With the requirements of New English Curriculum Standards, English teaching has shifted towards organising content thematically, focusing not only on the transmission of linguistic knowledge but also on the improvement of students' comprehensive quality during language learning. This evolution undoubtedly raises higher demands on English instruction. Unit-based integrated teaching, as an innovative pedagogical model, is characterised by its holistic, interconnected, progressive, and comprehensive features. It can help students to build a correlated knowledge network facilitates the establishment of connections between disparate pieces of knowledge, deepens students' understanding and enhances their retention, improves their overall linguistic competence and learning ability, so as to foster the comprehensive development of core literacy. Therefore, this article takes the teaching of English in compulsory education as an example, and explores and elaborates on the design and implementation path of integrated teaching of English units under the new curriculum standards from four aspects: teaching objectives, teaching content, teaching process, and teaching evaluation, in order to provide reference for promoting integrated teaching of English units in compulsory education.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

Complex security systems are designed to elevate physical security. Besides people’s first-hand experience of being secured, there is a secondary sensation of anxiety while being watched which should be given a particular emphasis. In this paper, first the Security & Happiness by Design Framework is proposed which is based on research findings in psychology. After a brief literature review on scholarly works addressing the intersection between security and psychology. The concept presented by HIBLISS, the Happiness Initiated Behaviour Led Intelligence Security System, underscores the integration of user well-being, behavioral analysis, and advanced technology within security frameworks. Specifically, the case study of the Jewel Airport in Singapore is cited to enhance the concept’s applicability, detailing its advantages and its role in a holistic risk assessment methodology.