Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques

This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results

The influence of institutional fragility on corporate cash holdings: evidence from China

This study examines the relationship between institutional fragility and corporate cash holdings. Using data from China between 2004 and 2017, we find that institutional fragility is associated with increased corporate cash holdings. The relationship is stronger for non-state-owned enterprises and stronger when firms have no relationship with banks. Furthermore, we find that institutional fragility reduces current investment opportunities, leading to an increase in corporate cash holdings. Investment opportunities play an intermediary effect; hence, institutional fragility affects corporate cash holding

Study on QSTR of Benzoic Acid Compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA – 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability

2-Benzyl­imino­methyl-4-chloro­phenol

The title compound, C14H12ClNO, is a Schiff base derived from the condensation of equimolar quanti­ties of 5-chloro­salicylaldehyde and 1-benzyl­amine. The mol­ecule has a trans configuration with respect to the imine C=N double bond. The N atom is involved in an intra­molecular O—H⋯N hydrogen bond

Bis[μ-2-(benzyl­imino­meth­yl)-4-chloro­phenolato]bis[chloridocopper(II)]

The title complex, [Cu2(C14H11ClNO)2Cl2], has a centrosymmetric dinuclear structure where two symmetry-related copper(II) metal centres are bridged by the O atoms of two phen­oxy groups. Each copper(II) centre displays a distorted tetra­hedral coordination provided by one N atom and two O atoms from two Schiff base ligands and by one Cl atom. The Cu⋯Cu separation is 3.0702 (9) Å

Effect of Different Presentation Orders on Processing Time and Time Estimation of Verbal Working Memory

This research explored the effect of different presentation orders on processing time and time estimation, from the perspective of verbal working memory dual-task mode task. 108 participants took part in memorizing order or disorder French word, it showed that the presentation order significantly shortens the processing time and estimation time, thus it proved that the orderly presentation can enhance the work efficiency compared with the disorderly presentation. This research highlights the impact of presentation upon the verbal working memory, which had important theoretical and practical implications.</p

Separation and Identification of HSP-Associated Protein Complexes from Pancreatic Cancer Cell Lines Using 2D CN/SDS-PAGE Coupled with Mass Spectrometry

Protein complexes are a cornerstone of many biological processes and together they form various types of molecular machinery. A broad understanding of these protein complexes is crucial for revealing and building models of protein function and regulation. Pancreatic cancer is a highly lethal disease which is difficult to diagnose at early stage and even more difficult to cure. In this study, we applied a gradient clear native gel system combined with subsequent second-dimensional SDS-PAGE to separate protein complexes from cell lysates of SW1990 and PANC-1 pancreatic cancer cell lines with different degrees of differentiation. Ten heat-shock-protein- (HSP-) associated protein complexes were separated and identified, and the differentially expressed proteins related to cancers were also found, such as HSP60, protein disulfide-isomerase A4 (ERp72), and transitional endoplasmic reticulum ATPase (TER ATPase)