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To assess efficacy and prognostic factors of endoscopic balloon dilatation for the treatment of subglottic stenosis in children.A retrospective review was performed on 49 pediatric patients with subglottic stenosis treated at the Shanghai Children's Hospital between December 2017 and December 2021. Specific demographic data, type and severity of the stenosis, number of balloon dilatations and outcomes were recorded and analyzed.Forty-nine children (30 male, 19 female) were included in the study with a median age at diagnosis of 24 (13-36.5) months, of which 7 (14.3%) had received open laryngotracheal reconstruction previously. The degree of subglottic stenosis was grade I in six patients, grade II in 16 patients, grade III in 20 patients and grade IV in seven patients. After various numbers of balloon dilatations (1-7 times), 29 patients showed a good outcome (decannulation or prevention of tracheostomy) and the success rate in that series was 59.2%. Overall, prognosis of balloon dilatation was not dependent on pathogeny (congenital or acquired) or open surgical history(P 0.05), but rather on the severity grade of stenosis and the number of dilatations (P 0.05).Endoscopic balloon dilatation can be safe and effective in the treatment of subglottic stenosis in children, except for more serious cases (grade IV). Open surgery should be considered if no significant improvement is observed after dilatation, especially after three or more dilatations.

Polyoxymethylene dimethyl ethers (PODE) are newly appeared promising oxygenated additive which can significantly reduce soot emissions of diesel engines. In this study, the spray and combustion characteristics of the blends of diesel with different PODE blending ratio of 0%, 20%, 50% and 100% by volume are investigated in a constant volume chamber (CVC) under 100 MPa, 120 MPa and 140 MPa injection pressures. The spray results show that liquid spray tip penetration (STP), average spray angle (ASA) and spray projected area (SPA) all decrease with PODE blending ratio under 4 MPa ambient pressure and 573 K ambient temperature. The addition of PODE in diesel can significantly reduce the soot formation and the integrated natural flame luminosity (INFL). Under 100 MPa injection pressure, the time integral sum KL in whole combustion duration of D100, P20, P50 and P100 are namely 107.35, 2.66, 0.27 and 0.08. When the PODE blending ratio are 50% and 100%, nearly no yellow flame can be observed in the combustion duration. The chemical structure of PODE plays an important role in reducing soot formation.

To investigate the clinical features and surgical outcomes of pediatric congenital first branchial cleft anomalies (CFBCAs).We conducted a retrospective analysis of 100 children who were referred to Shanghai Children's Hospital from March 2014 to March 2022 for the treatment of CFBCAs.This study included 100 patients (33 males, 67 females) with an average age of 4.0 ± 2.7 years. 64 cases were type I FBCAs and 36 were type II. The main clinical manifestations included having a skin pit or discharge from it (62%), painless masses (5%), mucopurulent otorrhea (8%) and recurrent swelling with pain (90%) in the Pochet's triangle area. 92% had infection histories, 84% had incision and drainage histories, and 18% had surgical histories. 6 cases of tympanic membranous attachment were found by auricular endoscopy. Ultrasonography (US) was 55.6% (30/54) accurate and enhanced CT was 75% (75/100) accurate in diagnosing CFBCAs. We dissected the facial nerve (FN) in 46% cases. Lesions ended in the external auditory canal (EAC) wall in 86 cases. 69 exhibited close relationship with the parotid. The patients were followed up 0.25-8.2 years. 11 had postoperative temporary facial paralysis and all improved within 6 months. 3 had recurrence and they were secondarily successfully retreated. No EAC stenosis were found.CFBCAs often presented with repeated swelling and purulence in Pochet's triangle. CT, US and auricular endoscopy can assist in diagnosis and planning the surgical strategy. Complete excision in non-infection stage as soon as possible is the first choice for the treatment of CFBCAs.

IntroductionAcute and chronic otitis media (AOM and COM) are common middle ear infections that can lead to hearing loss and other complications. Recent research has shown that both macrophages and nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway are involved in the immune response to and the resolution of otitis media. However, the specific effects of Nrf2 on macrophages in the transition of AOM to COM are not well understood, and a practical approach to prevent this transition by targeting Nrf2/macrophages has not been established.MethodsIn an AOM mouse model using lipopolysaccharide (LPS) injection into the middle ear, middle ear effusion (OME)-macrophages were isolated and analyzed for Nrf2 expression. M2-like polarization of macrophages was induced by Nrf2 activation and its effects on inflammatory resolution were studied by examining inflammatory neutrophils and macrophages, proinflammatory cytokines, and oxidative levels. The survival of human middle ear epithelial cells (HMMECs) co-cultured with Nrf2-modified macrophages was also evaluated. Furthermore, restoration of Nrf2 in macrophages with adeno-associated virus (AAV) vectors was performed to determine the effect on the transition of AOM to COM in experimental mice.ResultsReduced Nrf2 in OME-macrophages during the recovery phase was associated with uncured AOM or its development into COM, demonstrated by persistent increases in inflammatory neutrophils and macrophages, proinflammatory cytokines, and oxidative levels. Nrf2 activation induced M2-like polarization of macrophages, which improved the survival of co-cultured HMMECs treated with LPS in vitro. Restoration of Nrf2 in OME-derived low-Nrf2-expressing macrophages with AAV vectors significantly inhibited the transition of AOM to COM in experimental mice.DiscussionNrf2 in macrophages plays a critical role in the immune response to and resolution of otitis media Restoration of Nrf2 expression in OME-macrophages could be a promising therapeutic approach to prevent the development of COM in AOM patients.

This paper develops a distributed cooperative energy management system with two distributed control layers for speed-coupling plug-in hybrid electric vehicles. By introducing personalized non-stationary inference, this system can fuse driving behavior and vehicle state information to adaptively adjust power-split control parameters for the improvement of vehicle energy economy. In the on-board control layer, five sets of personalized control parameters are optimized offline by using chaos-enhanced accelerated particle swarm optimization. In the distributed control layer, interval type-2 fuzzy sets are applied to develop a real-time driving style recognition function. Driving behavior is detected remotely, via the vehicle to everything network, and downloaded to adaptively adjust power-split control parameters in the on-board vehicle controller. Hardware-in-the-loop testing is carried out based on the four laboratory driving cycles and four personal driving cycles. The proposed system has been demonstrated with strong robustness that saves energy by up to 5.25% over the equivalent consumption minimization strategy (ECMS), especially for gentle drivers. Even under harsh communication conditions (with signal loss 80+%), it still performs better than the ECMS (by 0.57%) and the series-parallel control strategy (by 2.66%).

This paper proposes a new form of algorithm environment for multi-objective optimization of energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary algorithm (SSPEA) is developed to optimize the power-split control parameters guided by the data from the physical PHEV and its digital twins. By introducing a ''confidence factor'', the SSPEA uses the fused data of physically measured and virtually simulated vehicle performances (energy consumption and remaining battery state-of-charge) to converge the optimization process. Gaussian noisy models are adopted to emulate the real vehicle system on the hardware-in-the-loop platform for experimental evaluation. The testing results suggest that the proposed SSPEA requires less R&D costs than the model-free method that only uses the physical information, and more than 44.6% energy can be saved during the R&D process. Driven by the SSPEA, the optimized energy management system surpasses other non-DT-assisted systems by saving more than 4.8% energy.

This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0-3 by the Klintrup-Mäkinen (KM) grading method and this was compared to DL approaches. Inter-rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 μm of the IM (f_im200) remained the most significant prognostic factor for progression-free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001-0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001-0.645], p = 0.024). Inter-rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.

For real-world automatic sleep-stage classification tasks, various existing deep learning-based models are biased toward the majority with a high proportion. Because of the unique sleep structure, most of the current polysomnography (PSG) datasets suffer an inherent class imbalance problem (CIP), in which the number of each sleep stage is severely unequal. In this study, we first define the class imbalance factor (CIF) to describe the level of CIP quantitatively. Afterward, we propose two balancing methods to alleviate this problem from the dataset quantity and the relationship between the class distribution and the applied model, respectively. The first one is to employ the data augmentation (DA) with the generative adversarial network (GAN) model and different intensities of Gaussian white noise (GWN) to balance samples, thereinto, GWN addition is specifically tailored to deep learning-based models, which can work on raw electroencephalogram (EEG) data while preserving their properties. In addition, we try to balance the relationship between the imbalanced class and biased network model to achieve a balanced state with the help of class distribution and neuroscience principles. We further propose an effective deep convolutional neural network (CNN) model utilizing bidirectional long short-term memory (Bi-LSTM) with single-channel EEG as the baseline. It is used for evaluating the efficiency of two balancing approaches on three imbalanced PSG datasets (CCSHS, Sleep-EDF, and Sleep-EDF-V1). The qualitative and quantitative evaluation of experimental results demonstrates that the proposed methods could not only show the superiority of class balancing through the confusion matrix and classwise metrics, but also get better N1 stage and whole stages classification accuracies compared to other state-of-the-art approaches. peerReviewed

BackgroundIncreased rates of methicillin-resistant Staphylococcus aureus (MRSA) antibiotic resistance and the associated morbidity have increased dermatologists' attention to skin and soft tissue MRSA infections. However, the clinical characterization of MRSA skin and soft tissue infections (SSTIs) in Southwest China is lacking, which precludes optimal prevention and treatment of these infections.ObjectivesThis study was conducted to characterize the prevalence, clinical comorbidities and antibiotic susceptibility of MRSA isolates from SSTIs, including community-associated (CA) and healthcare-associated (HA) isolates.MethodsIn the Dermatology Inpatient Department of the First Affiliated Hospital of Guangxi Medical University, a retrospective study was conducted on data, including patient demographics and clinical information, from culture-confirmed S. aureus isolated from skin and soft tissue between January 1, 2015, and December 31, 2021. Isolate susceptibility to 13 antibiotics was determined using the Vitek 2 system.ResultsFrom among 864 S. aureus strains, we identified 283 MRSA (32.75%) isolates comprising 203 CA-MRSA and 80 HA-MRSA isolates. The average rate of CA-MRSA isolation for MRSA SSTIs was 71.73%. The HA-MRSA isolation rate for MRSA SSTIs increased significantly. HA-MRSA-infected patients were older. The most common dermatological presentation of CA-MRSA infection was staphylococcal scalded skin syndrome, while the comorbidity severe drug eruption was significantly associated with HA-MRSA infection. One CA-MRSA strain was resistant to linezolid, and one HA-MRSA strain had an intermediate phenotype for vancomycin; both strains had low sensitivity to clindamycin and erythromycin (3.70%~19.40%). However, HA-MRSA isolates were more susceptible to trimethoprim/sulfamethoxazole.ConclusionsCA-MRSA is a predominant pathogen causing SSTIs, and HA-MRSA infection incidence is increasing gradually. Both strains showed increasing antibiotic resistance. Our data on MRSA susceptibility may guide dermatologist antibiotic treatment decisions. Dermatologists should consider these identified comorbidities of MRSA SSTIs when patients are admitted and initiate early prevention and treatment of MRSA.

ObjectiveThe objectives of this study was to review the clinical features and surgical treatment outcomes of congenital second branchial cleft anomalies (CSBCAs) and to investigate the characteristic computed tomography (CT) findings of CSBCAs.MethodsWe conducted a retrospective study of 52 children who were referred to Shanghai Children's Hospital from October 2014 to December 2021 diagnosed as CSBCAs.ResultsThere were 36 males and 16 females. Of them, 35 patients were presented as having a skin pit at birth or discharge from the skin opening on the lateral neck, and 17 patients presented with an asymptomatic or painful mass. The typical CT features of CSBCAs included isolated and homogeneously hypodense cystic lesions surrounded by a uniformly thin, smooth wall. CSBCAs were generally located at the anteromedial border of the sternocleidomastoid muscle, posterior to the submandibular gland, and lateral to the carotid sheath. All patients were treated surgically and only one case underwent ipsilateral tonsillectomy. After a median follow-up of 30 (range 4–90) months, no recurrence or complications were observed.ConclusionsThe CSBCAs show some characteristic CT findings, which can help clinicians diagnose and plan surgical strategies. High ligation of the lesions is sufficient for complete excision of CSBCAs.

Predicting adverse outcomes is essential for pregnant women with systemic lupus erythematosus (SLE) to minimize risks. Applying statistical analysis may be limited for the small sample size of childbearing patients, while the informative medical records could be provided. This study aimed to develop predictive models applying machine learning (ML) techniques to explore more information. We performed a retrospective analysis of 51 pregnant women exhibiting SLE, including 288 variables. After correlation analysis and feature selection, six ML models were applied to the filtered dataset. The efficiency of these overall models was evaluated by the Receiver Operating Characteristic Curve. Meanwhile, real-time models with different timespans based on gestation were also explored. Eighteen variables demonstrated statistical differences between the two groups; more than forty variables were screened out by ML variable selection strategies as contributing predictors, while the overlap of variables were the influential indicators testified by the two selection strategies. The Random Forest (RF) algorithm demonstrated the best discrimination ability under the current dataset for overall predictive models regardless of the data missing rate, while Multi-Layer Perceptron models ranked second. Meanwhile, RF achieved best performance when assessing the real-time predictive accuracy of models. ML models could compensate the limitation of statistical methods when the small sample size problem happens along with numerous variables acquired, while RF classifier performed relatively best when applied to such structured medical records.

Essential decision-making tasks such as power management in future vehicles will benefit from the development of artificial intelligence technology for safe and energy-efficient operations. To develop the technique of using neural network and deep learning in energy management of the plug-in hybrid vehicle and evaluate its advantage, this article proposes a new adaptive learning network that incorporates a deep deterministic policy gradient (DDPG) network with an adaptive neuro-fuzzy inference system (ANFIS) network. First, the ANFIS network is built using a new global K-fold fuzzy learning (GKFL) method for real-time implementation of the offline dynamic programming result. Then, the DDPG network is developed to regulate the input of the ANFIS network with the real-world reinforcement signal. The ANFIS and DDPG networks are integrated to maximize the control utility (CU), which is a function of the vehicle's energy efficiency and the battery state-of-charge. Experimental studies are conducted to testify the performance and robustness of the DDPG-ANFIS network. It has shown that the studied vehicle with the DDPG-ANFIS network achieves 8% higher CU than using the MATLAB ANFIS toolbox on the studied vehicle. In five simulated real-world driving conditions, the DDPG-ANFIS network increased the maximum mean CU value by 138% over the ANFIS-only network and 5% over the DDPG-only network.

Cyclopentanone from lignocelluloses is a promising renewable biofuel and can be used as diesel alternative fuel. Cyclopentanone has excellent fuel properties and high intramolecular oxygen content, which has the potential in promoting combustion and reducing emissions. In this study, the effects of blending cyclopentanone with diesel on the combustion and emission of common rail diesel engine were investigated. The novelty of this study is to figure out which blending ratio is the most promising for diesel engine. Cyclopentanone is blended with diesel with 10% and 20% ratio by vol., noted as C10 and C20, respectively. Double injection strategy (pre-injection and main-injection) was adopted in this study. Compared with pure diesel, blending cyclopentanone can prolong the ignition delay, increase the peak heat release rate and increase the peak in-cylinder temperature. The emission tests indicate that blending cyclopentanone can effectively reduce the particle number concentrations (PNC) and particle volume concentrations (PVC) compared to diesel. Further, the number geometric mean diameters (NGMD) of cyclopentanone diesel blend fuels are lower than those of diesel and the reduction increases with cyclopentanone blending ratio. However, a little increase in NOx emission is observed. With the increase of EGR ratio, the NOx emissions of all test fuels decrease obviously, the PNCs and PVCs of diesel and C10 increase. PNCs and PVCs of C20 almost remain unchanged when the EGR ratio is no higher than 8%. Consequently, with 8% EGR ratio, C20 has the best effect in reducing both particle and NOx emissions and it has great potential for application on diesel vehicles.

This research examined the effects of ethanol and aromatic compositions in E10 match-blends on the exhaust regulated, greenhouse gases, and unregulated toxic emissions of five China-6 compliant gasoline direct injection vehicles at room and low ambient temperatures. Emissions of a China-6 compliant gasoline and four E10 match-blends having roughly the same total aromatic concentration but containing different aromatic compositions were measured using Fourier transform infrared spectrometer, gas chromatogram-mass spectrometer, and high-performance liquid chromatogram. The results show that ethanol content decreases the fleet-averaged regulated gas and particulate mass (PM) emissions and increases the particulate number (PN), benzene, and toluene emissions. As the aromatic compositions in E10 match-blends shift from toluene to C8 and C9 aromatics, PM and PN emissions significantly increase. The low temperature leads to higher emission factors than the room temperature, especially the emissions from the fuel with more toluene content. C8 and C9 aromatics are found to have larger impacts on PM emissions but smaller impacts on PN emissions than the C10 and C10+ aromatics. It is noticed that benzene emissions at the low temperature heavily depend on the fuel toluene concentration.

ObjectiveTo explore the clinical characteristics and surgical treatment of children with bronchogenic cysts (BCs) in the head and neck region.MethodsA retrospective study of 10 pediatric patients with BCs in the head and neck region treated in Shanghai Children's Hospital during 2011 to 2022 was performed.ResultsBased on their pathological diagnosis, 10 patients with BCs in the head and neck were identified. The most common location was the neck (8 patients, 80%; 2 midline neck, 6 lateral neck), followed by the ventral tip of tongue (1 patient), and the posterior pharyngeal wall (1 patient). Misdiagnosed as lymphangioma in 5 cases, cyst in 3 cases, thyroglossal duct cyst (TGDC) in 2 cases and congenital pyriform sinus fistula (CPSF) in 1 case preoperative. The median follow-up period after surgery was 4.68 (range, 0.67–9.25) years. All 10 patients underwent complete resection without recurrence or other complications.ConclusionsAlthough extremely rare, BCs should be considered in the differential diagnosis of midline and lateral neck masses or intraoral cysts in children. Surgical excision is recommended in BCs, and the diagnosis is definitively confirmed by histopathology.

Background Pediatric temporal fistulae are rarely reported in the literature. Dissemination of these cases can help inform future diagnosis and effective treatment. Case summary Three pediatric patients came to the clinic due to repeated infections of the skin and soft tissue of the temporal area. One patient presented with a temporal fistula that penetrated the temporal bone and reached the dura mater. Another patient presented with a temporal fistula that penetrated into the temporal muscle fascia. The third patient presented with a fistula that penetrated the lateral wall of the orbit and entered the orbit. All patients underwent surgical fistula resection informed by preoperative computed tomography (CT) evaluation. Histopathological evaluation was also performed. All three patients were surgically treated successfully. Histopathological evaluations confirmed the fistula diagnoses in all three cases. Conclusion For patients who have temporal fistulae with repeated infections, surgical treatment should be performed as soon as possible to prevent serious complications. CT can be very useful for preoperative evaluation. B-mode ultrasound examination and evaluation also have a certain auxiliary role.

This article proposes a human-knowledge-integrated particle swarm optimization (Hi-PSO) scheme to globally optimize the design of the hydraulic-electromagnetic energy-harvesting shock absorber (HESA) for road vehicles. A newly developed k-fold swarm learning framework is the key to the Hi-PSO scheme, which runs k groups (folds) of individual local optimization (using a selected learning cycle), and validation (using the other k-1 testing cycles) with the concept of digital twin introduced into the design of the HESA. It aims to achieve the optimum energy recovery efficiency globally in both learning cycles and testing cycles. Within the learning framework, a nearest-neighborhood particle swarm learning algorithm is developed to incorporate human knowledge (e.g., ISO standards) for local optimization so that the computational load can be reduced through downsizing of the learning spaces. Experiments have been conducted to evaluate the energy recovery and damping performance under both local conditions (duty cycles used for learning) and global conditions (six duty cycles covering the main equivalent amplitudes and frequencies of the suspension's operation). Compared with the conventional PSO algorithm, Hi-PSO is shown to be more robust by achieving a 5.17% higher mean value in 10 trials while achieving the same maximum energy efficiency. The global optimum result is obtained under 20 mm/1.5 Hz condition and achieves an average energy efficiency of 59.07%.

This article proposes a new modular design method for hybrid powertrains using a modified accelerated particle swarm optimization (MAPSO) algorithm. The method determines the optimal combination of component specifications and control parameters, where the component specifications include integer variables (e.g., the number of battery modules). A unified chaotic attraction strategy for MAPSO is developed based on a logistic map to improve the probability of achieving the global optimal result. The Pareto analysis is carried out to identify the weighting value for the tradeoff in modular design. The comprehensive reputation score (CRS), considering both Monte Carlo results and the probability of achieving global optima, is employed to evaluate the advantages of the MAPSO compared with conventional PSO and four other PSO variants. The MAPSO is verified as the best because it has the highest CRS. Both two-level and simultaneous methods for modular design are developed with the MAPSO, where the former first operates component sizing at the level 1 and then conducts control optimization at the level 2, and the later optimizes the size and control simultaneously. Compared with the two-level method, the simultaneous method achieves a 7% higher cost function value and saves 50% time.

Injection-induced rail pressure fluctuations are proven to cause nonuniform spray development. These fluctuations are also responsible for generating lower injection pressures, to the detriment of jet penetration length and break-up timing. Despite the vast literature dealing with such issues, several aspects of rail pressure fluctuations remain unclear. Additionally, the need for compliance with the emission legislation has shed light on the potential of alternative fuels, which represent a pathway for sustainable mobility. This scenario has motivated the present study dealing with the assessment of the time history of rail pressure correlated with fuel properties. Tests have been performed using a last-generation common rail injection equipment under various injection settings, employing diesel and 2-methylfuran-diesel blend. This paper describes the research activity and aims to provide new insights into the correlation of rail pressure fluctuations with fuel properties.

Cyclopentanol is a promising alternative biofuel for automobiles. In this study, an optical engine was employed to investigate the effects of cyclopentanol blending ratios on combustion characteristics. Cyclopentanol was blended with diesel by 10% and 20% volume, denoted as CP10 and CP20, respectively. Comparison tests between blends and diesel (denoted as D100) were performed under three injection pressures (100, 120, and 140 MPa) and four injection timings (−6°, −9°, −12° and −15° crank angle after top dead center (°CA ATDC)). The experimental results indicated that the addition of cyclopentanol prolongs the ignition delay, shortens the combustion duration and improves the indicated thermal efficiency. As the injection pressure increases or the injection timing advances, the peak combustion pressures (PCPs) and peak heat release rates (PHRRs) increase. Compared to D100, the PCPs and PHRRs of cyclopentanol/diesel fuel blends are lower and decrease with the increase of cyclopentanol ratio. Blending cyclopentanol in diesel is effective in reducing soot emission, and the reduction effect weakens as the injection pressure increases or the injection timing advances. CP10 is superior to CP20 according to the comprehensive evaluation of combustion and emission, especially at the injection timing of −12°CA ATDC under the injection pressure of 140 MPa.

This work studies torque oscillation control of a hybrid driveline, and proposes a new cross-domain collaborative (C2) control strategy that comprises a notch filter, a PI filter, and a backlash estimator. The C2 control strategy is centered on a notch filter, which is built on digitalization of the driveline system and analyses of oscillation elements in the frequency domain. To enhance the performance of the notch filter in the time domain, a PI filter is connected in series to the notch filter. Considering that the driveline is a nonlinear time system during backlash travel, a backlash estimator is developed to handle the torque control target. The notch frequency and backlash travel time are identified through simulations, and the control strategy and selected parameters are evaluated in road tests. The results indicate that this control strategy can reduce the peak torsion speed to 71%.

An autonomous calibration method based on particle swarm optimization (PSO) is studied for digital twin modelling of an electrified vehicle. To enhance the model robustness and mitigate the computational cost, a hybrid terminating strategy, which is built on a min-max function of the maximum iterations and minimal error, is implemented. A three-fold cross-validation experiment is designed to determine the setting of the terminating strategy. The proposed method is superior to the conventional PSO-based methods that are terminated by maximum iterations and minimal error. It can obtain a digital twin with at least 10% less error and save 45% computing time.

In the usage phase, diesel engines fuelled with diesel-biodiesel blends produced lower soot, HC and CO emissions. However, the environmental effects should include the greenhouse gas (GHG) emissions. GHG variations of biodiesel production from three generation feedstocks were analyzed based on the land use change (LUC). Adequate land use change could keep the biodiversity and did not cause the increase of GHG emissions. The choice for feedstocks of biodiesel in China was comprehensively analyzed based on the land use type, the precipitation and the oil contents and the cultivation conditions of plants. Then, the suggestions for biodiesel development in China were given. Results showed that China should thoroughly abandon the first generation feedstocks due to the lack of arable land. Jatropha curcas (J. curcas), Pistacia chinensis Bunge (P. chinensis), Comus wilsoniana (C. wilsoniana) and Xanthoceras sorbifolium Bunge (X. sorbifolium) were considered as the most promising feedstocks for biodiesel production. It is suggested to plant X. sorbifolium in sand lands in the north and northwest of China with less natural rainfall and the others in south and southwest of China. The concern for microalgae should be transmitted to engineering microalgae cultivated in wastewater. The microalgae biodiesel refinery should be developed together with wastewater proposal industry and coal fired power plants. Lastly, only natural gas-based or biomass-based methanol can be used for biodiesel production.

This article introduces the concept of the driver-identified supervisory control system, which forms a novel architecture of adaptive energy management for hybrid electric vehicles (HEVs). As a man–machine system, the proposed system can accurately identify the human driver from natural operating signals and provides driver-identified globally optimal control policies as opposed to mere control actions. To help improve the identifiability and efficiency of this control system, the method of spectrum-guided fuzzy feature extraction (SFFE) is developed. First, the configuration of the HEV model and its control system are analyzed. Second, design procedures of the SFFE algorithm are set out to extract 15 groups of features from primitive operating signals. Third, long-term and short-term memory networks are developed as a driver recognizer and tested by the features. The driver identity maps to corresponding control policies optimized by dynamic programming. Finally, the comparative study includes involved extraction methods and their identification system performance as well as their application to HEV systems. The results demonstrate that with help of the SFFE, the driver recognizer improves identifiability by at least 10% compared to that obtained using other involved extraction methods. The improved HEV system is a significant advance over the 5.53% reduction on fuel consumption obtained by the fuzzy-logic-based system.

This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems.

Many studies have highlighted the added value of incorporating vehicle dynamics into microsimulation. Such models usually focus on simulation of conventional vehicles, failing to account for the acceleration dynamics of electrified vehicles that have different power characteristics from those of internal combustion engine vehicles (ICEV). In addition, none of them have explicitly dealt with the vehicle’s deceleration characteristics. Although it is not commonly considered critical how a vehicle decelerates, unrealistic behaviors in simulations can distort both traffic flow and emissions results. The present work builds on the lightweight microsimulation free-flow acceleration (MFC) model and proposes an extension, marking the first attempt to address these research gaps. First, a comprehensive review of dynamics-based car-following (including free-flow) models is conducted. Second, the methodology of the MFC model to capture the dynamics of electrified vehicles is described. Then, the experimental setup in different dimensions is introduced for the model validation and implementation. Finally, the results of this study indicate that: (1) the acceleration and deceleration potential curves underlying the MFC model can accurately represent real dynamics of electrified vehicles tested on the chassis dynamometer; (2) smooth transitions can be guaranteed after implementing the MFC model in microsimulation; (3) when reproducing the on-road driving trajectories, the MFC model can deliver significant reductions in root mean square error (RMSE) of speed (by ∼69%) and acceleration (by ∼50%) compared with benchmarks; (4) the MFC model can accurately predict the vehicle 0–100 km/h acceleration specifications, with RMSE 49.4% and 56.8% lower than those of the Gipps model and the intelligent driver model (IDM), respectively.

Biofuels have extensive available resources and have an immense potential as promising alternative fuels for automobile. The application advantages of biofuels are mainly reflected as particulate matter (PM) reduction, carbon neutral, greenhouse gases reduction, waste utilization, energy and economic security, and fuel pluralism. Based on the understanding of molecular structure effects of biofuels on soot formation and particles morphology, the effects of alcohols, ethers, esters and biodiesel on spray and combustion process in constant volume bomb in recent years are retrospectively analyzed in this paper. For the mixture, macromolecular ester fuels and polyoxymethylene dimethyl ether (PODE) are conducive to the improvement of liquid spray, while biodiesel, small molecules, dimethyl ether (DME) and alcohols are reversed. Alcohols are advantageous to the extension of mixing time and the increasing of vapor-phase mixture. Through the influence integrated assessment, alcohols show the best performance on the spray, atomization and combustion, while biodiesels show the worst. But in terms of combustion, PODE is the best choice without considering spray and atomization. For binary alternative-diesel fuel blends, methanol or butanol is the best additive based on synthetically considerations on spray, atomization and combustion. To meet the requirements of the fuel application of diesel engine, ternary fuel or even quaternary fuel have been proposed and explored. This review can help to form a systematic understanding on fuel recombining and obtain the guide of clean and efficient fuel formulation for diesel engine.

Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

Objectives: The purpose of this study is to explore the curative effect of open surgical excision and endoscopic radiofrequency ablation (RA) in the treatment of piriform fossa fistula (PSF). Methods: Retrospective study of 80 cases of PSF in the Department of Otolaryngology-Head and Neck Surgery, Shanghai Children’s Hospital, from June 2009 to June 2017. Results: In this series, there were 43 males and 37 females, and the mean age was 5.2 years (17 days to 12 years). Surgical excision was performed for 62 patients. Radiofrequency ablation was performed for 18 patients. Six cases of postoperative temporary hoarseness occurred, and the hoarseness rates were not significantly different between the excision and RA groups (6.4% vs 11.1%, respectively, P = .88). Two cases of temporary neck abscess occurred in the RA group. After the mean follow-up period of 3.1 years (1-8 years), no recurrence was found between the excision and RA groups. Conclusions: The curative effect of excision and RA for PSF is not significantly different; each of the 2 methods has its advantages and disadvantages. However, RA for PSF has the merit of being minimally invasive, easy to operate, and safe; this procedure seems to be more suitable in the clinic.

During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.

Background. Coronavirus disease 2019 (COVID-19) has been a global threat that pushes healthcare to its limits. Hypertension is one of the most common risk factors for cardiovascular complications in COVID-19 and is strongly associated with disease severity and mortality. To date, clinical mechanisms by which hypertension leads to increased risk in COVID-19 are still unclear. Furthermore, additional factors might increase these risks, such as the consideration of age and sex, which are of interest when in search of personalized treatments for hypertensive COVID-19 patients. Methods. We conducted a retrospective cohort study of 543 COVID-19 patients in seven provinces of China to examine the epidemiological and clinical characteristics of COVID-19 in this population and to determine risk factors of hypertensive COVID-19 patients. We also used univariable and multivariable logistic regression methods to explore the risk factors associated with hypertensive COVID-19 patients in different age and sex subgroups. Results. Among the enrolled COVID-19 patients, the median age was 47 years (interquartile range (IQR) 34.0–57.0), and 99 patients (18.23%) were over 60 years old. With regard to comorbidities, 91 patients (16.75%) were diagnosed with hypertension, followed by diabetes, coronary disease, and cerebrovascular disease. Of the hypertensive COVID-19 patients, 51 (56.04%) were male. Multivariable analysis showed that old age, comorbid diabetes or coronary heart disease on admission, increased D-dimer, increased glucose, and decreased lymphocyte count were independent risk factors associated with hypertensive COVID-19 patients. Elevated total bilirubin (odds ratio [OR]: 1.014, 95% confidence interval [CI]: 0.23–1.05;p=0.043) and triglycerides (OR: 1.173, 95% CI: 0.049–1.617;p=0.007) were found to be associated with elderly hypertensive COVID-19 patients. In addition, we found that decreased lymphocytes, basophil, high-density lipoprotein, and increased fibrinogen and creatinine were related to a higher risk of disease severity in male patients. The most common abnormal clinical findings pertaining to female hypertensive COVID-19 patients were hemoglobin, total bile acid, total protein, and low-density lipoprotein. Conclusions. Factors associated with increased risk of hypertensive COVID-19 patients were identified. Results to the different age and sex subgroups in our study will allow for better possible personalized care and also provide new insights into specific risk stratification, disease management, and treatment strategies for COVID-19 patients with hypertension in the future.