https://dr-lee.shinyapps.io/RefractureApp/) (https://dr-lee.shinyapps.io/LeakageApp/) can effectively predict the occurrence of cement leakage and new OVCF after PVP. [ABSTRACT FROM AUTHOR]

Background. This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods. Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results. Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions. In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface—a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S. [ABSTRACT FROM AUTHOR]

Background: Established prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR).Methods: Data from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model.Results: The LightGBM model had the highest AUC (0.815, 95% CI 0.747-0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years.Conclusions: This study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a powertrain control strategy for a series hybrid electric vehicle (SHEV) based on the integrated design of an active charging scenario and fixed-boundary-layer sliding mode controllers (FBLSMCs). An optimized charging curve for the battery is predetermined rather than subject to engine output and vehicle power demand, which is a total inverse of normal SHEV powertrain control process. This is aimed to remove surge and high-frequency charge current, keep the battery staying in a high state-of-charge (SOC) region and avoid persistently-high charge power, which are positive factors to battery lifetime extension. Then two robust chattering-free FBLSMCs are designed to locate the engine operation in the optimal efficiency area. One is in charge of engine speed control, and the other is for engine/generator torque control. Consequently, not only fuel economy is improved but also battery life expectancy could be extended. Finally, simulation and experimental results confirm the validity and application feasibility of the proposed strategy. [Copyright &y& Elsevier]

This paper presents the mathematical modeling and analysis of a novel multimode transmission (MMT) for a hybrid electric vehicle (HEV) using a single electric machine (EM), which implies compactness and low cost. The single-EM solution avoids losses from another EM and its power electronics, which are employed in many existing HEVs. The topology of the MMT planetary gearset is the same as that of conventional four-speed automatic transmissions (ATs). The MMT realizes five power flow modes, which are developed into 16 operation modes, including one Motor_only mode, four Engine_only modes, four Compound driving modes, six Braking modes, and one Charging while parking mode. The properly arranged clutches transmit power flow more flexibly, allow direct mechanical power transmission from the engine to the drive shaft, and avoid spin loss for the engine and energy conversion loss for the electric components. Simulation under the New European Driving Cycle (NEDC) shows that the fuel consumption of the proposed HEV is comparable to a benchmark “THS II-like” vehicle, which uses a planetary gearset, two EMs, and no clutch, which indicates the fuel economy potential of this concept. [ABSTRACT FROM PUBLISHER]

A MPC5554-based hybrid control unit (HCU) is developed for a series-parallel hybrid electric bus ( SPHEB) in this paper. Firstly considering the operation features and powertrain configuration of SPHEB with minimizing fuel consumption as objective, a real-time optimal energy management strategy (EMS) based on iterative dynamic programming global optimization algorithm and Elman dynamic neural network is proposed. Then according to the vehicle requirements and EMS, the hardware system of HCU is designed with MPC5554 as CPU. Finally HCU hardware-in-the-loop simulation test is conducted with ETAS PT-LABCAR as platform. The results demonstrate that the developed HCU operates stable and reliable and can effectively realize real-time control for the SPHEB, significantly improving the fuel economy of the vehicle. [ABSTRACT FROM AUTHOR]

Abstract: Hybrid electric buses have been a promising technology to dramatically lower fuel consumption and carbon dioxide (CO2) emission, while energy management strategy (EMS) is a critical technology to the improvements in fuel economy for hybrid electric vehicles (HEVs). In this paper, a suboptimal EMS is developed for the real-time control of a series–parallel hybrid electric bus. It is then investigated and verified in a hardware-in-the-loop (HIL) simulation system constructed on PT-LABCAR, a commercial real-time simulator. First, an optimal EMS is obtained via iterative dynamic programming (IDP) by defining a cost function over a specific drive cycle to minimize fuel consumption, as well as to achieve zero battery state-of-charge (SOC) change and to avoid frequent clutch operation. The IDP method can lower the computational burden and improve the accuracy. Second, the suboptimal EMS for real-time control is developed by constructing an Elman neural network (NN) based on the aforementioned optimal EMS, so the real-time suboptimal EMS can be used in the vehicle control unit (VCU) of the hybrid bus. The real VCU is investigated and verified utilizing a HIL simulator in a virtual forward-facing HEV environment consisting of vehicle, driver and driving environment. The simulation results demonstrate that the proposed real-time suboptimal EMS by the neural network can coordinate the overall hybrid powertrain of the hybrid bus to optimize fuel economy over different drive cycles, and the given drive cycles can be tracked while sustaining the battery SOC level. [Copyright &y& Elsevier]

In view of the problem of power batteries being frequently charged and discharged at large power in hybrid electric vehicle, the battery/ultra-capacitor hybrid energy storage system (B/UC HESS ) is adopted, which takes advantage of the high power characteristics of ultra-capacitor to improve the performance of storage system. In this paper, two schemes of B/UC HESS are studied: direct battery/ultra-capacitor parallel connection and active battery/ultra-capacitor parallel connection, in which the latter uses DC-DC convertor with zero current transition soft switching as interface to connect battery and ultra-capacitor. The dynamic model for the DC-DC convertor and the models for battery and ultra-capacitor are set up with Matlab simulink and a simulation is conducted with AVL Cruise. The results indicate that the direct parallel scheme doesn't take full use of the capability of ultra-capacitor, while with the active parallel scheme the peak current of battery lowers in pure electric driving and regenerative braking conditions, the variation range of battery end voltage reduces, with an energy efficiency 14. 92 % higher than that of sole battery energy storage system. In addition, its dynamic response performance also improves due to the use of fuzzy PID control algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to present a new type of series–parallel hybrid electric bus and its energy management strategy. This hybrid bus is a post-transmission coupled system employing a novel transmission as the series–parallel configuration switcher. In this paper, the vehicle architecture, transmission scheme and numerical models are presented. The energy management system governs the mode switching between the series mode and the parallel mode as well as the instantaneous power distribution. In this work, two separated controllers using fuzzy logic called Mode Decision and Parallel-driving Energy Management are employed to fulfill these two tasks. The energy management strategy and the applications of fuzzy logic are described. The strategy is validated by a forward-facing simulation program based on the software Matlab/Simulink. The results show that the energy management strategy is effective to control the engine operating in a high-efficiency region as well as to sustain the battery charge state while satisfy the drive ability. The energy consumption is theoretically reduced by 30.3% to that of the conventional bus under transit bus driving cycle. In addition, works need future study are also presented. [Copyright &y& Elsevier]

Reliable trajectory prediction of preceding target vehicles (PTVs) is crucial for the planning and decision making of automated vehicles. However, the future trajectory is affected by the driver's intention and diverse driving styles, which can hardly be predicted precisely, especially when the vehicle performs a lane change maneuver. In this study, we propose a lane crossing and final points generation (CFPG) model-based trajectory prediction approach for PTVs, in which the key influence factors such as the driver's intention and the mixed driving style are included. Firstly, we build a maneuver and stage recognition model upon the long short term memory (LSTM) to infer the current maneuver of the preceding target vehicle. Furthermore, the approach predicts the lane crossing point using a physics-based model combining with a deep conditional generative model trained by a deep neural network. Moreover, a maneuver-based model is adopted to predict the final point according to the prediction interval. In order to avoid the possible cumulative error caused by iteratively generating trajectories in traditional methods, we use a curve fitting method to obtain the predicted trajectory. Lane changing data collected from naturalistic driving scenarios are used to verify the proposed approach, and the results suggest more accurate and reliable prediction trajectories compared with conventional methods. [ABSTRACT FROM AUTHOR]

Background. Sudden cardiac death is a leading cause of death from coronary heart disease (CHD). The risk of sudden cardiac death (SCD) increases with age, and sudden arrhythmic death remains a major cause of mortality in elderly individuals, especially ventricular arrhythmias (VA). We developed a risk prediction model by combining ECG and other clinical noninvasive indexes including biomarkers and echocardiology for VA in elderly patients with CHD. Method. In the retrospective study, a total of 2231 consecutive elderly patients (≥60 years old) with CHD hospitalized were investigated, and finally 1983 patients were enrolled as the model group. The occurrence of VA within 12 months was mainly collected. Study parameters included clinical characteristics (age, gender, height, weight, BMI, and past medical history), ECG indexes (QTcd, Tp-e/QT, and HRV indexes), biomarker indexes (NT-proBNP, Myo, cTnT, CK-MB, CRP, K+, and Ca2+), and echocardiology indexes. In the respective study, 406 elderly patients (≥60 years old) with CHD were included as the verification group to verify the model in terms of differentiation and calibration. Results. In the multiparameter model, seven independent predictors were selected: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age. Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors (RR > 1), while female and increased LVEF were protective factors (RR < 1). This model can well predict the occurrence of VA in elderly patients with CHD (for model group, AUC: 0.721, 95% CI: 0.669∼0.772; for verification group, AUC: 0.73, 95% CI: 0.648∼0.818; Hosmer–Lemeshow χ 2 = 13.541, P = 0.095). After adjusting the predictors, it was found that the combination of clinical indexes and ECG indexes could predict VA more efficiently than using clinical indexes alone. Conclusions. LVEF, LAV, QTcd, Tp-e/QT, gender, age, and HLP were independent predictors of VA risk in elderly patients with CHD. Among these factors, the echocardiology indexes LVEF and LAV had the greatest influence on the predictive efficiency of the model, followed by ECG indexes, QTcd and Tp-e/QT. After verification, the model had a good degree of differentiation and calibration, which can provide a certain reference for clinical prediction of the VA occurrence in elderly patients with CHD. [ABSTRACT FROM AUTHOR]

Background: The prognosis of lung metastasis (LM) in patients with chondrosarcoma was poor. The aim of this study was to construct a prognostic nomogram to predict the risk of LM, which was imperative and helpful for clinical diagnosis and treatment.Methods: Data of all chondrosarcoma patients diagnosed between 2010 and 2016 was queried from the Surveillance, Epidemiology, and End Results (SEER) database. In this retrospective study, a total of 944 patients were enrolled and randomly splitting into training sets (n = 644) and validation cohorts(n = 280) at a ratio of 7:3. Univariate and multivariable logistic regression analyses were performed to identify the prognostic nomogram. The predictive ability of the nomogram model was assessed by calibration plots and receiver operating characteristics (ROCs) curve, while decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure predictive accuracy and clinical practice. Moreover, the nomogram was validated by the internal cohort.Results: Five independent risk factors including age, sex, marital, tumor size, and lymph node involvement were identified by univariate and multivariable logistic regression. Calibration plots indicated great discrimination power of nomogram, while DCA and CIC presented that the nomogram had great clinical utility. In addition, receiver operating characteristics (ROCs) curve provided a predictive ability in the training sets (AUC = 0.789, 95% confidence interval [CI] 0.789-0.808) and the validation cohorts (AUC = 0.796, 95% confidence interval [CI] 0.744-0.841).Conclusion: In our study, the nomogram accurately predicted risk factors of LM in patients with chondrosarcoma, which may guide surgeons and oncologists to optimize individual treatment and make a better clinical decisions.Trial Registration: JOSR-D-20-02045, 29 Dec 2020. [ABSTRACT FROM AUTHOR]

This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning (IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous action space. The learning process resembles that of human beings by clustering the encountered states, developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement. It adopts the actor–critic architecture whereas being different from mainstream policy gradient methods. The value function is approximated through the fuzzy system AnYa. The state–action space is discretized into a static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value of the node being the consequent. Values of consequents are updated using the Sarsa(λ) algorithm. Probability distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA), Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets (ε FS). The fuzzy kernel of IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with four problems, namely Mountain Car, Continuous Gridworld, Pendulum Position, and Tank Level Control, are presented as a proof of the proposed concept. [ABSTRACT FROM AUTHOR]

Multiple vehicle tracking (MVT) system is a prerequisite to path planning and decision making of self-driving cars as it can provide positions of surrounding vehicles. Most of the available approaches belonging to the so called tracking-by-detection approach inevitably bring detection errors into the tracking result. In this study, we proposed a laser range finder (LRF) based track-before-detect MVT algorithm without detection procedure. Moreover, different from the state of the art in track-before-detect approaches using raw data, we applied a pre-clustering procedure to segment the raw data into disjoint clusters to reduce computation demand. Specifically, a clustering algorithm named iterative nearest point search (INPS) which can even handle the partial occlusion situations that are challenging for traditional clustering algorithms was designed for the pre-clustering procedure. Furthermore, a detailed cluster-to-target measurement model was proposed to describe the difference between cluster and hypothesis vehicle. Finally, we integrated the measurement model into the labeled multi-Bernoulli filter with particle implementation. Simulations and experiments show that the proposed MVT algorithm provides more accurate estimates of vehicle number and position in comparison with conventional methods. [ABSTRACT FROM AUTHOR]

Clutch-to-clutch shift technology is a key enabler for fast and smooth gearshift for multi gear transmissions. However, conventional hydraulic actuation systems for clutches have drawbacks of oil leakage and low efficiency. Electromechanical devices including wedge mechanism offer potential alternative actuators. The previous studies on the wedge emphasize on self-reinforcement, but neglect self-weakened phenomenon. In this paper, a novel dual-wedge mechanism is proposed to exert self-reinforcement and avoid self-weakened effect by selecting a correct working slope. The design concept and physical structure are thoroughly described. Dynamic models for the actuation system and vehicle powertrain are built for performance validation. The results show that the normal force generated by the wedge under self-reinforced case is 2.74 times that under self-weakened case. Using the same amount of the driving motor current, the upshift can be successfully completed in 0.78 s when the correct slope is used; however, it fails to engage the clutch when the incorrect slope is used due to self-weakened effect. So does the downshift. Moreover, the experimental results of the dual-wedge mechanism using the correct slope are comparable to those from the hydraulic actuator of a conventional automatic transmission. [ABSTRACT FROM AUTHOR]

Understanding the factors that contribute to population genetic structures and ecological niche divergence is crucial for elucidating the biogeographical history and speciation of plants. To disentangle the present phylogeographical patterns and evolutionary history of two closely related parapatric species from Ephedra ( Ephedra sinica and E. intermedia), we systematically profiled their population genetic structures, niche divergence and potential speciation processes based on sequence variation of four chloroplast DNA fragments and climate-based ecological niche modelling (ENM). Phylogenetic analyses suggested that E. sinica formed a monophyly which was nested in E. intermedia. The latter species formed two paraphyletic subclades, corresponding to Western and Eastern lineages. Furthermore, the phylogeographical analysis results indicated very significant inter- and intra-species genetic differentiation, and BEAST inference suggested that E. sinica diverged from E. intermedia at 3.20 million years ago (Ma; 95 % HPD: 2.05-8.10 Ma), while the Western lineage of E. intermedia diverged much earlier at 6.10 Ma (95 % HPD: 4.10-11.82 Ma). In addition, niche identity tests performed using MaxEnt strongly supported the hypothesis that the two sister species had significantly diverged on their ecological niches. Isolation by geographical and environment distance (IBD and IBE) tests suggested that the overall divergence of the populations from the two species were more influenced by environmental factors. Our data provide new insight into the ecological speciation of Ephedra and also provides molecular evidence for the identification and genetic conservation of these important pharmaceutical plants. [ABSTRACT FROM AUTHOR]

To improve the suitability of lithium-ion battery model under varying scenarios, such as fluctuating temperature and SoC variation, dynamic model with parameters updated realtime should be developed. In this paper, an incremental analysis-based auto regressive exogenous (I-ARX) modeling method is proposed to eliminate the modeling error caused by the OCV effect and improve the accuracy of parameter estimation. Then, its numerical stability, modeling error, and parametric sensitivity are analyzed at different sampling rates (0.02, 0.1, 0.5 and 1 s). To identify the model parameters recursively, a bias-correction recursive least squares (CRLS) algorithm is applied. Finally, the pseudo random binary sequence (PRBS) and urban dynamic driving sequences (UDDSs) profiles are performed to verify the realtime performance and robustness of the newly proposed model and algorithm. Different sampling rates (1 Hz and 10 Hz) and multiple temperature points (5, 25, and 45 °C) are covered in our experiments. The experimental and simulation results indicate that the proposed I-ARX model can present high accuracy and suitability for parameter identification without using open circuit voltage. [ABSTRACT FROM AUTHOR]

This paper presents a novel topology for a nonisolated bidirectional dc–dc converter with soft-switching capabilities, which usually operates at a zero-voltage-switching (ZVS) condition. A nonisolated dc–dc converter combines a buck converter and a backward boost converter into one circuit, which consists of a half-bridge power switch, an inductor, and capacitors. In order to realize ZVS conditions, the proposed converter utilizes a coupled inductor, a small independent inductor, and auxiliary switches and diodes. Due to ZVS, switching stress on switch components is reduced, and the reverse recovery problem of MOSFET antiparallel body diodes is also eliminated. Moreover, the operating modes of the proposed converter can be switched between a ZVS mode and a conventional hard-switching mode on the basis of load conditions. The soft-switching mode is for heavy loads, and the hard-switching mode is for light-load conditions. Therefore, the highest efficiency can be obtained at almost all load ranges. The detailed theoretical analyses in each mode are presented, and a 1-kW prototype is also built to verify the principle of the circuit and the theoretical analysis. [ABSTRACT FROM PUBLISHER]

This letter presents a novel topology for a buck dc–dc converter with soft-switching capability, which operates under a zero-current-switching condition at turn on and a zero-voltage-switching condition at turn off. In order to realize soft switching, based on a basic buck converter, the proposed converter added a small inductor, a diode, and an inductor coupled with the main inductor. Because of soft switching, the proposed converter can obtain a high efficiency under heavy load conditions. Moreover, a high efficiency is also achieved under light load conditions, which is significantly different from other soft-switching buck converters. The detailed theoretical analyses of steady-state operation modes are presented, and the detailed design methods and some simulation results are also given. Finally, a 600 W prototype is built to validate the theoretical principles. The switching waveforms and the efficiencies are also measured to validate the proposed topology. [ABSTRACT FROM AUTHOR]