Neural and Learning Approaches for Automotive Engine Control and Residential Energy System Optimization

Due to the tightened legislation on emissions, fuel economy, and diagnostic standards in spark ignition internal combustion engines from governments, the automotive industry is striving to minimize the emission and, at the same time, to achieve better fuel economy and vehicle driveability. Using the actual data from a test vehicle with a V8 engine, specific neural networks are trained offline to simulate engine torque dynamics and exhaust air-fuel ratio dynamics for the purpose of the identification of controllers. Validation results demonstrate that neural network models simulate the engine processes with a high degree of accuracy. The goals of the present learning control design for automotive engines include improved performance, reduced emissions and maintained optimum performance under various operating conditions. More specifically, The goal of engine torque control is to track the commanded torque. The objective of the air-fuel ratio control is to regulate the engine air-fuel ratio at specified setpoints. We applied three different learning control algorithms to solve these problems: adaptive critic learning technique, neural sliding mode control technique and biological nonlinear adaptive control technique. Simulation results for the three control techniques indicate excellent tracking control has been achieved with proper control actions. Intelligent energy management systems can help to minimize energy costs for the residential customers and reduce emissions by efficiently using renewable energy resources and distributed energy storage systems. We applied a self-learning scheme for the control and management of residential energy system. Simulation results confirmed that our proposed learning scheme can greatly benefit the residential customers with the minimum electricity charge. This technique has the potential to revolutionize the residential energy management as it reduces and shifts demand automatically, provides valuable insights to customers which ultimately save the environment by reducing the carbon footprint of power companies

Self-assembled MoS₂‑GO Framework as an Efficient Cocatalyst of CuInZnS for Visible-Light Driven Hydrogen Evolution

A ternary heterostructured CuInZnS/MoS₂-GO (graphene oxide) photocatalyst was constructed by a simple two-step hydrothermal method. The three-dimensional hierarchical architecture of MoS₂-GO hydrogel was first synthesized through a facile hydrothermal method. The obtained MoS₂-GO hydrogel with ultralow density and high surface area was redispersed into water and composite with CuInZnS. The resulting catalysts were analyzed by systematic characterizations including X-ray diffraction (XRD), transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), Raman, and UV–vis diffuse reflectance spectra (DRS), et al. The noble metal-free composite exhibited dramatically enhanced photocatalytic performance toward hydrogen evolution. The enhanced solar water splitting performance could be ascribed to the synergetic effect of GO and MoS₂. GO served as an electron acceptor and transporter while MoS₂ provided abundant active sites for hydrogen evolution. We hope this work may give some perspectives on the construction of noble-metal free catalysts for visible-light driven hydrogen production

Facilely Synthesizing Ethynyl Terminated All-Aromatic Liquid Crystalline Poly(esterimide)s with Good Processability and Thermal Resistance under Medium-Low Temperature via Direct Esterification

Developing a facile strategy to synthesize thermosetting all-aromatic liquid crystalline poly­(esterimide)­s (LCPEIs) at medium-low temperature and endowing LCPEIs with good processability and high thermal resistance are still two big challenges. Herein, a new solution polymerization based on direct esterification under 120 °C is developed, overcoming bottlenecks of traditional melt and solution polymerizations. Besides, two new reactive LCPEIs (LCPEI-1 and LCPEI-2) terminated with 3-ethynylaniline (3-EA) were synthesized, and their structures and properties were compared with two control samples without 3-EA end groups. LCPEI-1 and LCPEI-2 not only show good processing characteristics including low melting temperature (<i>T</i>_m = 200 °C), low melting viscosity, and good solubility in solvent, but their cured samples also have high glass transition temperature (<i>T</i>_g = 192 and 225 °C) and high storage modulus, whereas control samples, even treated with similar thermal history as curing procedure for LCPEI-1 and LCPEI-2, have poor performances. Cured-LCPEI-2 exhibits the highest <i>T</i>_g among polyesters with low <i>T</i>_m values (<250 °C) reported. The mechanism behind outstanding performances of LCPEIs is discussed

Interactions Affecting the Mechanical Properties of Macromolecular Microsphere Composite Hydrogels

Macromolecular microsphere composite (MMC) hydrogel is a kind of tough hydrogel fabricated by using peroxidized macromolecular microspheres as polyfunctional initiating and cross-linking centers (PFICC). The contribution of chemical cross-linking (covalent bonding) and physical cross-linking (chain entanglement and hydrogen bonding) to the mechanical properties are understood by testing the hydrogels, which were swollen in water or aqueous urea solutions to different water contents. The as-prepared MMC gels exhibited moderate moduli (60–270 kPa), high fracture tensile stresses (up to 0.54 MPa), high extensibilities (up to 2500%), and high fracture energies (270–770 J m^–2). The moduli of the swollen gels decrease dramatically, but there are no significant changes in fracture tensile strength and fracture strain, even slight increases. More interestingly, the swollen gels show much-enhanced fracture energies, higher than 2000 J m^–2. A gradual decrease in the hysteresis ratio and residual strain is also found in the cyclic tensile testing of the hydrogels that were swollen to different water contents. The covalent bonding determines the tensile strength and fracture energy of the MMC gels, whereas the physical entanglement and hydrogen bonding among the polymer chains contributes mainly to the modulus of the MMC gels, and they are also the main reason for the presence of hysteresis in the loading–unloading cycles

Additional file 2 of Knowledge, attitude and application towards fast track surgery among operating room paramedics: a cross-sectional study

Additional file 2

Le Petit Marocain

31 août 19431943/08/31 (A31,N7918)-1943/08/31

Efficacy and safety of Descemet’s membrane endothelial keratoplasty versus Descemet’s stripping endothelial keratoplasty: A systematic review and meta-analysis

<div><p>Purpose</p><p>Based on current evidence, the efficiency and safety of Descemet’s membrane endothelial keratoplasty (DMEK) was compared with that of Descemet’s stripping endothelial keratoplasty (DSEK).</p><p>Methods</p><p>Pubmed, Embase, Web of Science, the Cochrane Database and conference abstracts were comprehensively searched for studies that compared the efficacy and safety of DMEK and DSEK. The efficacy outcome was the postoperative best-corrected visual acuity (BCVA). The safety outcomes included the postoperative endothelial cell density (ECD) and complications such as graft detachment, graft rejection, graft failure, postoperative elevated intraocular pressure (IOP), tissue loss, etc. The outcomes were pooled using random-effects models with Stata 13.0 software. Heterogeneity was qualified with Q statistic and <i>I</i>^<i>2</i><i>/H</i>^<i>2</i> statistic. Publication bias was assessed using funnel plot, Begg rank correlation test, and Egger or Horbard linear regression.</p><p>Results</p><p>19 articles were eligible, and 1124 eyes and 1254 eyes were included in the DMEK and DSEK groups, respectively. The overall pooled estimates showed a significantly better postoperative BCVA, a comparable ECD and an increased graft detachment rate in the DMEK group compared with the DSEK group (BCVA: mean difference (MD) = -0.15, 95% CI = -0.19 to -0.11, P<0.001; ECD: MD = 14.88, 95% CI = -181.50 to 211.27, P = 0.882; graft detachment rate: OR = 4.56, 95% CI = 2.43 to 8.58, P<0.001). Except for the postoperative ECD, which was changed to be higher in the DSEK group than the DMEK group, the learning curve did not have a marked effect on the comparison outcome of the BCVA and graft detachment rate based on the estimates pooled from studies that collected data during the DMEK learning phase (ECD (learning curve): MD = -361.24, 95% CI = -649.42 to -73.07, P = 0.014).</p><p>Conclusion</p><p>Although DMEK is a more technically difficult and challenging procedure, it may represent a safe and more efficient alternative to DSEK for the treatment of corneal endothelial diseases, even during its learning curve.</p></div

Ammonia-Induced Size Convergence of Atomically Monodisperse Au₆ Nanoclusters

Developing effective synthetic protocols for atomically monodisperse Au nanoclusters is pivotal to their fundamental science and applications. Here, we present a novel synthetic protocol toward atomically monodisperse [Au₆(PPh₃)₆]²⁺ nanoclusters (abbreviated as Au₆) via ammonia-induced size convergence from polydisperse Au_<i>x</i> (<i>x</i> = 6–11) nanocluster mixture. The analogous ammonia-induced size conversion reactions starting from individually prepared Au₇ and Au₉ nanoclusters to Au₆ were traced by time-dependent ultraviolet–visible absorption and electrospray ionization mass spectra. It is observed that in both cases the size conversion is achieved through gradual release of the ion–molecule complex [NH₄AuPPh₃Cl]⁺ from the larger Au nanoclusters until the formation of thermodynamically stable Au₆ nanoclusters with the stability against the etching reaction. The role of ammonia ions in this size convergence synthesis is to accelerate the depletion of [Au­(PPh₃)]⁺ fragments from the PPh₃-protected Au nanoclusters, by the formation of the stable complex [NH₄AuPPh₃Cl]⁺

Naringin inhibited high glucose-induced proliferation in HBZY-1 cells.

<p>The proliferation of HBZY-1 cells was determined by MTT assay. Data were expressed as means ± SD, n = 5.^a-f Means with different superscripts are significantly different (P< 0.05).</p