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Both vehicle exhaust and cooking emission are closely related to the daily life of city dwellers, which are considered as major sources of urban secondary organic aerosol (SOA). Here, we defined the SOA derived from vehicle exhaust and cooking emission as Urban Lifestyle SOA, and simulated their formation using a Gothenburg potential aerosol mass reactor (Go: PAM). After samples had been aged under 0.3–5.5 days of equivalent photochemical age, these two urban lifestyle SOA showed markedly distinct features in SOA mass growth potentials, oxidation pathways and mass spectra. The SOA / POA mass ratios of vehicle groups (107) were 44 times larger than those of cooking groups (2.38) at about 2 days of equivalent photochemical age. It reveals that organics from vehicle may undergo the alcohol/peroxide and carboxylic acid oxidation pathway to produce abundant less/more oxidized oxygenated OA (LO-OOA and MO-OOA), and only a few primary hydrocarbon-like organic aerosol (HOA) remains unaged. In contrast, organics from cooking may undergo the alcohol/peroxide oxidation pathway to produce moderate LO-OOA, and comparable primary cooking organic aerosol (COA) remains unaged. Our findings provide an insight into atmospheric contributions and chemical evolutions for urban lifestyle SOA, which would greatly influence the air quality and health risk assessments in urban areas.

Background: Microsatellite Instability (MSI) is a prognostic and predictive biomarker which can guide treatments including immunotherapy for patients with gastrointestinal cancer. Universal MSI testing would benefit to patients to receive a better therapy, but many patients remain untested. To deliver broadly accessible MSI testing, deep learning models have demonstrated their feasibility for MSI prediction using H&E-stained whole-slide images (WSIs). However, to be deployed into clinical routine care, prediction models should be validated in datasets from multicenter and multiethnic groups. In addition, these models should provide uncertainty of prediction to help clinicians to make informed decisions. Method: We develop a MSI prediction model using WSIs based on Deep Gaussian process (DGP), a Bayesian model being able to model the uncertainty in prediction. We implement a DGP model in the transfer learning, where a WPI is decomposed into multiple non-overlap image patches which are converted to feature vectors by a pretrained convolutional neural network (CNN). Then, the DGP model makes a prediction for an image by averaging score function values at all the feature vectors in the image in weakly supervised learning (WSL). To test our method, we collected H&E stained colorectal/stomach cancer slides (n=1,619) from multi-institutions and multiethnic groups, including TCGA Colorectal (CRC; n=351) and Stomach Adenocarcinoma (STAD; n=174) and six datasets from tertiary hospitals in Korea (i.e., Yonsei-1 (n=174), Yonsei-1-remade (n=146), Yonsei-2 (n=95), St. Mary-1 (n=48), St. Mary-2 (n=50), and Yonsei-CLASSIC trial (n=581)). We also compare our method to multiple deep learning methods, including Densenet, Resnet, Shufflenet, Googlenet, Squeezenet and two WSL models, MIL (Campanell et al., 2019) and CLAM (Lu et al., 2021). Result: In each cancer type, we train each model using TCGA dataset as discovery cohort and tested on the rest of datasets as validation cohort. We use the area under of roc curve (AUC) for the evaluation metric. For the CRC cohorts, our DGP method achieves overall best performance with 0.81 (Yonsei-1), 0.82 (Yonsei-1-remade), 0.89 (Yonsei-2), 0.85 (St. Mary-1) 0.75 (St. Mary-2) AUCs compared to the state-of-the art methods. For the STAD cohorts, our method also achieves overall best performance with 0.74 AUC (Yonsei-classic). In addition, incorporating the uncertainty in prediction measured by our method improves the model’s performance to predict MSI. Specifically, removing cases with prediction results with high uncertainty, which could lead false positive prediction, significantly improves MSI prediction performance. Taken together, these results demonstrate the robustness and generalisability of the DGP model for MSI prediction across multicenter and multiethnic datasets. Citation Format: Sunho Park, Hongming Xu, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Deep Gaussian process with uncertainty estimation Improves microsatellite instability prediction based on whole slide image: A retrospective multicenter and multiethnic cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5012.

Histological distribution and density of tumor infiltrating lymphocytes (TILs) are known to be informative to cancer patients’ outcome. However, due to the heterogeneity and huge size of digitized pathology slides, it has been a challenging problem to quantify TILs-related features in whole slide images (WSI). This study aimed to explore the impact of spatial distribution of TILs quantified by deep learning (DL) approaches based on digitalized WSI stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The cancer and TILs regions are first separately identified by deep learning models in the WSI. The TILs densities at tumor invasive margins are then quantified by image analysis algorithms and used for prognosis of progression-free survival (PFS) in CRC patients. The prognostic impact of spatial distributions of TILs in CRC patients was explored in the Yonsei cohort (n=180) and validated in the TCGA cohort (n=268). Two experienced pathologists manually measured TILs at the most invasive margin as 0-3 by the Klintrup-Mäkinen (KM) grading method and compared to DL approaches. On multivariate analysis, TILs densities within 200µm of the invasive margin (f_im200) was remained as the most significant prognostic factor for PFS (HR 0.004 [95% CI, 0.0001-0.1502], p=.002) 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=.024). Inter-observer agreement of manual KM grading was insignificant in both cohorts. The survival analysis by pathologists’ manual KM grading showed statistically significant different PFS from the TCGA cohort, but not the Yonsei cohort. In summary, automated quantification of TILs at the invasive margin showed a prognostic utility to predict PFS, and could provide reproducible TILs density measurement in CRC patients. Citation Format: Hongming Xu, Yoon Jin Cha, Jean Clemenceau, Jinhwan Choi, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Spatial analysis of tumor infiltrating lymphocytes in histological images using deep learning predicts progression-free survival in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 642.

Background: The tumor microenvironment is a biological environment comprising of heterogeneous cellular components, such as tumor, immune, and stromal cells. Stromal cells in the tumor microenvironment have been shown that they play an important role in cancer development, but how they are linked to patient prognosis, chemotherapy response, and immunotherapy resistance is not fully investigated yet. The present study sought to investigate the stroma composition in the tumor microenvironment by using spatial analysis and deep learning. Method: We analyzed stroma composition in Whole Slide Images (WSIs) of hematoxylin and eosin-stained formalin-fixed paraffin-embedded (FFPE) sections from 4 patient cohorts: St. Mary (37 patients), Yonsei-1 (622 patients), Yonsei-2 (66 patients), and TCGA-STAD (359 patients). St. Mary was collected from St. Mary Hospital, Seoul, Republic of Korea. Yonsei-1 and Yonsei-2 were collected from Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. Each WSI was tessellated into 256x256 um at 40X magnification and normalized using Macenko and Reinhard normalization methods to reduce the color variability of the stained tissue images. Two deep learning models (ResNet and U-Net) were used to create prediction models using Kather’s tumor dataset and a crowdsourcing stroma dataset. Models were trained over 100 epochs, using the Adam optimizer, 1e-4 learning rate, a binary loss function, and L2 regularization. Trained models were used to predict WSIs including tumor, stroma, tumor-infiltrating lymphocytes (TILs), Microsatellite instability. Spatial analysis was conducted by using Getis-Ord-Gi*, a local statistic that identifies a statistically significant hot-spot region by providing relatedness with its neighbors. Tumor hot-spot regions were computed based on z-scores in the predicted WSIs and used for understanding the spatial variation of cancer cells, recognizing statistically significant regions and their spatial relatedness. Results: In Yonsei-1, stroma composition was significantly higher in the chemotherapy high-risk group (p-value 2.9e-09). The proportion of stroma composition is significantly different with the risk groups (p-value 0.074). In TCGA-STAD, the proportion of stroma composition is significantly different with ACTA2 and Microsatellite instability (p-value 0.028). In St. Mary, Yonsei-1, and Yonsei-2, stroma composition was significantly higher in the immunotherapy treatment non-response group (p-value 0.001). Conclusion:These findings suggest that stroma composition in the tumor microenvironment is strongly associated with patient prognosis, chemotherapy response, and immunotherapy resistance on stomach cancer. Citation Format: Sanghoon Lee, Isaiah Pressman, Hongming Xu, Tae Hyun Hwang. Spatial image analysis on stroma composition linking to patient prognosis, chemotherapy response, and immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5044.

Simple Summary Currently, the optimal treatment for colorectal cancer (CRC) is planned on the basis of the results of preoperative imaging studies. Previous studies investigating the impact of radiomics signatures derived from positron-emission tomography (PET) images mainly focused on patients with rectal cancer, who underwent preoperative chemoradiotherapy, and included a relatively small number of patients, without a validation set. The impact of PET-based radiomics signature analysis in patients undergoing curative-intent radical surgery, with or without chemotherapy, has not been extensively investigated. Thus, we aimed to identify the prognostic value of radiomics signature from18F-fluorodeoxyglucose (18F-FDG) PET images by assessing the imaging features to predict the progression-free survival in patients with CRC. This study demonstrated that radiomics features derived from PET-CT images can help stratify patient prognosis and additionally increase diagnostic accuracy with respect to conventional clinicopathological data-driven prediction model in patients with CRC. Abstract The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from 18F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Background and Purpose. Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and identify the risk factors for COVID-19 patients complicated with diabetes. Methods. In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors. Results. 538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. The median age was 47 years (IQR 35.0-56.0). And the elderly patients with diabetes were more likely to have dry cough, and the alanine aminotransferase, lactate dehydrogenase, Ca, and mean hemoglobin recovery rate were higher than the other groups. Furthermore, we also found the liver and kidney function of male patients was worse than that of female patients, while female cases should be paid more attention to the occurrence of bleeding and electrolyte disorders. Moreover, advance age, blood glucose, gender, prothrombin time, and total cholesterol could be considered as risk factors for COVID-19 patients with diabetes through the multivariable logistic regression model in our study. Conclusion. The potential risk factors found in our study showed a major piece of the complex puzzle linking diabetes and COVID-19 infection. Meanwhile, focusing on gender and age factors in COVID-19 patients with or without diabetes, specific clinical characteristics, and risk factors should be paid more attention by clinicians to figure out a targeted intervention to improve clinical efficacy worldwide.

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 on the combustion and emission were investigated on a common rail diesel engine. Cyclopentanone is blended with diesel with 10% and 20% ratio by vol., noted as C10 and C20, respectively. Compared with pure diesel, blending cyclopentanone can prolong the ignition delay, increase the peak heat release ratio 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.

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

Congenital pyriform sinus fistula (CPSF) is a very rare branchial apparatus malformation. Traditional open surgery for fistulectomy might fail to excise the lesion completely, leading to continual recurrence. Herein, we report our experience of endoscopic coblation technique for treatment of CPSF in children. To observe the clinical efficacy of endoscopic coblation treatment of CPSF in children, especially for those in acute infection stage. Retrospective case series with 54 patients (including 20 cases in acute infection stage and 34 cases in non infection stage) who were diagnosed with CPSF between October 2017 to November 2019, all patients were treated with endoscopic coblation to close the piriform fossa fistula, neck abscess incision and drainage performed simultaneously for acute infection stage cases. Data collected including age of diagnosis, presenting symptoms, diagnostic methods, prior and subsequent treatments, length of hospitalization, and recurrence were analyzed. Of the 20 cases in acute infection stage, there were 3 children with transient vocal cord paresis all of which resolved with 1 month. Four children of the 34 cases in non infection stage appeared reddish swelling of the neck on the 4th, 5th, 6th, and 7th days after coblation and then underwent abscess incision and drainage. All cases experienced no recurrence, vocal cord paralysis, pharyngeal fistula and massive hemorrhage after their first endoscopic coblation of the sinus tract in the follow up of 3 to 28 months. Endoscopic coblation is an effective and safe approach for children with CPSF, neck abscess incision and drainage could be performed simultaneously in acute infection stage. We advocate using this minimally invasive technique as first line of treatment for CPSF.

Background Primary small cell carcinoma of the bladder (SCCB) is a rare disease of the genitourinary tract and reported limitedly. SCCB is very aggressive and always mixed with other histologic components, but coexistence of SCCB and prostate adenocarcinoma is extremely rare. Cases presentation Two aged males (72 and 58 years) were included in this study. Both of them presented with gross hematuria as initial symptom. Magnetic resonance imaging (MRI) demonstrated protruding lesions in the urinary bladder. Pathological examination after radical cystectomy and prostatectomy showed the concurrence of SCCB and prostate adenocarcinoma. One patient died of liver and lung metastasis 8 months after surgery, and the other patient was still alive after 19 months of follow-up. Conclusion In this paper, we reported two unusual cases of coexistence of SCCB and prostate adenocarcinoma, and reviewed relative literatures with respect to the epidemiology, clinical features, pathologic features, diagnosis, treatment and prognosis of SCCB.

This study was performed to investigate the feasibility of using a three-dimensional (3D) finite element model for laryngomalacia severity assessment. We analyzed laryngeal computed tomography images of seven children with laryngomalacia using Mimics software. The gray threshold of different tissues was distinguishable, and a 3D visualization model and finite element model were constructed. The laryngeal structure parameters were defined. The peak von Mises stress (PVMS) value was obtained through laryngeal mechanical analysis. The PVMS values of the laryngeal soft tissue and cartilage scaffolds were independently correlated with disease severity. After stress loading the model, the relationship between laryngomalacia severity and the PVMS value was apparent. However, the PVMS value of laryngeal soft tissue was not correlated with laryngomalacia severity. This study established the efficacy of a finite element model to illustrate the morphological features of the laryngeal cavity in infants with laryngomalacia. However, further study is required before widespread application of 3D finite element modeling of laryngomalacia. PVMS values of the laryngeal cartilage scaffold might be useful for assessment of laryngomalacia severity. These findings support the notion that structural abnormalities of the laryngeal cartilage may manifest as quantifiable changes in stress variants of the supraglottic larynx.

The work presented in this paper is a progression to previous research which developed an overcurrent-tolerant prediction model. This paper presents some of the modelling and development techniques used for the previous research, but more emphasis is placed on the requirements of the case study; whereby an aeroplane pushback tug is converted into a series Hybrid Electric Vehicle (HEV). An iterative design process enabled the traction motor, transmission, generator and battery pack parameters to be tailored for this vehicle’s unique duty cycle. A MATLAB/Simulink model was developed to simulate the existing internal combustion engine powertrain as well as the series HEV equivalent for comparative analysis and validation purposes. The HEV design was validated by comparing the simulation results to recorded real-world data collected from the existing vehicle (torque, speeds etc.). The HEV simulations provided greater fuel savings and reduced emissions over the daily duty cycle in comparison to the existing vehicle.

This paper reports the design optimization for a dual clutch transmission (DCT) of a multi-mode axle-split hybrid electric vehicle (HEV). The purpose of optimization is to minimize the equivalent fuel consumption of automobile power demand. The variables that need to be optimized are gear ratio and Shifting threshold value. The optimization is based on a co-simulation platform, in which, the HEV is modelled in LSM Amesim and the control/optimization algorithms are programmed in MATLAB/ Simulink. In this study, the Hybrid Particle Swarm Optimization (HPSO) algorithm is used to solve the optimization problem, which has better global and local search capability comparing with conventional PSO algorithm. The simulation results show that the optimized transmission parameters can guarantee the power demand and have a good fuel economy.

Flame kernel evolution and shock wave propagation in ethanol/air mixtures with laser ignition in a constant-volume chamber was investigated by means of high-speed Schlieren photography. At initial pressure of 0.1 MPa and temperature of 333 K, ignition was performed using a pulsed laser at the second harmonic wavelength with six different laser energies from 75 to 200 mJ, for the equivalence ratios ranging from 0.8 to 1.4. A cross-shaped plasma spot is captured ∼3.0 µs after the laser triggering, whereas the shape of the flame kernel is initially circular and then transforms to an ellipsoid structure. Following the contraction of the plasma, the flame kernel growth rate is initially very different in the four directions and then approaches a steady flame speed. The contraction of the plasma zone is accompanied by a rapid shock wave propagation where, the wave propagation speed is found to be negligibly influenced by the variations in either laser energy or mixture equivalence ratio. Under the test conditions in this study for ethanol/air mixtures, the initial shock wave speed is ∼480 m/s which decays to ∼380 m/s after 20 µs. The shock wave front propagates with a time dependency of about t0.6 when t

In order to mitigate spray collapse and fully utilize the advantage of flash boiling injection in gasoline direct injection (GDI) engines, it is important to understand the flashing jet behavior and its relationship with spray collapse. In this study, n-hexane sprays discharged from a modified single-hole injector and the original five-hole injector were studied. The tests were carried out in a constant volume vessel with injection temperatures from 30 to 130 °C and ambient pressures (Pamb) from 20 to 101 kPa. By analyzing the relationship between the jet width and different parameters including superheat level, nucleation rate, and chemical potential of phase change (Δμ), strong correlation was found between the jet width and Δμ·Pamb−0.5, indicating the radial expansion of flashing jets was determined by chemical potential of phase change and ambient resistance. Beyond 20d0, the correlation was gradually weakened along the axis, as flash boiling process was finished. Besides, it was found that the extent of single-jet radial expansion was positively related to that of multi-jet spray collapse in the transitional collapse region. Therefore, to mitigate spray collapse, it is necessary to restrict the radial jet expansion, and generating external flashing jets is proposed as a practical approach to realize it.

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off., European Transport Research Review, 12, ISSN:1867-0717, ISSN:1866-8887

This paper is to investigate the characteristics of trans-critical propane spray compared with the flash boiling spray ejected from a multi-hole injector in a constant volume chamber by using the Schlieren and backlit imaging methods. The fuel temperature ( T f ) is set from 30 °C to 120 °C, injection pressure ( p f ) from 60 bar to 120 bar and ambient pressure ( p a ) from 0.2 bar to 10 bar. The results show that the trans-critical spray has longer vapor penetration and shorter liquid penetration than the flash boiling spray, but those two sprays have similar curve liquid boundaries near the nozzle. Several discernable collapsed shock structures near the nozzle are observed at T f = 120 °C, p f =120 bar, p a = 1 bar, but they disappear when p f drops to 60 bar for the transition path may not enter the two-phase region, and the liquid phase hardly occurs. The Mach disk distance of trans-critical spray in this study is larger than that of the under-expanded ideal gas jet because of the collapse phenomenon. At T f = 120 °C, p f = 120 bar, the liquid length decreases when p a increases from 0.2 bar to 2.5 bar and increases when p a continues to rise.

Understanding spray behaviors is key for the successful employment of high injection pressure systems and biofuels such as 2-methylfuran (MF) and ethanol (ETH) in modern direct-injection gasoline engines. In this present work, the MF spray characteristics in the near-field (up to 1.6 mm) and far-field (up to 48 mm) were experimentally investigated under ultra-high injection pressure of 30, 40 and 50 MPa compared to those of ETH and isooctane (ISO). The effects of injection pressure and fuel properties on the spray morphology, tip penetration and cone angle development have been analyzed. The results showed that MF had a unique spray tip structure with a bubble attached to a mushroom tip at 30 and 40 MPa. The formation of this mushroom tip and bubble was closely related and strongly influenced by the Weber number. Among the three fuels tested, MF presented the lowest spray tip velocity at both near-field and far-field due to its large density. Compared to ETH, MF and ISO always had wider macro cone angles due to their lower viscosity.

This study presents a new hybrid and electric vehicle (HEV/EV) traction motor sizing strategy, an overcurrent-tolerant prediction model is used to estimate the dynamic and thermal characteristics of a motor operating in the overcurrent region. This can be used to determine if a prospective traction motor and powertrain configuration is able to fulfil the HEV/EVs target dynamic objectives. Since the prediction model only requires minimal motor torque-speed characteristics, it can be a useful tool during the early development stages of an HEV/EV when the detailed motor parameters used in analytical models cannot be obtained. Allowing the motor to operate in the overcurrent region could downsize the traction motor used in the final HEV/EV design to one that is smaller, easier to package and likely to run in a higher efficiency region. A case study is explored where this sizing strategy is used to convert an aeroplane pushback vehicle into a series HEV and tasked with following a rigorous duty cycle. The feasibility of two HEV configurations is then analysed further. The final HEV design reduces the fuel consumption and engine emissions by up to 52% from the original internal combustion engine powered vehicle.

This study researches an improved scheme of membership function optimisation (MFO) for fuzzy air–fuel ratio (AFR) control of gasoline direct injection (GDI) engines based on correspondence analysis (CA). This proportional–integral-like fuzzy knowledge-based controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the three-way catalyst to eliminate the exhaust emissions in real time. Different from the conventional experience-based membership function (MF) design method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: (i) specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution; (ii) based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively; (iii) the engine controller with the proposed scheme is real time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.

This paper researches on a cyber-physical energy-saving control framework for a plug-in hybrid aircraft-towing tractor, in which, an online optimization methodology named the online swarm intelligent programming (OSIP) is proposed. The new methodology obtains real-time optimal control signals from the vehicle to everything (V2X) network, and the widely used charge depleting/charge sustaining strategy is upgraded to a more adaptive and intelligent level. The energy flow of the hybrid aircraft-towing tractor with connectivity is first analyzed and modeled for OSIP. The optimal control problem is then formulated as an online integer optimization and the OSIP algorithm based on chaos-enhanced accelerated swarm optimization is developed to minimize the powertrain power loss in real-time. Finally, the advantages of the new energy management system are demonstrated and evaluated by hardware-in-the-loop testing. The results show that up to 17% fuel and 13% total energy loss can be saved via the proposed cyber-physical control.

This paper proposes a new concept of PI-like fuzzy knowledge–based controller with self-tuning capability, high robustness and rapid development capability to regulate air/fuel ratio for gasoline d...

For modern engines, the number of adjustable variables is increasing considerably. With an increase in the number of degrees of freedom and the consequent increase in the complexity of the calibration process, traditional design of experiments–based engine calibration methods are reaching their limits. As a result, an automated engine calibration approach is desired. In this paper, a model-based computational intelligence multi-objective optimization approach for gasoline direct injection engine calibration is developed, which can optimize the engine’s indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number simultaneously, by intelligently adjusting the engine actuators’ settings through Strength Pareto Evolutionary Algorithm 2. A mean-value model of gasoline direct injection engine is developed in the author’s earlier work and used to predict the performance of indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number with given value of intake valves opening timing, exhaust valves closing timing, spark timing, injection timing, and rail pressure. Then a co-simulation platform is established for the introduced intelligence engine calibration approach in the given engine operating condition. The co-simulation study and experimental validation results suggest that the developed intelligence calibration approach can find the optimal gasoline direct injection engine actuators’ settings with acceptable accuracy in much less time, compared to the traditional approach.

In this research, n-hexane jets discharged from a single-hole gasoline direct injector (GDI) were studied in a constant volume vessel. The injection temperature ranged from 20 °C to 100 °C, and the ambient pressure varied from 4 kPa to 101 kPa. The jets were in the external flashing regime over all the superheated conditions with the maximum Rp of 60.23. At the ambient pressures lower than 10 kPa, a liquid core can be observed inside the sprays, surrounded by the mist composed of fine droplets. With the increase in the injection temperature, the length of the liquid core decreased and the mist area became wider. By correlating the nucleation rate with the spray width, it was found that the intensity of bubble blast was positively related to the nucleation rate when the nucleation rates were relatively low. However, as the nucleation rate reached a critical point, the thermal energy for vaporization became the rate-controlling factor. Furthermore, realizing external flashing was proposed as a potential way to mitigate the collapse of multi-jet flashing GDI sprays because it could suppress the severe jet overlap near the nozzle exit, which was believed as one of the necessary conditions for the spray collapse.

The current optical and thermal experimental tests were mainly focused on obtaining a comprehensive understanding of the effects of gasoline direct injector fouling on mass flow rates, spray characteristics, engine emissions and engine performance. A robust injector fouling cycle was employed to produce coked injectors in a multi-cylinder thermal engine. Deposit build-up in the coked injectors and fouling cycle repeatability was first examined by measurements of fuel flow rate. Macroscopic spray characteristics of the clean and the coked injectors were carried out using high-speed imaging and Planar Laser Induced Fluorescence (PLIF) of sprays foot-print. Fuel droplets size and velocity were characterised with a two-dimensional Phase Doppler Particle Analyser (PDPA). It was observed that the deposit build-up inside injector nozzles and on injector tips reduced the plume cone angle while increased plume penetration length, plume separation angles, mean droplet velocity and size for the coked injector. Impact of injector fouling was further investigated by PLIF measurements of in-cylinder charge inhomogeneity and repeatability in mixture preparation. The coked injectors showed higher degree of inhomogeneity and poorer repeatability in mixture preparation. These were in agreement with combustion analysis results where the coked injectors showed lower load and lower combustion stability compared with the clean injector under same operating conditions. Significantly higher unburned hydrocarbon emissions and particulate number concentration were also observed for the coked injectors. This work was carried out to obtain a broad picture of injector cooking effects in GDI engines.

Spray and droplet characteristics of a coked injector were compared to those of a clean injector at the atmospheric conditions and investigated using high-speed imaging and a Phase Doppler Particle Analyzer (PDPA). Scanning Electron Microscope (SEM) and X-ray 3D microtomography images were analysed to understand the physical characteristics of injector nozzle deposits. Furthermore, Energy Dispersive X-Ray Spectroscopy (EDS) was utilized to obtain the elemental composition of the deposit. A single cylinder optical gasoline direct injection (GDI) engine was used to compare diffusion flames for each injector. In this study, the location and topography of the deposits demonstrated that they extensively formed in the external holes of the injector, and reduced in size and quantity through the internal holes. Elemental analysis of the deposits exhibited that carbon (C) and oxygen (O) were the predominant elemental components through both the internal and external holes of the injector. The coked injector exhibited higher penetration lengths, smaller plume angles, larger spray cone angles, higher mean droplet velocity and larger droplet size as compared to the clean injector. Images of the optical engine indicated strong diffusion flames around the coked injector tip. In-cylinder pressure measurements indicated that the coked injector produced lower in-cylinder pressures, implying lower combustion stability compared with the clean injector. This work was carried out to obtain a comprehensive study of injector coking effects on spray behaviour and engine performance.

This article proposes an intelligent transient calibration method for the air-path controller of a light-duty diesel engine. This method is developed based on the chaos-enhanced accelerated particle swarm optimization algorithm. The target is to reduce the engine’s fuel consumption during transient scenarios by optimizing the controller parameters. The advanced dual-loop exhaust gas recirculation system is first introduced. Then, it formulates the transient calibration process as a multiple-objective optimization problem with constraints. Different from steady state calibration, the proposed method designs a new cost-function to evaluate the controller’s transient performance. The intelligent transient calibration module is programmed in MATLAB code. Interface between the calibration module and a physical engine plant is established via ETAS INCA. The optimization result of the proposal method is discussed by comparing it with the result of existing calibration methods. The engine performance with the calibrated controller is evaluated based on engine tests.

Airborne particles and vapours, like many other environmental samples including water, soils and sediments, contain complex mixtures of hydrocarbons, often deriving from crude oil either before or after fractionation into fuels, lubricants and feedstocks. Comprehensive 2D gas chromatography time-of-flight mass spectrometry (GC × GC-ToF-MS), offers a very powerful technique that separates and identifies many compounds in complicated hydrocarbon mixtures. However, quantification and identification of individual constituents at high ionization energies would require hundreds of expensive (when available) standards for calibration. Although the precise chemical structure of hydrocarbons does matter for their environmental impact and fate, strong similarities can be expected for compounds having very similar chemical structures and carbon numbers. There is, therefore, a clear benefit in an analytical technique which is specific enough to separate different classes of compounds and to distinguish homologous series while avoiding the need to handle each isomer individually. Varying EI (electron impact) ionization mass spectrometry significantly enhances the identification of individual isomers and homologous compound groups, which we refer to as “isomer sets”. Advances are reported in mapping and quantifying isomer sets of hydrocarbons (≥ C12) in diesel fuel, lubricating oil and diesel exhaust emissions. By using this analysis we report mass closures of ca. 90 and 75 % for diesel fuel and lubricating oil, and identify 85 and 75 % of the total ion current for gas- and particulate-phase diesel exhaust emissions.

A number of major studies have demonstrated that the SVOC (Semi-volatile organic compounds) within engine emissions derive predominantly from unburned fuel and lubricants, and are a major contributor to primary atmospheric aerosol containing thousands of organic compounds. The GC × GC-ToF-MS (2 dimensional Gas Chromatography – Time of Flight – Mass Spectrometry) comprehensive analytical technique was utilized in this study, to resolve the complex mixtures and characterize the SVOC content in eight different commercial lubricants, including 5 W30 synthetic and semi-synthetic, mineral and base oil. In order to quantify the aliphatic isomers, which comprise the largest component of the lubricants, a TIC-M.Q./Mass (Total ion current ratio to the molar quantity/mass) method has been developed. The TIC intensity was observed to be proportional to the molar quantity of n-alkanes for carbon number C25. Additionally, the TIC intensity of the alkyl-cyclohexanes under the identical retention indices were found to have an equivalent response to those of the n-alkanes, showing that the quantitative calibrations derived for the n-alkane series could be applied to estimate the concentrations of isomeric aliphatic compounds with similar molecular weight. Furthermore, a mesh method was introduced to group the alkane species (n-alkanes, branched alkanes and cyclic alkanes etc.), combining with the use of a soft EI (electron impact) ionization (14 eV) to retain the distinct identity of the isomers with less fragmentation, which allowed the TIC-M.Q./Mass methodology to integrate all the constitutional isomers present in the lubricating oil samples. By utilizing this methodology, compositions from different samples were comprehensively compared, leading to the following conclusions: 1) the synthetic and semi-synthetic oils contained a larger abundance of HMW (high molecular weight) aliphatic compounds (carbon number C24–C29), while those in the LMW (low molecular weight range, carbon number C18–C25) were predominant in the mineral and base oil; 2) cycloalkanes were predominant in the synthetic and semi-synthetic oils; whereas the branched alkanes were more prominent in the mineral and base oils; 3) for lubricants used for a short period, a slight increase of LMW compounds was observed, while the HMW compounds underwent a decrease, whereas, there was an overall mass reduction for all the aliphatic compounds detected in the oil samples used for six-months.

AlxCoCrFeNiTi1-x (x = 1.0, 0.8, 0.5 in molar ratio) high entropy alloys (abbreviated as Al-1.0, Al-0.8, Al-0.5 in the paper) were fabricated by repeated arc-melting and casting under vacuum. the effects of Al being replaced partially by Ti on the microstructure, mechanical properties and corrosion resistance in the solutions of 3.5 wt% NaCl, 0.5 mol/L NaOH and 0.5 mol/L H2SO4 were studied. The microstructure analysis showed that the Al-1.0 is mainly composed of single-phase BCC solid solution, while the Al-0.8 and the Al-0.5 consist of BCC and FCC biphasic solid solutions. With the increase of Ti atomic content, the elemental segregation intensifies, the FCC phase increases, and the BCC phase decreases. The results of hardness test and compressive test showed that all of the three alloys have good comprehensive mechanical properties, especially the Al-0.8. In the NaCl and NaOH solutions, the Al-1.0 and the Al-0.5 have similar corrosion resistance, but the Al-1.0 is a better choice out of economic consideration. In the H2SO4 solution, the Al-0.5 has the best corrosion resistance, followed by the A1-1.0 and the Al-0.8. A small amount of Ti addition in AlxCoCrFeNiTi1-x cannot offset the negative effects of the biphasic structure and the intense elemental segregation on the corrosion resistance and the corrosion resistance can be improved obviously Only when the atomic percentage of Ti exceeds that of Al.

Gasoline direct injection (GDI) engines are currently the dominant powertrains for passenger cars. With the implementation of increasingly stringent fuel consumption and emission regulations worldwide, GDI engines are facing challenges owing to high particulate matter emissions and a tendency to knock, leading to a change in the research and design (R&D) issues compared with those in the twentieth century. This paper reviews the progress in research regarding GDI engine technologies over the past 20 years, focusing on combustion system configurations, and also highlights common issues in GDI R&D, including pre-ignition and deto-knock, soot formation and PM emissions, injector deposits and gasoline compression ignition (GCI). First, an overview of recent developments in the field as driven by regulations is provided, following which progress in injection and combustion systems is examined. Third, the review addresses the occurrence and mechanism of deto-knock and considers means of suppressing this phenomenon. The fourth section discusses soot formation mechanisms and particulate matter emission characteristics of GDI engines and describes the application of gasoline particulate filter (GPF) after-treatment. The subsequent section summarizes studies regarding injector deposit formation, as well as pioneering research into GCI combustion modes. Finally, a summary and future prospects for GDI engine technologies are provided.

Atomization of fuel sprays is a key factor in the control of combustion quality in direct-injection engines. In the present work, the near-field spray patterns of 2-methylfuran (MF), ethanol (ETH) and isooctane (ISO) under non-flash boiling and flash boiling conditions were investigated using an ultra-highspeed imaging technique. Fuel was injected from a single-hole solenoid injector into an optically accessible constant volume chamber at the injection pressure of 40 MPa. Various conditions were tested, ranging from non-flash boiling conditions (ambient) to flare-flash boiling conditions with fuel temperatures of 20 °C and 80 °C and different back pressures. High-speed imaging was performed using a long-distance microscope coupled with an ultra-highspeed camera (1 million fps). Results showed that under flash boiling conditions, near-nozzle spray patterns changed significantly and clear radial expansion was observed due to bubble formation and explosion. Among the three fuels, MF showed the most intense flash boiling behavior due to it having the highest vapor pressure. The effects of different non-dimensional numbers were also considered and it was found that saturation ratio and cavitation number were the two main governing factors for the near-nozzle spray behaviors. During the end of the injection process, the low effective pressure led to poorly atomized spray with a compact liquid column and large ligaments; this could result in poor air/fuel mixing and thus higher HC and particle emissions. Significant improvements were observed at Rs = 0.2 where flash boiling greatly promoted the spray atomization, even with low fuel velocity.

Objective Lymphatic malformations (LMs) are rare and benign diseases. This article aimed to present a series of surgically treated giant cervicofacial LMs in children. Methods A retrospective analysis of giant cervicofacial LMs (tumor diameter greater than 10 cm) in children over the past 8 years was performed in our department. Results The 10 patients included 4 males and 6 females. Macrocystic lesion presented in 4 patients, mixed cystic lesion presented in 4 patients, and microcystic lesion presented in 2 patients. All the patients underwent surgical resection combined with bleomycin sclerotherapy. Despite the wide range of lesions, most patients (7/10, 70%) received only a single treatment, 3 patients had a recurrent lesion. Seven patients experienced complete or almost complete resolution of the disease with an average follow-up time of 26.4 months. Conclusions Surgical resection is the preferred method for the treatment of giant LMs, combination of bleomycin sclerotherapy can reduce the recurrence rate. The curative effect of macrocystic disease is relatively enhanced compared with microcystic and mixed cystic diseases.

Electric vehicles, including plug-in hybrids, are important for achieving net-zero emission and will dominate road transportation in the future. Energy management, which optimizes the onboard energy usage, is a critical functionality of electric vehicles. It is usually developed following the model-based routine, which is conventionally costly and time-consuming and is hard to meet the increasing market competition in the digital era. To reduce the development workload for the energy management controller, this paper studies an innovative transfer learning routine. A new transferable representation control model is proposed by incorporating two promising artificial intelligence technologies, adaptive neural fuzzy inference system and Gaussian process regression, where the former applies k-fold cross valudation to build a neural fuzzy system for real-time implementation of offline optimization result, and the later connects the neural fuzzy system with a ‘deeper’ architecture to transfer the offline optimization knowledge learnt at source domain to new target domains. By introducing a concept of control utility that evaluates vehicle energy efficiency with a penalty on usage of battery energy, experimental evaluations based on the hardware-in-the-loop testing platform are conducted. Competitive real-time control ultility values (as much as 90% of offline benchmarking results) can be achieved by the proposed control method. They are over 27% higher than that achieved by the neural-network-based model.

The present study is geared towards the investigation of the impact of fuel temperature, ambient pressure, and fuel properties on the spray characteristics of a 6-hole gasoline direct injector. High speed imaging is employed to verify the spray structural variation of various fuels including iso-octane, gasoline, methanol, and ethanol over wide range of operating conditions in an optical vessel. These structural parameters were correlated to the ratio of the ambient to saturation pressure ratio (Pa/Ps) that represents the superheated degree. At a constant pressure of 150 bar the fuel was injected, and the fuel temperature ranged between 25 and 120 °C, whilst the ambient pressure ranged from 0.1 to 1 bar. The results indicated that for all the fuels, the spray width was reduced, and the adjacent plumes collapsed into single bulk as the (Pa/Ps) decreased. Furthermore, at very low (Pa/Ps = 0.065), the spray getting longer, and for iso-octane the fuel spray shrinkage toward the injector centreline and shaped like a Fish with sharpen spray tip. The spray cone angle for ethanol, methanol, and gasoline was reduced when the Pa/Ps ratio was decreased from 1 to 0.16 with the exception that at Pa/Ps = 0.54 for ethanol the cone angle was increased and then decreased with the reduction of Pa/Ps. Then a massive increase in the cone angle was noticed by reducing the Pa/Ps ratio reduced from 0.16 to 0.065. For iso-octane, the behaviour is chaotic and not follow any specific trend with the reduction of Pa/Ps. Furthermore, the appearance of interstitial streams in the gaps between the original spray streams was noticed at Pa/Ps ratio ≤ 0.28. Phase Doppler data showed for the Pa/Ps ratios of 0.85, 0.54, and 0.28, that the interstellar streams consistently had a lower mean droplets velocity and a very narrow head stage compared to that of spray main streams. Moreover, reducing the Pa/Ps ratios of 0.85, 0.28, resulted in a significant reduction in Sauter mean diameter with approximately 49.5%.

Flash boiling injection can significantly promote spray atomization owing to rapid bubble growth and explosion. Nevertheless, the effects of component proportions on spray expansion of multi-component fuels still need more quantitative studies. In this work, a typical non-ideal binary solution (aqueous ethanol solution) was utilized as the injection fluid. The effects of ethanol proportion at varying injection temperatures and ambient pressures were tested in a constant volume vessel. Both increasing injection temperature and decreasing ambient pressure intensified the flashing instant expansion for all tested fluids. Instant expansion of pure water spray can be intensified by adding ethanol but adding ethanol into the aqueous ethanol solution induced an opposite behavior. Such phenomenon was caused by the combined effect of differences in heat capacity and proportions of evaporation components. Hence, superheat level was no longer a suitable index for evaluating the instant expansion between sprays with different component proportions. A new index ΔQ′ was proposed to quantify the expansion extent of binary solutions. A strong correlation was found between expansion extent and ΔQ′∙Pamb−0.75 over a wide condition range, indicating the superheated energy available for the first-vaporizable part of the solution is an important factor controlling the extent of instant expansion for binary solution spray.

Bioenergy attracts more attention owing to the reduction of both air pollution and greenhouse gas emissions in a whole life cycle compared to fossil fuels. As a third-generation biofuel, Microalgae Oil (MAO) can utilise carbon dioxide and light energy at an increased photosynthetic efficiency compared to energy crops for biomass. Due to the wide variety of MAO and their blends with diesel in different ratios, characterization of these biofuels’ engine performance is difficult to be standardized, e.g., in-cylinder pressure. This paper proposes a novel approach of geometric neuro-fuzzy transfer learning (GNFTL) for in-cylinder pressure modelling of a diesel engine fuelled with MAO. Inspired by computational geometry, this approach only utilizes limited experimental data obtained by geometric screening to learn a high-precise transfer model of the in-cylinder pressure with different MAO blending ratios. Followed by the process of MAO extraction and test cell description, the proposed approach of GNFTL is presented which comprises geometric transfer domain segmentation and neuro-fuzzy transfer learning. By a comprehensive study, the results demonstrate that the proposed approach can achieve a competitive prediction accuracy whilst significantly reducing experimental efforts on used biofuel by 47.8% and operation time by 41.5%, compared to the conventional manual design of experiment.