Engineering a Ce Promoter into a Three-Dimensional Porous Mo2C@NC Heterostructure for Hydrogen Evolution Electrocatalysis via Weakening the Mo-H Bond Strength.

The pursuit of highly efficient electrocatalysts for the alkaline hydrogen evolution reaction (HER) is of paramount importance for water splitting. However, it is still a formidable task in Mo2C-based materials because of the agglomeration and strong Mo-H binding of Mo2C units. Herein, a novel CeOCl-CeO2/Mo2C heterostructure nesting within a three-dimensional porous nitrogen-doped carbon matrix has been designed and used for catalyzing HER via simultaneous morphology and heterointerface engineering. As expected, the optimal CeOCl-CeO2(0.2)/Mo2C@3DNC exhibits impressive HER activity, with a low overpotential of 156 mV at a current density of 10 mA cm-2 coupled with a slight Tafel slope of 62.20 mV dec-1. Introducing a Ce promoter, that is CeOCl and CeO2, would endow the interface with an internal electric field and electron redistribution between CeOCl-CeO2 and Mo2C induced by the heterogeneous work function difference. Moreover, experimental investigation and density functional calculations confirm that the CeOCl-CeO2/Mo2C heterointerface can downshift the d-band center of the active Mo center, weakening the strength of the Mo-H coupling. This proposed concept, engineering Ce-based promoters into active entities involved in the heterostructure to modulate intermediate adsorption, offers a great opportunity for the design of superior electrocatalysts for energy conversion.

Deep learning approach for unified recognition of driver speed and lateral intentions using naturalistic driving data.

Driver intention recognition is a critical component of advanced driver assistance systems, with significant implications for improving vehicle safety, intelligence, and fuel economy. However, previous research on driver intention recognition has not fully considered the influence of the driving environment on speed intentions and has not exploited the temporal dependency inherent in the lateral intentions to prevent erroneous changes in recognition. Furthermore, the coupling of speed and lateral intentions was overlooked; they were generally considered separately. To address these limitations, a unified recognition approach for speed and lateral intentions based on deep learning is presented in this study. First, extensive naturalistic driving data are collected, and information related to road slope and driving trajectories is extracted. A comprehensive classification of driver intentions is then performed. Toeplitz inverse covariance-based clustering and trajectory clustering methods are applied separately to label speed and lateral intentions, so that the influence of driving environments and the coupling of speed and lateral intentions are integrated into intention recognition. Finally, a deep-learning-based unified recognition model for driver intention is developed. This model uses a hierarchical recognition approach for speed intentions and includes a double-layer networks architecture with long short-term memory for the recognition of lateral intention. The validation results show that the created driver intention recognition model can accurately and stably recognize both speed and lateral intentions in complex driving environments.

Principle Investigation and Method Standardization of Inhibition Zone Assay Based on Antimicrobial Peptides Extracted from Black Soldier Fly Larvae.

The black soldier fly is a valuable resource insect capable of transforming organic waste while producing antimicrobial peptides (AMPs). The inhibition zone assay (IZA) is a method used to demonstrate the antimicrobial activity of AMPs. This study aimed to examine the experimental principles and establish a standardized IZA method. Results indicated that the AMPs extract consisted of proteins ranging in molecular weights from 0 to 40 kDa. The AMPs diffused radially on an agar plate through an Oxford cup. The diffusion radius was influenced by the concentration and volume of the AMPs but ultimately determined by the mass of the AMPs. The swabbing method was found to be effective for inoculating bacteria on the agar plate. Among the conditions tested, the plate nutrient concentration was the most sensitive factor for the IZA, followed by bacterial concentration and incubation time. Optimal conditions for the IZA included a nutrient plate of 0.5× TSA, a bacterial concentration of 106 CFU/mL, and an incubation time of 12 h, with oxytetracycline (OTC) at 0.01 mg/mL serving as the positive control. The antimicrobial-specific activity of AMPs could be standardized by the ratio of inhibition zone diameters between AMPs and OTC. These findings contribute to the standardization of the IZA method for profiling the antimicrobial activity of AMPs.

Comparison of two mathematical prediction models in assessing the toxicity of heavy metal mixtures to the feeding of the nematode Caenorhabditis elegans.

The combined toxicity of four heavy metals (copper, zinc, cadmium and chromium) to the nematode Caenorhabditis elegans was determined by using feeding as an endpoint. Six equivalent-effect concentration ratio (EECR) mixtures and six uniform design concentration ratio (UDCR) mixtures were designed to fully explore the combined toxicities of these heavy metals. Observed toxicities were compared with predictions calculated by two basic models, concentration addition (CA) and independent action (IA). All the concentration-response relationships of the mixtures can be well characterized and described by the Weibull function. CA provided a relatively better prediction for the mix-toxicity of the four heavy metals, which share a similar mode of action on the feeding of C. elegans, although the prediction calculated by IA was also reliable, from the viewpoint of model prediction.

A highway crash risk assessment method based on traffic safety state division.

In order to quantitatively analyze the influence of different traffic conditions on highway crash risk, a method of crash risk assessment based on traffic safety state division is proposed in this paper. Firstly, the highway crash data and corresponding traffic data of upstream and downstream are extracted and processed by using the matched case-control method to exclude the influence of other factors on the model. Secondly, considering the weight of traffic volume, speed and occupancy, a multi-parameter fusion cluster method is applied to divide traffic safety state. In addition, the quantitative relationship between different traffic states and highway crash risk is analyzed by using Bayesian conditional logistic regression model. Finally, the results of case study show that different traffic safety conditions are in different crash risk levels. The highway traffic management department can improve the safety risk management level by focusing on the prevention and control of high-risk traffic safety conditions.

Rapid detection of carmine in black tea with spectrophotometry coupled predictive modelling.

Carmine is an artificial colorant commonly used by fraudulent food business participants in black tea adulteration, for purpose of gaining illegal profits. This study combined spectrophotometry with machine learning for rapid detection of carmine in black tea based on the spectral characteristics of tea infusion. The qualitative model demonstrated an accuracy rate of 100% for successful identification of the presence/absence of carmine in black tea. For quantitative analysis, the R2 between carmine concentrations generated according to spectral characteristics and those determined with HPLC was 0.988 and 0.972, respectively, for black tea samples involved in the test subset and an independent dataset II. Paired t-test indicated that the difference was statistically insignificant (P values of 0.26 and 0.44, respectively). The method established in this study was rapid and reliable for detecting carmine in black tea, and thus could be used as a useful tool to identify black tea adulteration in market.

Automated traffic incident detection with a smaller dataset based on generative adversarial networks.

An imbalanced and small training sample can cause an incident detection model to have a low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a novel incident detection framework is proposed based on generative adversarial networks (GANs). First, spatial and temporal rules are presented to extract variables from traffic data, which is followed by the random forest algorithm to rank the importance of variables. Then, some new incident samples are generated using GANs. Finally, the support vector machine algorithm is applied as the incident detection model. Real traffic data, which were collected from a 69.5-mile section of the I-80 highway, are used to validate the proposed approach. A total of 140 detectors are installed on the section enabling traffic flow to be measured every 30s. During 14 days, 139 incident samples and 946 nonincident samples were extracted from the raw data. Five categories of experiments are designed to evaluate whether the proposed framework can solve the small sample size problem, imbalanced sample problem, and timeliness problem in the current incident detection system. The experimental results show that our proposed framework can considerably improve the detection rate and reduce the false alarm rate of traffic incident detection. The balance of the dataset can improve the detection rate from 87.48% to 90.68% and reduce the false alarm rate from 12.76% to 7.11%. This paper lends support to further studies on combining GANs with the machine learning model to address the imbalance and small sample size problems related to intelligent transportation systems.