Application of Blended Learning in French Teaching Reform

This paper explores the application of blended learning in the reform of French teaching, aiming to address the challenges and issues existing in French language education. Firstly, it provides an overview of the definition, characteristics, and prevalence of blended learning in the field of education. Then, it analyzes the limitations of traditional French teaching models and emphasizes the necessity of reform. Through the introduction of application cases of blended learning in French teaching, it discusses the combination of online teaching tools and offline classroom practices, as well as the effects of blended learning on various language skills. Furthermore, it discusses teaching design and methods, evaluation, and reflection under the blended learning model. Finally, it summarizes the application effects of blended learning in French teaching and prospects for future development directions and challenges

AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization

Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations. Previous literature on model finetuning has mainly focused on regularization terms and specific policy models, while the misalignment of channels between source and target models has not received sufficient attention. In this work, we revisited the dynamics of batch normalization (BN) layers and observed that the trainable affine parameters of BN serve as sensitive indicators of domain information. Therefore, Affine Collaborative Normalization (AC-Norm) is proposed for finetuning, which dynamically recalibrates the channels in the target model according to the cross-domain channel-wise correlations without adding extra parameters. Based on a single-step backpropagation, AC-Norm can also be utilized to measure the transferability of pretrained models. We evaluated AC-Norm against the vanilla finetuning and state-of-the-art fine-tuning methods on transferring diverse pretrained models to the diabetic retinopathy grade classification, retinal vessel segmentation, CT lung nodule segmentation/classification, CT liver-tumor segmentation and MRI cardiac segmentation tasks. Extensive experiments demonstrate that AC-Norm unanimously outperforms the vanilla finetuning by up to 4% improvement, even under significant domain shifts where the state-of-the-art methods bring no gains. We also prove the capability of AC-Norm in fast transferability estimation. Our code is available at https://github.com/EndoluminalSurgicalVision-IMR/ACNorm

Digital image processing technology applied in level measurement and control system

AbstractAs the diversity of industrial processes, the common level meter devices are more impacted by external factors. This paper presents a new type of digital image processing technology for the level control system, combining with CCD camera technology as one of the measurement method. The fixed beam for measuring needs generated by the laser measurements, shape a special light point on the object surface, We can measure according to the changing scope of these points, or moving distance. From the experiment we can see, the CCD-based level measurement method not only has strong anti-interference ability, good usability, easy adaptability, but also applies to variety of more complex industrial applications

Electrical Property of Polypropylene Films Subjected to Different Temperatures and DC Electric Fields

A polypropylene (PP) film is usually used as a dielectric material in capacitors as well as cables. However, PP films may degrade because of the combined effect of temperature and electric field. In an earlier study, plain PP films and PP films loaded with nano-metric natural clay were studied under sinusoidal (AC) electric fields at power frequency and temperatures above the ambient. To better understand the electrical characteristics of PP film under various conditions, the objective of this study is to determine the time-to-breakdown of the plain PP and PP filled with 2% (wt) natural nano-clay when subjected to time-invariant (DC) electric fields at elevated temperatures. In order to achieve this objective, the effects of uniform as well as non-uniform electric fields were compared at the same temperature for the PP film. In this study, experimental results indicated that the time-to-breakdown of all PP films, plain or filled with nano-clay, decreases with the increase in electric field intensity, non-uniformity of the electric field, and temperature. It was also found that the time-to-breakdown of PP film filled with 2% (wt) natural nano-clay under DC electric field is longer and less sensitive to temperature. Furthermore, when compared with the results under the uniform electric field, PP film filled with 2% (wt) nano-metric natural clay indicates shorter time-to-failure under non-uniform DC electric fields. Finally, the morphology of the samples was observed by digital camera, optical micrography, and SEM, to better understand the mechanism of the breakdown

Rational Number Representation by the Approximate Number System

The approximate number system (ANS) enables organisms to represent the approximate number of items in an observed collection, quickly and independently of natural language. Recently, it has been proposed that the ANS goes beyond representing natural numbers by extracting and representing rational numbers (Clarke & Beck 2021a). Prior work has demonstrated that adults and children discriminate ratios in an approximate and ratio-dependent manner, consistent with the hallmarks of the ANS. Here, we use a well-known “connectedness illusion” to provide evidence that these ratio-dependent ratio discriminations are (a) based on the perceived number of items in seen displays (and not just non-numerical confounds), (b) are not dependent on verbal working memory, or explicit counting routines, and (c) may involve representations with a part-whole (or subset-superset) format, like a fraction, rather than a part-part format, like a ratio. These results support and refine the hypothesis that the ANS represents full-blown rational numbers

Effects of dimephosphone on skin survival in conditions of reduced blood circulation

The search for and creation of drugs with dermatoprotective and metabotropic activity is one of the priorities of modern diabetology. Synthetic organophosphorus compounds with no anticholinesterase activity, to which dimephosphone belongs to, deserve great attention in this respec

Effect of Nano-clay Filler on the Thermal Breakdown Mechanism and Lifespan of Polypropylene Film under AC Fields

The wide application of nanocomposites in the insulation system has greatly contributed to the performance improvement of power equipment. However, nano fillers are not omnipotent for improving the properties of composite dielectrics. In some situations, nano-modified materials are in fact a compromise of improving some performance features while sacrificing others. In this work, the breakdown characteristics and time-to-failure of polypropylene film with nano-clay fillers have been evaluated under combined thermal stress and AC electric fields. Experiments on plain polypropylene (PP) samples have also been carried out under the same test conditions as control. Test results indicated that the time-to-failure of the samples with nano-clay filler was shorter than those without nano filler, which is different from the previous experience. SEM and EDS analyses were conducted to study how the failure mechanism had taken place in both plain polypropylene and the nano-clay filled polypropylene. The failure phenomenon in these materials can be explained by molecular thermodynamics. The main reason for the premature thermal breakdown of PP nanocomposite is essentially due to the weak coupling between nano-clay filler and polymer matrix. Finally, suggestions are proposed for nano modification methods and lifespan prediction models of composite dielectrics

RESTORE: Towards Feature Shift for Vision-Language Prompt Learning

Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently optimized along a single modality path, may sacrifice the vision-language alignment of pre-trained models in return for improved performance on specific tasks and classes, leading to poorer generalization. In this paper, we first demonstrate that prompt tuning along only one single branch of CLIP (e.g., language or vision) is the reason why the misalignment occurs. Without proper regularization across the learnable parameters in different modalities, prompt learning violates the original pre-training constraints inherent in the two-tower architecture. To address such misalignment, we first propose feature shift, which is defined as the variation of embeddings after introducing the learned prompts, to serve as an explanatory tool. We dive into its relation with generalizability and thereafter propose RESTORE, a multi-modal prompt learning method that exerts explicit constraints on cross-modal consistency. To be more specific, to prevent feature misalignment, a feature shift consistency is introduced to synchronize inter-modal feature shifts by measuring and regularizing the magnitude of discrepancy during prompt tuning. In addition, we propose a "surgery" block to avoid short-cut hacking, where cross-modal misalignment can still be severe if the feature shift of each modality varies drastically at the same rate. It is implemented as feed-forward adapters upon both modalities to alleviate the misalignment problem. Extensive experiments on 15 datasets demonstrate that our method outperforms the state-of-the-art prompt tuning methods without compromising feature alignment.Comment: 18 pages, 5 figure