Evaluation of the McGill‐Tongji Blended Education Program for Teacher Leaders in General Practice:The importance of partnership and contextualization in International Primary Care Training Initiatives

Purpose:Strong primary health care(PHC)systems require well‐established PHC education systems to enhance the skills of general practitioners(GPs).However,the literature on the experiences of international collaboration in primary care education in low‐and middle‐income countries remains limited.The purpose of this study was to evaluate the implementation and perceived impact of the McGill‐Tongji Blended Education Program for Teacher Leaders in General Practice(referred to as the“Tongji Program”).Methods:In 2020–2021,the McGill Department of Family Medicine(Montreal,Canada)and Tongji University School of Medicine(TUSM,Shanghai,China)jointly implemented the Tongji Program in Shanghai,China to improve the teaching capacity of PHC teachers.We conducted an exploratory longitudinal case study with a mixed methods design for the evaluation.Quantitative(QUAN)data was collected through questionnaire surveys and qualitative(QUAL)data was collected through focus group discussions.Results:The evaluation showed that learners in Tongji Program were primarily female GPs(21/22,95%)with less than 4 years of experience in teaching(16/22,73%).This program was considered a successful learning experience by most participants(19/22,86%)with higher order learning tasks such as critical thinking and problem‐solving.They also agreed that this program helped them feel more prepared to teach(21/22,95%),and developed a positive attitude toward primary care(21/22,95%).The QUAL interview revealed that both the Tongji and McGill organizers noted that TUSM showed strong leadership in organization,education,and coordination.Both students and teachers agreed that by adapting training content into contextualized delivery formats and settings,the Tongji Program successfully overcame language and technology barriers.Conclusions:Committed partnerships and contextualization were key to the success of the Tongji Program.Future research should focus on how international primary care education programs affect learners'behavior in their practice settings,and explore barriers and facilitators to change.

Resilience, the 6th Vital Sign: Conceptualizing, Contextualizing, and Operationalizing All Six Vital Signs

There are five vital signs that healthcare providers assess: temperature, pulse, respiration, blood pressure, and pain. Normal levels for the five vital signs are published by the American Heart Association, and other specialty organizations, however, the sixth vital sign (resilience) which adopts the measure of immune resilience is suggested in this paper. Resilience is the ability of the immune system to respond to attacks and defend effectively against infections and inflammatory stressors, and psychological resilience is the capacity to resist, adapt, recover, thrive, and grow from a challenge or a stressor. Individuals with better optimal immune resilience had better health outcomes than those with minimal immune resilience. The purpose of this paper is to conceptualize, contextualize, and operationalize all six vital signs. We suggest measuring resilience subjectively and objectively. Subjectively, use a 5-item guided interview revised from the Connor-Davidson Resilience Scale (CDRC), a scale of 10 items. The revised CDRC scale is a 5-item scale. The scale is rated on a 5-point Likert scale from 0 (not true) to 4 (true all the time). The total score ranges from 0 to 20, with higher total scores indicating greater resilience. The scale demonstrated good construct validity and internal consistency (α = 0.85) during the development of the scale. The CD-RISC had a good Cronbach’s alpha level of 0.85. The Revised CD-RISC can be completed in 2 - 4 minutes. To measure resilience objectively, we suggest using Immune Resilience (IR) levels, the level of resilience to preserve and/or rapidly restore immune resilience functions that promote disease resistance and control inflammation and other inflammatory stress. IR levels are gauged with two peripheral blood metrics that quantify the balance between CD8 and CD4 T-cell levels and gene expression signatures tracking longevity-associated immunocompetence and mortality- or entropy-associated inflammation. IR deregulation is potentially reversible by decreasing inflammatory stress. IR metrics and mechanisms have utility as vital signs and biomarkers for measuring immune health and improving health outcomes.

Research on the Classification of Western Literary Criticism Under M. H. Abrams’Fourfold Model

This study explores the application of Abrams’Fourfold Model in the classification of Western literary criticism.Abrams’framework categorizes literary criticism into four fundamental elements:text,author,world,and audience.The text is viewed as an independent entity with intrinsic artistic value,necessitating a detailed analysis of its structure,style,themes,and symbols.Author study delves into the creator’s life and socio-cultural context,often to uncover the work’s deeper meanings.Contextual study situates the work within its historical and social milieu,examining its reflection of or response to societal norms and events.Audience response analysis considers the diverse interpretations shaped by readers’backgrounds,emphasizing the reader’s role in constructing the work’s meaning.The study concludes that Abrams’Fourfold Model offers a comprehensive and flexible analytical tool,enabling critics to engage with literary works from multiple perspectives,thereby enriching the understanding of literary complexity and diversity.

The Impact of Contextual Teaching Method on Sichuan Folk Song Education and Students’Musical Expressiveness

This study explores the application of the contextual teaching method in Sichuan folk song education and its impact on students’musical expressiveness.By incorporating contextual teaching methods in music classes,this research investigates the effectiveness of this approach in enhancing students’understanding of Sichuan folk songs and improving their musical expressiveness and emotional expression.A mixed-method research approach is employed,utilizing classroom observations,questionnaires,interviews,and statistical analysis to assess the practical outcomes of contextual teaching in folk song education.

Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

基于CREAM和贝叶斯网络的空管人误概率预测方法

针对研究管制人因可靠性时存在的模糊性和片面性问题,采用认知可靠性与失误分析方法(CREAM)中的扩展预测法,计算10项管制通用任务的人误概率;在此基础上,以管制行为形成因子作为根节点构建贝叶斯网络,建立其与情景控制模式的不确定关系模型,对管制员在多任务中的人误概率进行预测。研究结果表明:在由相同评判者给出行为形成因子影响效应的前提下,由CREAM扩展预测法和构建贝叶斯网络的方法预测得到的多数任务的人误概率差异较大,从方法的客观性、合理性和适用性角度分析,贝叶斯网络在研究该问题时更具优势。

MapReduce编程模型下的上下文离群数据挖掘算法

目前,对于离群数据挖掘算法研究颇多,但对于离群数据挖掘结果可理解性和可解释性的研究相对较少。采用相关子空间,给出一种MapReduce编程模型下的上下文离群数据挖掘算法。该算法利用局部稀疏差异度,确定相关子空间,并计算其数据对象离群因子值;将离群因子和相关属性维,定义为数据对象的上下文信息,提高了数据对象的可理解性;选取离群因子最大的N个数据对象,作为上下文离群数据;利用MapReduce编程模型,实现了一种上下文离群数据并行挖掘算法;最后,采用UCI数据集,实验验证了该算法的可解释性和有效性。

语用顺应论:一个诗歌评析的新视角——从英诗《When You Are Old》的文学顺应性谈起

诗歌生成过程所发生的语言选择与交流使诗歌批评对语用顺应论得以敞开,即诗歌生成是诗人与隐含读者进行交际,在不断形成的动态认知语境中关照隐含读者的认知水平与审美诉求,进行语言及文学策略选择的过程,而诗人语言与文学策略的选择使诗歌携带了文学顺应性。若从文本逆向审视诗歌生成,通过分析诗歌的语境顺应、语言结构顺应以及以文学策略为载体的顺应意识度,便可实现对诗歌文学顺应性的讨流。

Context and Teaching of EFL Reading

Both linguistics and language teaching take language as their subject. Linguistics can provide language teaching with theories and language teaching can further prove the validity of linguistics. To some extent, linguistics is more theoretical while language teaching is more practical. Since linguistics is defined as the scientific study of language, it seems obvious that such a study would have direct relations to language teaching. This paper attempts to make an analysis of difference between Contextual theory of meaning and the teaching of EFL reading.

A Study on Translation of Chinese Culture-loaded Words from the Per-spective of Relevance Theory

The essay tends to analyze the translation of Chinese Culture-loaded words from the perspective of Relevance Theory.The theory gains its prominence by studying the translation process and transcending the conflicts between literal and free translation. It incorporates recent work in cognitive linguistics, with ostensive-inference as its key model. Under the influence of Relevance theory, the translation of culture-loaded words is reader-oriented. Translators are obliged to help target readers to establish new assumptions to achieve equivalent response.

A Research on the Improvement of Pragmatic Competence in College English Teaching

A core issue in foreign language teaching is to foster actual competence of language learners to practically utilize language, which is called pragmatic competence. But compared with traditional linguistic competence, the levels of their pragmatic competence need to be further understood. Thus it is essential to make an investigation into this. From the investigation results it is expected to reveal from which aspects teachers and students should carry on in order to improve pragmatic competence and meet foreign language learning need in new age.

The progress of operational forest fire monitoring with infrared remote sensing

Forest wildfires pose significant and growing threats to human safety, wildlife habitat, regional economies and global climate change. It is crucial that forest fires be subject to timely and accurate monitoring by forest fire managers and other stake-holders. Measurement by spaceborne equipment has become a practical and appealing method to monitor the occurrence and development of forest wildfires. Here we present an overview of the principles and case studies of forest fire monitoring（FFM） with satelliteand drone-mounted infrared remote sensing（IRRS）. This review includes four types of FFM-relevant IRRS algorithms： bi-spectral methods, fixed threshold methods, spatial contextual methods, and multi-temporal methods. The spatial contextual methods are presented in detail since they can be applied easily with commonly available satellite IRRS data, including MODIS, VIIRS, and Landsat 8 OLI. This review also evaluates typical cases of FFM using NOAAAVHRR, EOS-MODIS, S-NPP VIIRS, Landsat 8 OLI,MSG-SEVIRI, and drone infrared data. To better implement IRRS applications in FFM, it is important to develop accurate forest masks, carry out systematic comparative studies of various forest fire detection systems（known as forest fire products）, and improve methods for assessing the accuracy of forest fire detection. Medium-resolution IRRS data are effective for landscape-scale FFM, and the VIIRS 375 m contextual algorithm and RST-FIRES algorithm are helpful for closely tracking forest fires（including small and shortlived fires） and forest-fire early warning.

GLYX-13 pretreatment ameliorates long-term isoflurane exposure-induced cognitive impairment in mice

Accumulating evidence indicates that inhalation anesthetics induce or increase the risk of cognitive impairment. GLYX-13（rapastinel） acts on the glycine site of N-methyl-D-aspartate receptors（NMDARs） and has been shown to enhance hippocampus-dependent learning and memory function. However, the mechanisms by which GLYX-13 affects learning and memory function are still unclear. In this study, we investigated these mechanisms in a mouse model of long-term anesthesia exposure. Mice were intravenously administered 1 mg/kg GLYX-13 at 2 hours before isoflurane exposure（1.5% for 6 hours）. Cognitive function was assessed using the contextual fear conditioning test and the novel object recognition test. The mRNA expression and phosphorylated protein levels of NMDAR pathway components, N-methyl-D-aspartate receptor subunit 2B（NR2B）-Ca2+/calmodulin dependent protein kinase II（CaMKII）-cyclic adenosine monophosphate response element binding protein（CREB）, in the hippocampus were evaluated by quantitative RT-PCR and western blot assay. Pretreatment with GLYX-13 ameliorated isoflurane exposure-induced cognitive impairment and restored NR2B, CaMKII and CREB mRNA and phosphorylated protein levels. Intracerebroventricular injection of KN93, a selective CaMKII inhibitor, significantly diminished the effect of GLYX-13 on cognitive function and NR2B, CaMKII and CREB levels in the hippocampus. Taken together, our findings suggest that GLYX-13 pretreatment alleviates isoflurane-induced cognitive dysfunction by protecting against perturbation of the NR2B/CaMKII/CREB signaling pathway in the hippocampus. Therefore, GLYX-13 may have therapeutic potential for the treatment of anesthesia-induced cognitive dysfunction. This study was approved by the Experimental Animal Ethics Committee of Drum Tower Hospital affiliated to the Medical College of Nanjing University, China（approval No. 20171102） on November 20, 2017.

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing.Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much manpower.Due to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision.Thus,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer.Finally,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training cost.Meanwhile,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

LGNet:Local and global representation learning for fast biomedical image segmentation

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

Orthographic,Semantic,and Contextual Influences on Initial Processing and Learning of Novel Words During Reading:Evidence From Eye Movements

This study investigates how orthographic,semantic and contextual variables—including word length,concreteness,and contextual support—impact on the processing and learning of new words in a second language(L2)when first encountered during reading.Students learning English as a foreign language(EFL)were recruited to read sentences for comprehension,embedded with unfamiliar L2 words that occurred once.Immediately after this,they received a form recognition test,a meaning recall test,and a meaning recognition test.Eye-movement data showed significant effects of word length on both early and late processing of novel words,along with effects of concreteness only on late-processing eye-tracking measures.Informative contexts were read slower than neutral contexts,yet contextual support did not show any direct influence on the processing of novel words.Interestingly,initial learning of abstract words was better than concrete words in terms of form and meaning recognition.Attentional processing of novel L2 words,operationalized by total reading time,positively predicted L2 learners’recognition of new orthographic forms.Taken together,these results suggest:1)orthographic,semantic and contextual factors play distinct roles for initial processing and learning of novel words;2)online processing of novel words contributes to L2 learners’initial knowledge of unfamiliar lexical items acquired from reading.

A Relevance-Theoretical Analysis of the Persuasive Powerin English Print Advertisements——A Case Study of a Long Advertisement from Reader's Digest

Based on the Relevance Theory established by Sperber & Wilson, this paper sets out to look into the persuasive power in a chosen advertisement and at the same time tries to render plausible explanations of the contextual effects that are inferred by the audience in the communicative process.

The Value of Effective Strategies Employed in Vocabulary Instruction in College English Reading

Vocabulary teaching is one aspect of language teaching that has not been given the attention it deserves until recent years. For a long period of time, vocabulary is simply taught in the way by asking students to study and memorize its meaning and spelling, its part of speech and its general function in a sentence. Thus, a student with a command of five thousand English vocabulary still finds it hard to adapt himself to the requirement of our demanding reading assignments, in particular, to the extensive reading task, which is more demanding due to its wide range of materials and large amount of vocabularies. According to Wilkins (1979: 111) "Without grammar very little can be conveyed, without vocabulary, nothing can be conveyed." Yet without a deeper understanding of how vocabulary is taught in the classroom and which methods of teaching are more effective for learners, the teaching of vocabulary may not achieve the desired effects. By researching the topic on vocabulary learning and instruction, this essay intends to bring the attention of both teachers and learners to the weaknesses of the traditional approach of teaching vocabulary and some different strategies in vocabulary instruction with the aim of improving the students’ reading comprehension.\;

Visual Relationship Detection with Contextual Information

Understanding an image goes beyond recognizing and locating the objects in it,the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predictions of the relationships.But real-world image relationships often determined by the surrounding objects and other contextual information.In this work,we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.The core of the framework is a relationship inference network,which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the image.Experimental results on Stanford VRD and Visual Genome demonstrate that the proposed method achieves a good performance both in efficiency and accuracy.Finally,we demonstrate the value of visual relationship on two computer vision tasks:image retrieval and scene graph generation.

Retrieval of canopy biophysical variables from remote sensing data using contextual information

In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.