Construction of Blended Teaching Model Integrating Intercultural Competence Training

Under the international background of economic globalization and cultural diversity, it is an important task for current higher education to cultivate international talents with intercultural competence. In the field of foreign language teaching, more and more scholars pay attention to the integration of English education and intercultural education in English curriculum system, but there are relatively insufficient researches that can provide practical and operable guidance for front-line teachers to implement intercultural education. Nowadays blended teaching is recognized as an effective teaching model. This study intends to construct a blended teaching model which can integrate intercultural competence training into English courses, so as to help front-line teachers know how to carry out intercultural teaching design and implementation with the ultimate goal of improving college students’ foreign language ability and intercultural competence together

Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning

A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real time. On the passenger side, traditional approaches focus on pricing strategies by increasing the probability of users' call to adjust the distribution of demand. However, previous methods do not take into account the impact of changes in strategy on future supply and demand changes, which means drivers are repositioned to different destinations due to passengers' calls, which will affect the driver's income for a period of time in the future. Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy. In this study, we propose an offline deep reinforcement learning based method focusing on the demand side to improve the utilization of transportation resources and customer satisfaction. We adopt a spatio-temporal learning method to learn the value of different time and location, then incentivize the ride requests of passengers to adjust the distribution of demand to balance the supply and demand in the system. In particular, we model the problem as a Markov Decision Process (MDP)

The Chart Excites Me! Exploring How Data Visualization Design Influences Affective Arousal

As data visualizations have been increasingly applied in mass communication, designers often seek to grasp viewers immediately and motivate them to read more. Such goals, as suggested by previous research, are closely associated with the activation of emotion, namely affective arousal. Given this motivation, this work takes initial steps toward understanding the arousal-related factors in data visualization design. We collected a corpus of 265 data visualizations and conducted a crowdsourcing study with 184 participants during which the participants were asked to rate the affective arousal elicited by data visualization design (all texts were blurred to exclude the influence of semantics) and provide their reasons. Based on the collected data, first, we identified a set of arousal-related design features by analyzing user comments qualitatively. Then, we mapped these features to computable variables and constructed regression models to infer which features are significant contributors to affective arousal quantitatively. Through this exploratory study, we finally identified four design features (e.g., colorfulness, the number of different visual channels) cross-validated as important features correlated with affective arousal

Global exponential convergence of delayed inertial Cohen–Grossberg neural networks

In this paper, the exponential convergence of delayed inertial Cohen–Grossberg neural networks (CGNNs) is studied. Two methods are adopted to discuss the inertial CGNNs, one is expressed as two first-order differential equations by selecting a variable substitution, and the other does not change the order of the system based on the nonreduced-order method. By establishing appropriate Lyapunov function and using inequality techniques, sufficient conditions are obtained to ensure that the discussed model converges exponentially to a ball with the prespecified convergence rate. Finally, two simulation examples are proposed to illustrate the validity of the theorem results

Quantum-Inspired Machine Learning: a Survey

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.Comment: 56 pages, 13 figures, 8 table

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios

Structural defects in 2D MoS2 nanosheets and their roles in the adsorption of airborne elemental mercury

In this research, ab initio calculations and experimental approach were adopted to reveal the mechanism of Hg0 adsorption on MoS2 nanosheets that contain various types of defects. The ab initio calculation showed that, among different structural defects, S vacancies (Vs) in the MoS2 nanosheets exhibited outstanding potential to strongly adsorb Hg0. The MoS2 material was then prepared in a controlled manner under conditions, such as temperature, concentration of precursors, etc., that were determined by adopting the new method developed in this study. Characterisation confirmed that the MoS2 material is of graphene-like layered structure with abundant structural defects. The integrated dynamic and steady state (IDSS) testing demonstrated that the Vs-rich nanosheets showed excellent Hg0 adsorption capability. In addition, ab initial calculation on charge density difference, PDOS, and adsorption pathways revealed that the adsorption of Hg0 on the Vs-rich MoS2 surface is non-activated chemisorption

Targeting oncogenic miR-335 inhibits growth and invasion of malignant astrocytoma cells

<p>Abstract</p> <p>Background</p> <p>Astrocytomas are the most common and aggressive brain tumors characterized by their highly invasive growth. Gain of chromosome 7 with a hot spot at 7q32 appears to be the most prominent aberration in astrocytoma. Previously reports have shown that microRNA-335 (miR-335) resided on chromosome 7q32 is deregulated in many cancers; however, the biological function of miR-335 in astrocytoma has yet to be elucidated.</p> <p>Results</p> <p>We report that miR-335 acts as a tumor promoter in conferring tumorigenic features such as growth and invasion on malignant astrocytoma. The miR-335 level is highly elevated in C6 astrocytoma cells and human malignant astrocytomas. Ectopic expression of miR-335 in C6 cells dramatically enhances cell viability, colony-forming ability and invasiveness. Conversely, delivery of antagonist specific for miR-335 (antagomir-335) to C6 cells results in growth arrest, cell apoptosis, invasion repression and marked regression of astrocytoma xenografts. Further investigation reveals that miR-335 targets disheveled-associated activator of morphogenesis 1(Daam1) at posttranscriptional level. Moreover, silencing of endogenous Daam1 (siDaam1) could mimic the oncogenic effects of miR-335 and reverse the growth arrest, proapoptotic and invasion repression effects induced by antagomir-335. Notably, the oncogenic effects of miR-335 and siDAAM1 together with anti-tumor effects of antagomir-335 are also confirmed in human astrocytoma U87-MG cells.</p> <p>Conclusion</p> <p>These findings suggest an oncogenic role of miR-335 and shed new lights on the therapy of malignant astrocytomas by targeting miR-335.</p