In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy ...duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start-stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications.
Ion sensing technology has the potential to be an in-cylinder combustion diagnostic solution. However, the large-scale application of this technology still be difficult in the last decades due to the ...absence of fundamental level research on the ion current formatting mechanism. In this study, a multi-physics analysis of the ion formatting process in a premixed methane flame is conducted, and the effect of electron diffusion process on the ion current signal is explicitly studied. Through the incorporation of the flame plasma physics, ionization reaction mechanisms and ion-electric field interactions, a numerical flame ionization model is constructed. By comparing the flame schlieren imaging data and the modelling results, the ion current signal formatting mechanism is elaborated, and the effect of electron/ion motions is studied. The results show that the ion current signal amplitude is in good agreement with the flux of the charged species. More importantly, the model reveals that the ambipolar diffusion of the electrons play a dominate role in the ion current formatting process, and it will significantly affect the ion current waveform. Due to this diffusion process, electrons cannot transport outside of the flame front zone, and its distribution will be decided by the heavy ions in the flame. Consequently, a serial of experimental results about the ion current signal can be analyzed based on the constructed multi-physics model, and the findings in this work could be crucial for the development of the future ion sensing system.
A mild rhodium-catalyzed annulation of Boc-protected benzamides with diazo compounds via C-C/C-O bond formation has been explored. In the presence of Cp*RhCl₂₂, AgSbF₆ and Cs₂CO₃, Boc-protected ...benzamides can be effectively annulated to yield isocoumarins in 0.5⁻2 h.
The turbocharged diesel engine is a typical multi-input multioutput system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the exhaust emission regulation in ...turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit on board. The experimental results on a turbocharged Cat C6.6 diesel engine demonstrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables in comparison with the decoupled single-input single-output control methods.
The 6RUS parallel manipulator is a highly versatile and widely used robotic mechanism with six degrees of freedom. Its intricate kinematic structure and its capability to perform complex motion tasks ...have garnered significant research interest in recent years. The kinematic analysis of the 6RUS mechanism plays a crucial role in understanding its operational characteristics and optimizing its performance for various applications. In this paper, we present a state-of-the-art kinematic algorithm for the 6RUS parallel manipulator. Our algorithm is aimed at addressing the challenges associated with accurately determining the pose and motion of the end-effector relative to the base, considering the complexity of the mechanism’s architecture. By leveraging advanced mathematical modeling techniques and utilizing efficient computational algorithms, our proposed algorithm offers improved accuracy, efficiency, and robustness in determining the kinematic parameters of the 6RUS mechanism. The key contributions of this work include the development of a comprehensive forward and inverse kinematic model for the 6RUS parallel manipulator, incorporating the effects of joint constraints, singularities, and workspace limitations. We also present a detailed analysis of the algorithm’s performance in comparison to existing approaches, demonstrating its superiority in terms of computational efficiency and accuracy. The proposed kinematic algorithm holds significant potential for enhancing the design, control, and trajectory planning of 6RUS parallel manipulators. It provides a solid foundation for advanced applications such as robotic surgery, industrial automation, and virtual reality systems. The results presented in this paper contribute to the growing body of knowledge in parallel manipulator research and pave the way for future developments in the field.
A direct cobalt-catalyzed oxidative coupling between C(sp
)-H in unactivated benzamides and C(sp
)-H in simple alkanes, ethers and toluene derivatives was explored. This protocol achieves direct C-C ...formation without using alkyl or aryl halide surrogates and exhibits high practicality with ample substrate scope. The method provides a new way to construct linear and five- or six-membered ring moieties in bioactive molecules.
Type 1 diabetes impairs fracture healing. We tested the hypothesis that diabetes affects chondrocytes to impair fracture healing through a mechanism that involves the transcription factor FOXO1. Type ...1 diabetes was induced by streptozotocin in mice with FOXO1 deletion in chondrocytes (Col2α1Cre
FOXO1
) or littermate controls (Col2α1Cre
FOXO1
) and closed femoral fractures induced. Diabetic mice had 77% less cartilage and 30% less bone than normoglycemics evaluated histologically and by micro-computed tomography. Both were reversed with lineage-specific FOXO1 ablation. Diabetic mice had a threefold increase in osteoclasts and a two- to threefold increase in RANKL mRNA or RANKL-expressing chondrocytes compared with normoglycemics. Both parameters were rescued by FOXO1 ablation in chondrocytes. Conditions present in diabetes, high glucose (HG), and increased advanced glycation end products (AGEs) stimulated FOXO1 association with the RANKL promoter in vitro, and overexpression of FOXO1 increased RANKL promoter activity in luciferase reporter assays. HG and AGE stimulated FOXO1 nuclear localization, which was reversed by insulin and inhibitors of TLR4, histone deacetylase, nitric oxide, and reactive oxygen species. The results indicate that chondrocytes play a prominent role in diabetes-impaired fracture healing and that high levels of glucose, AGEs, and tumor necrosis factor-α, which are elevated by diabetes, alter RANKL expression in chondrocytes via FOXO1.
Healing is delayed in diabetic wounds. We previously demonstrated that lineage-specific Foxo1 deletion in keratinocytes interfered with normal wound healing and keratinocyte migration. Surprisingly, ...the same deletion of Foxo1 in diabetic wounds had the opposite effect, significantly improving the healing response. In normal glucose media, forkhead box O1 (FOXO1) enhanced keratinocyte migration through up-regulating TGFβ1. In high glucose, FOXO1 nuclear localization was induced but FOXO1 did not bind to the TGFβ1 promoter or stimulate TGFβ1 transcription. Instead, in high glucose, FOXO1 enhanced expression of serpin peptidase inhibitor, clade B (ovalbumin), member 2 (SERPINB2), and chemokine (C-C motif) ligand 20 (CCL20). The impact of high glucose on keratinocyte migration was rescued by silencing FOXO1, by reducing SERPINB2 or CCL20, or by insulin treatment. In addition, an advanced glycation end product and tumor necrosis factor had a similar regulatory effect on FOXO1 and its downstream targets and inhibited keratinocyte migration in a FOXO1-dependent manner. Thus, FOXO1 expression can positively or negatively modulate keratinocyte migration and wound healing by its differential effect on downstream targets modulated by factors present in diabetic healing.
Neutrophils play an essential role in the innate immune response to microbial infection and are particularly important in clearing bacterial infection. We investigated the role of the transcription ...factor FOXO1 in the response of neutrophils to bacterial challenge with
and
. In these experiments, the effect of lineage-specific FOXO1 deletion in LyzM.Cre
FOXO1
mice was compared with matched littermate controls. FOXO1 deletion negatively affected several critical aspects of neutrophil function
including mobilization of neutrophils from the bone marrow (BM) to the vasculature, recruitment of neutrophils to sites of bacterial inoculation, and clearance of bacteria.
FOXO1 regulated neutrophil chemotaxis and bacterial killing. Moreover, bacteria-induced expression of CXCR2 and CD11b, which are essential for several aspects of neutrophil function, was dependent on FOXO1
and
. Furthermore, FOXO1 directly interacted with the promoter regions of CXCR2 and CD11b. Bacteria-induced nuclear localization of FOXO1 was dependent upon toll-like receptor (TLR) 2 and/or TLR4 and was significantly reduced by inhibitors of reactive oxygen species (ROS and nitric oxide synthase) and deacetylases (Sirt1 and histone deacetylases). These studies show for the first time that FOXO1 activation by bacterial challenge is needed to mobilize neutrophils to transit from the BM to peripheral tissues in response to infection as well as for bacterial clearance
. Moreover, FOXO1 regulates neutrophil function that facilitates chemotaxis, phagocytosis, and bacterial killing.
Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine ...emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission information which derives from cycle-by-cycle engine combustion events, which leads to low accuracy and weak algorithm adaptability due to the inherent limitations of the encoder-decoder structure. In this paper, considering the highly nonlinear relation between the multi-dimensional engine operating parameters the engine emission data outputs, an Embedding-Graph-Neural-Network (EGNN) model was developed combined with self-attention mechanism for the adaptive graph generation part of the GNN to capture the relationship between the sequences, improve the ability of predicting long time step sequences, and reduce the number of parameters to simplify network structure. Then, a sensor embedding method was adopted to make the model adapt to the data characteristics of different sensors, so as to reduce the impact of experimental hardware on prediction accuracy. The experimental results show that under the condition of long-time step forecasting, the prediction error of our model decreased by 31.04% on average compared with five other baseline models, which demonstrates the EGNN model can potentially be used in future engine calibration procedures.
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