Improving the Quality of Wheat Flour Bread by a Thermophilic Xylanase with Ultra Activity and Stability Reconstructed by Ancestral Sequence and Computational-Aided Analysis.

Xylanase is an essential component used to hydrolyze the xylan in wheat flour to enhance the quality of bread. Presently, cold-activated xylanase is popularly utilized to aid in the development of dough. In this study, ancestral sequence reconstruction and molecular docking of xylanase and wheat xylan were used to enhance the activity and stability of a thermophilic xylanase. The results indicated that the ancestral enzyme TmxN3 exhibited significantly improved activity and thermal stability. The Vmax increased by 2.7 times, and the catalytic efficiency (Kcat/Km) increased by 1.7 times in comparison to TmxB. After being incubated at 100 °C for 120 min, it still retained 87.3% of its activity, and the half-life in 100 °C was 330 min, while the wild type xylanase was only 55 min. This resulted in an improved shelf life of bread, while adding TmxN3 considerably enhanced its quality with excellent volume and reduced hardness, chewiness, and gumminess. The results showed that the hardness was reduced by 55.2%, the chewiness was reduced by 40.11%, and the gumminess was reduced by 53.52%. To facilitate its industrial application, we further optimized the production conditions in a 5L bioreactor, and the xylanase activity reached 1.52 × 106 U/mL culture.

Models for predicting vehicle emissions: A comprehensive review.

Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %-45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is an emerging approach for capturing spatial-temporal relationships based on remote sensing data. Moreover, limited data-driven studies have been performed to forecast particle matter emissions, the main contributors to urban pollution, calling for more attention for future research.

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling.

Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes.

Real-time crash identification using connected electric vehicle operation data.

As the automobile market gradually develops towards intelligence, networking, and information-orientated, intelligent identification based on connected vehicle data becomes a key technology. Specifically, real-time crash identification using vehicle operation data can enable automotive companies to obtain timely information on the safety of user vehicle usage so that timely customer service and roadside rescue can be provided. In this paper, an accurate vehicle crash identification algorithm is developed based on machine learning techniques using electric vehicles' operation data provided by SAIC-GM-Wuling. The point of battery disconnection is identified as a potential crash event. Data before and after the battery disconnection is retrieved for feature extraction. Two different feature extraction methods are used: one directly extracts the descriptive statistical features of various variables, and the other directly unfolds the multivariate time series data. The AdaBoost algorithm is used to classify whether a potential crash event is a real crash using the constructed features. Models trained with the two different features are fused for the final outputs. The results show that the final model is simple, effective, and has a fast inference speed. The model has an F1 score of 0.98 on testing data for crash classification, and the identified crash times are all within 10 s around the true crash times. All data and code are available at https://github.com/MeixinZhu/vehicle-crash-identification.