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This study seeks to empirically explore the relation between multilingual learning experiences and language aptitude. Through employing LLAMA aptitude test battery (Meara, 2005) and a probabilistic version of the serial reaction time (SRT) task (Kaufman et al., 2010), scores from 24 Chinese-English bilinguals and 24 Tibetan-Chinese-English trilinguals were analyzed with One-way Analyses of Variance (ANOVA). LLAMA-B, E, and F sub-tests measured explicit language aptitude, while LLAMA-D sub-test and SRT task measured implicit language aptitude, a cutting-edge that has gained sway in aptitude research. Qualitative and quantitative results showed that trilingual group performed better on the LLAMA-E sub-test than bilingual group, whereas bilingual group outperformed trilingual group on the SRT task. These findings suggested that trilinguals might possess higher explicit aptitude but lower implicit aptitude than bilinguals. Thus, prior language learning experiences might be positively (for explicit aptitude) or negatively (for implicit aptitude) correlated with language aptitude. Additionally, explicit aptitude and implicit aptitude might have a competitive relationship. Possible implications were discussed in this article.

In view of the complex environments and varying object scales in drone-captured imagery, a novel PARE-YOLO algorithm based on YOLOv8 for small object detection is proposed. This model enhances feature extraction and fusion across multiple scales through a restructured neck network. Additionally, it incorporates a lightweight detection head that is optimized for small objects, thereby significantly improving detection performance in cluttered and intricate backgrounds. To further enhance the extraction of small object features, the conventional C2f is replaced with a novel architecture. Moreover, the EMA-GIoU loss function is proposed to mitigate class imbalance and enhance robustness, particularly in scenarios characterized by skewed class distributions. Evaluation on the VisDrone2019 dataset indicates that PARE-YOLO achieves a 5.9% improvement in mean Average Precision (mAP) at a threshold of 0.5, compared to the original YOLOv8 model. In addition, the PARE-YOLO model exhibits significant robustness, achieving a mean Average Precision (mAP) at a threshold of 0.5 values on the HIT-UAV dataset that are 0.8%, 0.5%, and 1.2% higher than those of YOLOv8, YOLOv10, and RT-DETR. These results underscore the effectiveness of PARE-YOLO in addressing the challenges inherent in aerial scenarios. The code will be available online (https://github.com/Sunnyxiao69/PARE-YOLO). [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated deformation calculation method for foundation pit engineering to improve deformation control between underground diaphragm walls and supports. The study validates the practicality of the coordinated deformation formula through actual engineering applications and simulation analysis, making it particularly suitable for foundation pit calculations with external overloads or additional loads caused by outer pit slopes. Finite-element software PLAXIS 3D (version 2022) is used for simulation, with the soil modeled using the hardening small strain constitutive model. When considering support positions and construction sequences, the calculations found that supports placed near the top of the wall and early installation of supports are more effective in controlling deformation. Additionally, the calculations show that the slope height of the outer pit, and the relative position between the outer and inner pits, significantly affect the deformation of the inner pit support structure. The smaller the outer pit slope height and the greater the distance from the outer pit to the inner pit, the less deformation occurs in the underground diaphragm wall. This method simplifies the calculation process and is applicable to various foundation pit excavation projects. [ABSTRACT FROM AUTHOR]

As underground structures' burial depth increases, buoyancy resistance due to groundwater becomes more pronounced. This study, through numerical simulation, analysis of field measurement data, and theoretical analysis, explores the impact of changes in groundwater level on the failure mode and uplift resistance of expanded base piles and proposes a new method for calculating the ultimate uplift capacity of expanded base piles considering the effect of groundwater. The research shows that the rise in groundwater level significantly affects the uplift performance of expanded base piles by altering the physical and mechanical properties of the soil and the morphology of the pile–soil failure surface, thereby affecting the pile's load-bearing capacity. The study identifies a three-segment failure mode for expanded base piles and notes that as the groundwater level rises, the extent of the failure surface gradually expands. Additionally, the paper underscores the importance of considering groundwater levels in practical engineering design and suggests re-evaluating the measured uplift capacity using the calculation method proposed in this study to ensure engineering safety. This research provides a theoretical basis and computational tools for designing belled uplift piles under the influence of groundwater, offering significant reference value for engineering practice. [ABSTRACT FROM AUTHOR]

Digital finance provides new possible ways to promote innovation-driven development strategies. Using data of 257 prefecture-level cities in China from 2011 to 2019, this paper examines the impact of digital finance on regional innovation, as well as the mediating effect of resource mismatch underlies it. The empirical results are as follows. First, digital finance can significantly boost innovation, both in terms of the quantity and quality. Second, the incentive effects of innovation have been more obviously observed in the eastern cities and the cities with high financial levels or light digital divides in China. Third, the mechanism effect implies that digital finance works positively on innovation through improving the factor allocation efficiency by alleviating the misallocation of labour and capital. These conclusions indicate that to better promote regional innovation, it is crucial to encourage the digital transformation of the financial industry, enhance digital infrastructure development, and facilitate the free flow of resources among regions. The paper offers both theoretical and empirical evidence that digital finance influences regional innovation by alleviating resource mismatches. It also examines the role of the ‘digital divide’ in shaping innovation through digital finance. These two points constitute the key innovations of the paper. [ABSTRACT FROM AUTHOR]

In the realm of intelligent cockpits, the predominant emphasis on enhancing driving safety and comfort through human‐vehicle interaction and driving behavior monitoring has conventionally centered on the upper body of the driver. Regrettably, the wealth of information inherent in the lower half of the driver's body, particularly the feet, tends to be systematically underestimated by researchers. A specialized intelligent carpet tailored for automotive cockpits that integrates sponge‐based triboelectric sensor to monitor driver's foot movement is proposed. The intelligent carpet contains two areas, namely the human‐vehicle interaction sensing area and the driving intention monitoring area. With the human‐vehicle interaction sensing area, the driver can interact with the vehicle through the left foot, resulting in reducing the occupation of visual resources and hand load. Furthermore, the driving intention monitoring area supported by the right foot can effectively identify driver's intentions and driver's behaviors, which are essential to trajectory prediction, optimal path planning, and driving safety. It is demonstrated that an intelligent carpet is potentially useful for human‐vehicle interaction, and transportation optimization and accident prevention. [ABSTRACT FROM AUTHOR]

Despite the worldwide prevalence of multilingualism, the knowledge of the relationship between domain-general cognitive control and multilingual language control remains scant. Here we provide new insights into this issue by examining systematically how different components of inhibitory control (i.e., response inhibition and interference suppression) contribute to language control in multilingual populations with high L2 proficiency. To this end, 65 Tibetan–Chinese–English trilinguals highly proficient in L2 were recruited to complete three tasks: a picture-naming task measuring the performance of online trilingual speech production, and two nonlinguistic tasks, a go/no-go task and a Simon task, as proxies for measuring response inhibition and interference suppression abilities, respectively. Using mixed-effects modeling, we analyzed both the trilingual language switching/nonswitching performances and their correlations with these two components of inhibitory control. Our data revealed unexpected patterns of reversed language dominance effect and (a)symmetries in switch costs. Notably, interaction analysis revealed that while response inhibition was robustly engaged in trilingual language control, interference suppression did not appear to play a role. Taken together, our study suggests that, for trilingual speakers highly proficient in L2, the recruitment of different subprocesses of inhibitory control in lexical access was selective and was constrained to reactive and local-level language control. We conclude by discussing theoretical implications. [ABSTRACT FROM AUTHOR]

Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T. [ABSTRACT FROM AUTHOR]