Search

Cold plasma (or non-thermal plasma) is one of the popular non-thermal processing technologies in recent years. Cold plasma technology is expected to be used as a rapid non-thermal sterilization technology in dairy processing. However, an unoptimized cold plasma process may lead to quality deterioration of dairy products, such as lipid oxidation and odor generation. Therefore, this article provides a review of the classification and mechanism of cold plasma sterilization technology and its potential impact on dairy products, with the aim of offering theoretical support for the application of cold plasma sterilization technology in the dairy industry. Future studies on cold plasma need to consider the preservation of the quality and sensory acceptability of dairy products while effectively inactivating microorganisms.

This research focuses on 72 approved varieties of colored wheat from different provinces in China. Utilizing coefficients of variation, structural equation models, and correlation analyses, six agronomic traits of colored wheat were comprehensively evaluated, followed by further research on different dwarfing genes in colored wheat. Using the entropy method revealed that among the 72 colored wheat varieties, 10 were suitable for cultivation. Variety 70 was the top-performing variety, with a comprehensive index of 87.15%. In the final established structural equation model, each agronomic trait exhibited a positive direct effect on yield. Notably, plant height, spike length, and flag leaf width had significant impacts on yield, with path coefficients of 0.55, 0.40, and 0.27. Transcriptome analysis and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) validation were used to identify three dwarfing genes controlling plant height: Rht1, Rht-D1, and Rht8. Subsequent RT-qPCR validation clustering heatmap results indicated that Rht-D1 gene expression increased with the growth of per-acre yield. Rht8 belongs to the semi-dwarf gene category and has a significant positive effect on grain yield. However, the impact of Rht1, as a dwarfing gene, on agronomic traits varies. These research findings provide crucial references for the breeding of new varieties. [ABSTRACT FROM AUTHOR]

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness., Author(s): Zixian Zhang [1,2]; Geqi Qi [1,2,3]; Avishai (Avi) Ceder [1,4]; Wei Guan (corresponding author) [1,2]; Rongge Guo [1,2]; Zhenlin Wei [1,2] 1. Introduction With respect to urban security, logistics [...]

Vehicle Routing Problem (VRP) is a widespread problem in the transportation field, which challenges the intelligent level of vehicle decisions. Multi‐Trip Vehicle Routing Problem with Time Windows (MTVRPTW), as a further evolved problem of VRP considering multiple departures from one depot and temporal constraint of visiting nodes, has developed into one of the critical issues in the scheduling of logistics, bus transit, railway, and aviation. Traditionally, MTVRPTW is solved by the heuristic algorithm, which is generally time‐consuming and of non‐steady results. Reinforcement learning (RL) and multi‐agent framework have become popular in solving VRP to get better performance. However, the lack of variant dimensions in searching space and knowledge exchange between agents inhibit the further improvement of algorithms. Therefore, a Coordinated Multi‐agent Hierarchical Deep Reinforcement Learning (CMA‐HDRL) method is proposed in this study to enhance the overall solution quality and convergence rate by constructing a three‐layered structure (time, communication, and global layers), which is particularly designed to handle the state space explosion and improve the collaboration between agents. The results show that the proposed method can significantly outperform the general genetic algorithm (GA), RL, multi‐agent algorithm, and hierarchical algorithm, not only from the effectiveness on the cost consisting of travel time and penalty time but also from the operation robustness. [ABSTRACT FROM AUTHOR]

The well-known Fuzzy C-Means (FCM) algorithm and its modified clustering derivatives have been widely applied in various fields. However, previous studies have focused on the yield of correctly clustered data, and few have addressed the alignment of extracted influential areas of clusters to natural cluster structure. Various clustering algorithms present diverse characteristics in cluster structure detection due to the different clustering principles involved. For example, Mahalanobis distance-based FCM algorithms effectively detect the influential direction of each cluster, while kernel-based FCM algorithms provide an interface for adjusting the influential range. Combining the advantages of these previous algorithms, the Adaptive Kernel Fuzzy C-Means (AKFCM) algorithm based on cluster structure is proposed in this paper. The AKFCM algorithm can effectively detect the influential direction and adjust the influential range of each cluster with adaptive kernelization. By applying the previous and AKFCM algorithms to both synthetic and real-world datasets, the proposed algorithm is proven to achieve better performance not only in clustering accuracy but also in the extraction of reasonable influential areas. The proposed algorithm could be helpful for clustering datasets composed of clusters with different directions and ranges in structure. [ABSTRACT FROM AUTHOR]

The well-known Fuzzy C-Means (FCM) algorithm and its modified clustering derivatives have been widely applied in various fields. However, previous studies have focused on the yield of correctly clustered data, and few have addressed the alignment of extracted influential areas of clusters to natural cluster structure. Various clustering algorithms present diverse characteristics in cluster structure detection due to the different clustering principles involved. For example, Mahalanobis distance-based FCM algorithms effectively detect the influential direction of each cluster, while kernel-based FCM algorithms provide an interface for adjusting the influential range. Combining the advantages of these previous algorithms, the Adaptive Kernel Fuzzy C-Means (AKFCM) algorithm based on cluster structure is proposed in this paper. The AKFCM algorithm can effectively detect the influential direction and adjust the influential range of each cluster with adaptive kernelization. By applying the previous and AKFCM algorithms to both synthetic and real-world datasets, the proposed algorithm is proven to achieve better performance not only in clustering accuracy but also in the extraction of reasonable influential areas. The proposed algorithm could be helpful for clustering datasets composed of clusters with different directions and ranges in structure. [ABSTRACT FROM AUTHOR]

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus-response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models. [ABSTRACT FROM AUTHOR]