Ternary system of pyrolytic lignin, mixed solvent, and water: phase diagram and implications

Bio-oil from biomass fast pyrolysis is considered to be an important feedstock for the production of renewable fuels and green chemicals. Fast pyrolysis bio-oil generally contains a water-soluble fraction (excluding water), a water-insoluble fraction (i.e., pyrolytic lignin, PL), and water in a single phase. However, phase separation can occur during bio-oil transport, storage, and processing. In this study, a mixed solvent (MS) is developed based on the compositions of various fast pyrolysis bio-oils produced from a wide range of feedstocks and reactor systems. Experiments are then carried out to investigate the phase behavior of the PL/MS/water ternary system. Several ternary phase diagrams are constructed for PL and its fractions, and the PL solubilities in various MS/water mixtures are also estimated. Under the experimental conditions, the PL solubility in the MS is high, i.e., ∼112 g per 100 g of MS. In the PL/MS/water system, an increase in water content to ∼17 wt % in the MS/water mixture leads to a slight increase in the PL solubility to a maximal value of ∼118 g per 100 g of MS/water mixture, followed by a gradual decrease in the PL solubility when the water content further increases. It is found that the phase stability of the PL/MS/ water system is strongly determined by the composition of the system. For example, the PL/MS/water system is always stable when the MS content is \u3e50 wt %, while the system is always phase-separated when the PL content is \u3e54 wt %. A comparison of the results for various PL fractions indicates that the molecular weight of PL can affect the ternary phase diagram, with the PL of a lower molecular weight having a higher solubility in the same MS/water mixture. The presence of free sugar (i.e., levoglucosan, present in bio-oil as solute) also influences the ternary phase diagram of the PL/MS/system, but only at a low water content (i.e., \u3c 20 wt %). The results suggest that such ternary diagrams may be potentially an important tool for predicting the phase separation of bio-oil, as a result of changes in the bio-oil chemistry in various processes (e.g., cold-water precipitation and aging). Please click Additional Files below to see the full abstract

CAN FIRM GOVERNANCE EXPLAIN THE DIFFERENCES THAT EXIST BETWEEN SALES AND EPS FORECAST ERRORS?

This study explores the differences that exist between sales and EPS forecast errors in a corporate governance’s perspective. We hypothesize that analysts have a harder time to forecast sales than EPS because firms have a greater ability to control EPS than sales figures. We also hypothesize the difference in absolute forecast error of sales and EPS is larger in weak governance firms, as these firms may tend more often to manipulate their earnings. We employ four variables as proxies of corporate governance: the number of analysts, market capitalization, institutional ownership percentage and years since IPO. We find that the better the corporate governance, the more accurate are analysts’ sales and EPS forecasts. Consistent with the idea that sales are much harder to manipulate, we find that firm with better governance has a smaller difference between the two measures of error. Overall, these results are new and may have important implications for better understanding the governance environment of firms

Convergence Theory of Learning Over-parameterized ResNet: A Full Characterization

ResNet structure has achieved great empirical success since its debut. Recent work established the convergence of learning over-parameterized ResNet with a scaling factor $\tau=1/L$ on the residual branch where $L$ is the network depth. However, it is not clear how learning ResNet behaves for other values of $\tau$. In this paper, we fully characterize the convergence theory of gradient descent for learning over-parameterized ResNet with different values of $\tau$. Specifically, with hiding logarithmic factor and constant coefficients, we show that for $\tau\le 1/\sqrt{L}$ gradient descent is guaranteed to converge to the global minma, and especially when $\tau\le 1/L$ the convergence is irrelevant of the network depth. Conversely, we show that for $\tau>L^{-\frac{1}{2}+c}$, the forward output grows at least with rate $L^c$ in expectation and then the learning fails because of gradient explosion for large $L$. This means the bound $\tau\le 1/\sqrt{L}$ is sharp for learning ResNet with arbitrary depth. To the best of our knowledge, this is the first work that studies learning ResNet with full range of $\tau$.Comment: 31 page

On Finite-Time Feedback Control for Switched Discrete-Time Systems under Fast and Slow Switching

The problem of finite-time stabilization for switched discrete-time systems under both fast and slow switching is addressed. In the fast switching case, the designed static state feedback controller combines controllers for each subsystem and resetting controller at switching instant, it is shown that the resetting controller can reduce the conservativeness on controller design. Then the results are extended to output feedback controller design. Under slow switching, both static state feedback and output feedback controller are designed with admissible average dwell time, respectively. Several numerical examples are given to illustrate the proposed results within this paper

Adjusting Community Survey Data Benchmarks for External Factors

Abstract. Using U.S. resident survey data from the National Community Survey in combination with public data from the U.S. Census and additional sources, a Voting Regressor Model was developed to establish fair benchmark values for city performance. These benchmarks were adjusted for characteristics the city cannot easily influence that contribute to confidence in local government, such as population size, demographics, and income. This adjustment allows for a more meaningful comparison and interpretation of survey results among individual cities. Methods explored for the benchmark adjustment included cluster analysis, anomaly detection, and a variety of regression techniques, including random forest, ridge, decision tree, support vector, gradient boosting, KNN, and ensembles. The final models used ensemble regression methods to predict trust in government and identify important features and cluster analysis to assign similar cities to clusters for comparison. The voting regression model predictions were compared to the actual raw scores, and cities that scored significantly above and below predictions were identified. These overperformers and underperformers may have additional factors not accounted for within the model contributing to their score