Lattice matching holds the secret to the Ru-catalysed hydrogenation of xylose to xylitol, a key reaction in practical biomass conversion.

This paper deals with the study and analysis of several rational approximations to approach the behavior of arbitrary-order differentiators and integrators in the frequency domain. From the Riemann–Liouville, Grünwald–Letnikov and Caputo basic definitions of arbitrary-order calculus until the reviewed approximation methods, each of them is coded in a Maple 18 environment and their behaviors are compared. For each approximation method, an application example is explained in detail. The advantages and disadvantages of each approximation method are discussed. Afterwards, two model order reduction methods are applied to each rational approximation and assist a posteriori during the synthesis process using analog electronic design or reconfigurable hardware. Examples for each reduction method are discussed, showing the drawbacks and benefits. To wrap up, this survey is very useful for beginners to get started quickly and learn arbitrary-order calculus and then to select and tune the best approximation method for a specific application in the frequency domain. Once the approximation method is selected and the rational transfer function is generated, the order can be reduced by applying a model order reduction method, with the target of facilitating the electronic synthesis.

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.

Converting hemicellulose into valuable platform chemicals is a key step in developing an integrated biorefinery. Traditionally, hemicellulose conversion into xylitol is done in two steps, using mineral acids and enzymes. Here we report a onepot hydrolysis−hydrogenation of hemicellulose to xylitol. We used a combination of either heteropoly acid or biomass-derived organic acid and Ru on carbon as catalyst. Silicotungstic acid, phosphotungstic acid, and lactic acid can be used efficiently in the hydrolysis part. Phosphomolybdic acid was not very active (<5% yield). The reduction can be done using either hydrogen gas or isopropanol as the reductant. The entire process runs in water, at relatively mild temperatures and pressures (140°C and 20 bar). Lactic acid or phosphotungstic acid combined with Ru/C gave around 70% xylitol yield in 3 h using H 2 as a reductant. With isopropanol as a reductant, phosphotungstic acid and Ru/C gave a high xylitol yield (82%), while only ∼20% xylitol yield was obtained with lactic acid.