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In the technological revolution of the 21st century, autonomous driving technology is rapidly changing transportation modes, and path planning, as a key component, relies heavily on advanced algorithm optimization. The Multi-Arm Bandit (MAB) algorithm may become an efficient decision optimization tool in autonomous driving path planning. Because it can continuously experiment, learn, and quickly determine the optimal strategy to maximize profits under resource constraints. When applied to autonomous driving, the MAB algorithm may be able to demonstrate its advantages. In complex traffic environments, it dynamically adjusts strategies to adapt to constantly changing road conditions, plans safe and efficient driving paths, and quickly responds to unexpected situations to ensure driving safety. Compared with other algorithms, the learning and adaptability of MAB algorithm makes it particularly suitable for the dynamics and unpredictability of real-world driving scenarios. However, the practical application of MAB algorithm in autonomous driving faces challenges, including accurately evaluating path efficiency, efficiently processing large amounts of traffic data, and ensuring the stability and reliability of the algorithm. Further in-depth research and exploration are crucial for fully utilizing the advantages of MAB algorithm in path planning and promoting the sustainable development and enhancement of autonomous driving technology.

China’s Singles Day Shopping Festivals (SDSFs) have continuously attracted consumers in recent years. As large-scale online sales events, their atmospherics have not yet been systematically analyzed. The present study aims to develop a measurement scale named “shopping festival atmospherics” (SFA) to capture the major dimensions of the atmospherics created by SDSFs. Following a rigorous procedure, both qualitative and quantitative approaches are employed to develop the measurement scale. Finally, three dimensions (cultural cues, social cues, and online cues) that contain eighteen attributes are extracted. The present study also identifies several novel shopping atmospheric cues that have not been mentioned in the literature. The happily crowded environment (“ Renao ” in the Chinese language) is a positive atmospheric cue for Chinese participants. A fair shopping environment is also a positive atmospherics cue as participants worry about price scams. Finally, the limitations and suggestions for future research are presented at the end of the paper.

Cellular micronucleus detection plays an important role in pathological toxicology detection and early cancer diagnosis. To address the challenges of tiny targets, high inter-class similarity, limited sample data and class imbalance in the field of cellular micronucleus image detection, this paper proposes a lightweight network called MobileViT-MN (Micronucleus), which integrates a multilayer perceptual attention mechanism. Considering that limited data and class imbalance may lead to overfitting of the model, we employ data augmentation to mitigate this problem. Additionally, based on domain adaptation, we innovatively introduce transfer learning. Furthermore, a novel Deep Separation-Decentralization module is designed to implement the reconstruction of the network, which employs attention mechanisms and an alternative strategy of deep separable convolution. Numerous ablation experiments are performed to validate the effectiveness of our method. The experimental results show that MobileViT-MN obtains outstanding performance on the augmented cellular micronucleus dataset. Avg_Acc reaches 0.933, F1 scores 0.971, and ROC scores 0.965. Compared with other classical algorithms, MobileViT-MN is more superior in classification performance.

A disastrous form of pedestrian behavior is a stampede occurring in an event involving a large crowd in a panic situation. To deal with such stampedes, the possibility to increase the outflow by suitably placing a pillar or some other shaped obstacles in front of the exit has been demonstrated. We present a social force based genetic algorithm to optimize the best design of architectural entities to deal with large crowds. Unlike existing literature, our simulation results indicate that appropriately placing two pillars on both sides but not in front of the door can maximize the escape efficiency. Human experiments using 80 participants correspond well with the simulations. We observed a peculiar property named tangential momentum, the escape speed and the tangential momentum are found to be negatively correlated. The idea to reduce the tangential momentum has practical implications in crowd architectural design.

It is of great significance to clarify the ranges and states of subsurface coalfield spontaneous combustion areas for coal mining and disaster management. Since the spontaneous combustion of coal seams produces highly magnetic burnt rocks and high temperatures, magnetic and infrared remote sensing measurements are commonly used for detection. To infer the accurate ranges of highly magnetic burnt rocks, we propose a three-dimensional constrained magnetization vector inversion method based on coal seam information, which considers highly magnetic burnt rocks to be produced via the combustion of a coal seam and to have thermal remanence, and this method can more accurately obtain the ranges of magnetic source for deducing coalfield spontaneous combustion areas. Combined with infrared remote sensing temperature measurement data, we analyze the range, state, and future spread direction of coalfield spontaneous combustion areas in Liaoning Province, China, according to the relative positions of high-temperature areas and highly magnetic burnt rocks. Based on the inversion results, we divided the survey area into nine blocks and obtained corresponding interpretation results. The accuracy of the interpretation was verified through drilling. This provides comprehensive spontaneous combustion area information for coal mining and disaster management.

In mining subsidence monitoring, “discrete point deformation monitoring and mining subsidence prediction model” is often used. The key to mining subsidence monitoring is to choose a convenient, economical, accurate, and reliable deformation monitoring method. In this study, the terrestrial laser scanner (TLS) with convenient, high efficiency, and high precision was used as the data acquisition method. And the Boltzmann function prediction method-exponent Knothe time function mining subsidence prediction model with high simulation degree for the deformation of rock strata above the mining affected area was constructed to calculate the surface deformation. Taking the surrounding area of South 1312 (1) working face of Gubei Coal Mine in Huainan, China as the application area, first, the model parameters are obtained by the wolf pack algorithm according to the TLS scanning point cloud data, followed by predicting the subsidence and horizontal displacement of the surrounding area. Finally, the building mining damage assessment is conducted according to the deformation of the surrounding area of the working face. The analysis results show that the mining subsidence monitoring method proposed in this paper can obtain the surface deformation in a large area affected by mining after observing a small area. The surface deformation obtained by this method is consistent with the surface deformation obtained by leveling observation. Moreover, the predicted effect of this method is better than that of the mining subsidence monitoring method using PIM-KTF model combined with TLS, and it has certain robustness to the geological and mining condition errors. The results of this work can provide a reference for predicting mining subsidence influence scope, deformation size, and mining damage assessment of mining buildings.

As an important supporting technology for carbon neutrality strategy, the combination of an integrated energy system and hydrogen storage is expected to become a key research direction. To address the insufficient flexibility of multi-energy coupling in the integrated energy system and the overall strategic demand of low-carbon development, a multi-storage integrated energy system architecture that includes electric storage, heat storage and hydrogen storage is established. Then, according to the system status factors, such as energy cost, response characteristics and energy storage status, a hierarchical energy supply control strategy focusing on the energy supply priority of energy storage units is proposed. NSGA-II-MOABC is created as the optimization algorithm to meet the demand of multi-energy coupling energy supply in the integrated energy system. Next, considering the system operational cost and carbon emission cost as the optimization goal, a comprehensive energy optimization scheduling model of multi-storage combined hierarchical energy supply is constructed. Based on the simulation example, the scheduling results of the multi-storage combined system are obtained, through comparative analysis, the addition of hydrogen storage can effectively improve the flexibility of system scheduling. Then, taking several scenarios as simulation examples, the hierarchical energy supply strategy can effectively reduce the system operation cost and carbon emission cost, and it also has a certain role in realizing the local consumption of renewable energy.

We have developed CRISPR-assisted RNA–protein interaction detection method (CARPID), which leverages CRISPR–CasRx-based RNA targeting and proximity labeling to identify binding proteins of specific long non-coding RNAs (lncRNAs) in the native cellular context. We applied CARPID to the nuclear lncRNA XIST, and it captured a list of known interacting proteins and multiple previously uncharacterized binding proteins. We generalized CARPID to explore binders of the lncRNAs DANCR and MALAT1, revealing the method’s wide applicability in identifying RNA-binding proteins. CARPID uses CRISPR technology to navigate biotin ligase to specific lncRNAs, which allows proximal labeling and thus the querying of RNA–protein interactions in living cells.

This work is concerned with investigating the effect of substrate hydrophobicity and zeta potential on the dynamics and kinetics of the initial stages of bacterial adhesion. For this purpose, bacterial pathogens Staphylococcus aureus and Escherichia coli O157:H7 were inoculated on the substrates coated with thin thiol layers (i.e., 1-octanethiol, 1-decanethiol, 1-octadecanethiol, 16-mercaptohexadecanoic acid, and 2-aminoethanethiol hydrochloride) with varying hydrophobicity and surface potential. The time-resolved adhesion data revealed a transformation from an exponential dependence to a square root dependence on time upon changing the substrate from hydrophobic or hydrophilic with a negative zeta potential value to hydrophilic with a negative zeta potential for both pathogens. The dewetting of extracellular polymeric substances (EPS) produced by E . coli O157:H7 was more noticeable on hydrophobic substrates, compared to that of S . aureus , which is attributed to the more amphiphilic nature of staphylococcal EPS. The interplay between the timescale of EPS dewetting and the inverse of the adhesion rate constant modulated the distribution of E . coli O157:H7 within microcolonies and the resultant microcolonial morphology on hydrophobic substrates. Observed trends in the formation of bacterial monolayers rather than multilayers and microcolonies rather than isolated and evenly spaced bacterial cells could be explained by a colloidal model considering van der Waals and electrostatic double-layer interactions only after introducing the contribution of elastic energy due to adhesion-induced deformations at intercellular and substrate-cell interfaces. The gained knowledge is significant in the context of identifying surfaces with greater risk of bacterial contamination and guiding the development of novel surfaces and coatings with superior bacterial antifouling characteristics.

The traditional leveling, total station, and global navigation satellite system (GNSS) and the new differential interferometric synthetic aperture radar (DInSAR) and terrestrial laser scanning (TLS) systems have their own advantages and limitations in the deformation monitoring of mining areas. It is difficult to obtain accurate deformation information only using single-source measurement data. In this study, we propose an LOS deformation correction method for DInSAR in mining areas by fusing ground data without control points. Based on free space data, small deformations at the edges of mining influence areas accurately obtained using DInSAR. By combining leveling/GNSS and TLS methods, it was possible to obtain large deformations in central areas without the need for control points located outside the mining influence range. For overcoming the non-uniform coordinates of the “space–ground” data and the limited overlap of the effective measurement ranges, the subsidence prediction model was employed to assist in its fusion. In addition, in LOS deformation correction, we retained the non-full cycle phase of DInSAR and replaced the full cycle phase with the one from the data fusion. Engineering experiments have shown that the correction results preserve the differences in the LOS deformations at the edge areas of the mine influence range, and they recover the lost LOS deformations at the center areas. Using the difference in the LOS deformation before and after correction as the verification indicator, the maximum absolute value of the errors after correction was 143 mm, which was approximately 6.4% of the maximum LOS deformation. In addition, there were still two errors that were large (−112 mm and −89 mm, respectively), and the absolute values of errors were not more than 75 mm. For all errors, the mean absolute value was 36 mm. Compared with 399 mm before correction, the error was reduced by 91%. This study provides technical support and theoretical reference for deformation monitoring and control in mining areas.