Metabolic syndrome (MetS) is characterized by insulin resistance, high blood pressure/sugar, dyslipidemia, and obesity. Whether MetS and its components affect the development of Behçet's disease (BD) ...remains unclear. This study was performed to investigate the associations between metabolic syndrome and risk of BD using nationwide population data. We conducted a retrospective cohort study of 10 505 818 Korean subjects who received health checkups in 2009–2012. Patients were classified into a MetS and its components group and were followed‐up until 2016 for new‐onset BD. A Cox proportional hazards model was used to assess the independent or synergistic effects of MetS and its components on the risk of incident BD. Compared to subjects without MetS components, the hazard ratio (HR) for development of BD in patients with MetS was 0.874 (95% confidence interval CI: 0.819–0.933) and this association was more prominent when all components of MetS were present (HR = 0.675, 95% CI = 0.571–0.798). Subjects with low high density lipoprotein (HDL) has a significantly increased risk of the development of BD (HR = 1.51, 95% CI = 1.4–1.594) compared to controls. This study showed that the incidence of Behçet's disease was reduced in subjects with MetS. Moreover, the presence of MetS components, with the exception of HDL, was negatively related to the development of BD.
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•A temperature-responsive attractive nanoemulsion (ANE) system is proposed.•Drop-to-drop association was obtained by polymer chain conformation-driven dipolar interaction across ...different oil droplets.•ANEs showed reversible sol–gel transition in response to the temperature change at upper and lower LCST.
This study introduces a temperature-responsive attractive nanoemulsion (ANE) system, which is characterized by the polymer chain conformation-driven dipolar interaction across different oil droplets in an aqueous medium. To achieve this, highly stable ANEs were produced by co-assembly of amphiphilic triblock copolymers (ATCs), poly(ethylene oxide)-b-poly(ε-caprolactone)-b-poly(ethylene oxide) (PEO-b-PCL-b-PEO), with lecithin at the oil-water interface. The dipolar attraction of the methoxy terminated-PEO (mPEO) of ATCs on one drop surface with the lecithin head located on the other drop surface led to the drop-to-drop association. We showed that the efficiency of this interdrop association was dominantly influenced by the chain conformation of mPEO blocks. From dense suspension rheology studies, it was demonstrated that the ANEs formed a gel-like phase below the lower critical solution temperature (LCST) of the mPEO, but transformed to a liquid-like phase above the LCST, which occurred reversibly, thus enabling the development of temperature-responsive emulsion fluids.
•New method was proposed to estimate the polytropic index during the compression.•The factors affecting the polytropic index were analyzed thermodynamically.•Pressure at the end of compression was ...predicted with the modelled polytropic index.•Result of the pressure prediction showed accuracy of R2 0.996 and RMSE 0.286 bar.
Compression and expansion strokes in internal combustion engines can be characterized by the well-known polytropic process, PVn = constant, which describes the relationship between the in-cylinder volume and the pressure. This process is widely used to estimate the in-cylinder pressure during the compression stroke before combustion occurs; the polytropic index (n) is a key parameter for calculating the pressure accurately when volume data are given. However, the polytropic index changes with different engine operating conditions. Therefore, using the same value for the polytropic index under different operating conditions can decrease the accuracy of the pressure prediction during the compression stroke. It can also affect pressure modelling during combustion in which the pressure at the end of the compression stroke is used as an initial condition. Therefore, it is important to obtain an appropriate polytropic index to predict the in-cylinder pressure precisely.
This study focused on a methodology to estimate the polytropic index for real-time applications. Heat loss from the in-cylinder gas was considered a major factor affecting the polytropic index. Therefore, a model of the polytropic index (n) could be established as a simple linear equation, ‘n=nisen+QlossT1×S’, where the heat loss (Qloss) is the main variable. nisen means the polytropic index with isentropic condition and T1 is the temperature of the in-cylinder gas when the compression starts. S which indicates the slope of the linear equation is inversely proportional to the compression ratio during the compression stroke. The main variable, heat loss, was modelled to complete the whole model of the polytropic index, and Woschni’s correlation was adopted to estimate the heat loss from the in-cylinder gas. The index model was established with 220 steady-state experimental cases using a 1.6-L compression-ignition (CI) engine. In addition, the pressure at the end of the compression stroke could be predicted using the modelled index with the polytropic process. The model could be validated against 1.6-L CI engine which has different compression ratio and in-cylinder geometry compared with the test engine. This study can contribute to a quantitative understanding of which parameters affect the in-cylinder pressure during the compression stroke. It will also be helpful for establishing a whole in-cylinder pressure prediction model for internal combustion engines; the pressure information obtained from such a model could be used for emissions modelling and combustion control.
Hyperpolarization techniques, in particular dissolution dynamic nuclear polarization (D-DNP), make a contribution to overcoming sensitivity limitations of magnetic resonance (MR) spectroscopy through ...signal enhancement, leading to the study of new fields of research in real time. Utilizing the large signal enhancement initially produced on small molecules, it has become possible to study systems with low γ nuclei, such as
13
C,
15
N, and
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Si. This review summarizes recent studies that have extended the applicability of D-DNP into various areas of research, especially for systems in nonequilibrium conditions that involve in vivo metabolic/molecular MR imaging for early stage disease diagnosis and real-time MR analysis of various chemical/biochemical reactions for kinetic and mechanistic studies. This review also deals with the theoretical aspects of DNP mechanisms and experimental arrangements of the dissolution setup.
To comply with the new non-road emission standards, final Tier 4, two big strategies have been applied to the 11 L heavy-duty diesel engine. The first strategy is to apply Exhaust gas recirculation ...and selectivity catalytic reduction system to meet the NOx limitation. The other one is to apply low swirl ratio (for higher volumetric efficiency) and multi-hole nozzle, a high pressure direct injection, and an optimized combustion system in order to reduce PM emission. Both strategies put a focus on the optimization of combustion system. In this study, a ULPC bowl concept applied in the previous works has been successfully verified in 11 L heavy-duty diesel engine with wellvalidated 3D combustion simulation, and the ULPC bowl shape has been geometrically optimized. A rough 0D calculation is used to calculate the fuel split ratio of the various ULPC bowl shapes. In the rated-power operating condition of the final Tier 4 engine which is deduced by 1D cyclic simulation, the optimal fuel split ratio of the injected fuel has been verified. Also, additional geometric optimization has been achieved without changing the optimal fuel split ratio. From these results, soot has been reduced by about 30% with ULPC optimum bowl shape against the Tier 3 re-entrant bowl shape.
Owing to increasing interest in the environment, particularly on air quality, regulations in the automobile industry have become stricter. Test cycles have been substituted to simulate real driving ...conditions, and they offer opportunities for researchers to satisfy regulations and predict emissions using models.
The objective of this study is to develop a deep neural network (DNN) model, optimize its hyperparameters using the Bayesian optimization method, and use hidden-node determination logic to predict engine-out NOx emissions by using the worldwide harmonized light vehicles test procedure (WLTP) of diesel engines. A DNN network learns the internal relationships between inputs and target outputs even though they are complicated. However, the hyperparameters of DNNs are typically determined by researchers before training, and they affected the accuracy of the model. In this study, the hyperparameters of the DNN model such as the number of hidden layers, number of nodes in each hidden layer, learning rate, learning rate decay, and batch size are automatically optimized using the Bayesian optimization method. Some logical equations are combined with the number of nodes in the first hidden layer and the number of hidden layers to realize the model’s structure instead of using the number of hidden nodes in each hidden layer.
Compared with grid search and random sampling, the Bayesian optimization method is a promising solution to optimize hyperparameters. In addition, a hidden-node determination logic further improved the accuracy of the model. The accuracy of the optimized model is indicated by an R2 value of 0.9675 with 14 input features. The result of cycle prediction shows that the mean absolute errors are approximately 16–17 ppm for four WLTP cycles, which are 1.6% of the maximum NOx value. These results indicate that the accuracy of the model is comparable to that of a physical NOx measurement device whose linearity is 1% of the full scale (5,000 ppm).
Soot is one of the main harmful emissions of diesel engines that is mainly generated in the reacting fuel jet of diesel injection. Over 99% of the engine-out soot can be filtered by a diesel ...particulate filter (DPF). However, when the soot load of the DPF is high, a regeneration process that oxidizes the accumulated soot reduces fuel economy. A real-time soot estimation model can contribute to real-time feedback soot control under transient conditions to minimize the engine-out soot emission and frequency of DPF regeneration.
A zero-dimensional engine-out soot estimation model for a diesel engine is developed in this study. The semi-empirical soot model considers both the formation and oxidation of soot. In the model, soot formation was correlated with the cross-sectional average equivalence ratio at the lift-off length of the fuel spray. The equivalence ratio at the lift-off length is an indicator of how much air and vaporized fuel are mixed as the fuel reaches the reaction zone. The mass of the injected fuel and combustion duration were also correlated with soot formation. The Nagle and Strickland-Constable mechanism, which calculates the soot oxidation rate was correlated with the soot oxidation in this study. The results of the soot estimation showed an R2 of 0.901 and root mean square error of 10.8 mg/m3 for steady-state experimental cases. The engine-out soot model was also combined with the in-cylinder pressure model proposed by the authors, and validated through the transient Worldwide Harmonized Light Vehicles Test Cycle (WLTC) mode. The estimates agreed with the measured soot, with an accumulated soot error of approximately 6% during the WLTC, even without using an in-cylinder pressure sensor. The soot model developed in this study can help minimize tailpipe-out soot emissions and improve fuel economy by influencing the real-time feedback control during transient and frequent DPF regeneration.
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•The semi-empirical engine-out soot model consists of formation and oxidation•Equivalence ratio at lift-off length was correlated with the soot formation•Nagle and Strickland-Constable mechanism was correlated with the soot oxidation•The soot model can estimate the engine-out soot during the transient cycle•The soot model was combined with an in-cylinder pressure estimation model
•AI is combined with expert knowledge of experiments on mechanical engineering.•Steady experiments were designed to predict transient phenomena for deep learning.•This method overcame the domain ...constraint for predicting pollutant emissions.•The accuracy results were 2.5% – 9.1% for WLTP cycles with different temperatures.•The proposed method could be applied to other mechanical systems.
Deep learning has been used to predict engine phenomena that are otherwise difficult to predict using conventional modeling approaches. Previous studies using deep learning for engine applications employed domain constraints between the training and target data. This study aimed to overcome the domain constraint when predicting the nitrogen oxides (NOx) emitted from a diesel engine by using deep learning. A steady-state dataset was designed and used to train a model for predicting transient NOx emissions. Based on analyses of the engine behaviors under transient conditions, the intake air mass, intake pressure, injection pressure, and main injection timing were set as swing variables, and data were acquired using experiments over certain ranges for the operating points. Additional experiments were conducted to consider the effects of the intake and coolant temperatures. For a total of 300 cases of the steady-state dataset, the error in the cumulative NOx mass for the Worldwide Harmonized Light Vehicles Test Procedure cycle was 2.5–9.1%. The design methodology developed in this study can be applied to predict other phenomena such as real driving emissions and other mechanical systems. This study also expanded the model’s availability to the design stages for new engine development.
Two catalytic systems, consisting of palladium nanoparticles supported by reverse phase amino functionalized silica are utilized as catalysts for Suzuki-Miyaura reaction and hydrogenation in water. ...The catalysts were developed by modifying silica into bidentate ligands, using either 2-pyridinecarboxaldehyde or 2,2′-bipyridine-4,4′-dicarboxylic acid. The synthesized catalysts showed quantitative reaction yields and recyclability with negligible leaching of Pd nanoparticles. Various characterization techniques including XPS, ICP-MS, SEM, BET, XRD, TEM, 1H- and 13C- NMR are used to verify the efficiency of the catalysts.
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•Palladium nanoparticles supported on functionalized bidentate reverse phase silica as heterogeneous catalysts.•Application of the catalysts to Suzuki-Miyaura reaction and Hydrogenation at room temperature in water.•Wide reaction scope with quantitative yields, short reaction time and recyclability.
