內港南部公共領域 - 策略願景 (University of Saint Joseph, 2021)

內港南部公共領域 - 概念設計：區域 06崗頂街區 (University of Saint Joseph, 2022)

內港南部公共領域 - 概念設計：區域 05福隆街區 (University of Saint Joseph, 2022)

內港南部公共領域 - 概念設計：區域 04海濱街區 (University of Saint Joseph, 2022)

內港南部公共領域 - 概念設計：區域 03司打口街區 (University of Saint Joseph, 2022)

內港南部公共領域 - 概念設計：區域 02高樓街區 (University of Saint Joseph, 2022)

內港南部公共領域 - 概念設計：區域 01下環街區 (University of Saint Joseph, 2022)

內港南部公共領域 – 概念性總體規劃 (University of Saint Joseph, 2022)

Validating the use of the 24-item long version and the 12-item short version of the Teachers’ Sense of Efficacy Scale (TSES) for measuring teachers’ self-efficacy in Macao (SAR) for inclusive education - Monteiro, Elisa; Forlin, Chris (Emerald Open Research, 2020)
Validation of the Teachers’ Sense of Efficacy Scale (TSES) for use with teachers in Macao (SAR) was undertaken to determine its usefulness as a measure of teacher self-efficacy for inclusive education. This paper discusses the results found by analyzing various versions of the TSES and TSES-C in a Chinese format with 200 pre-service teachers in Macao (SAR).  Psychometric analyses were undertaken to investigate the validity of the existing scales and the three and two factor solutions. The results indicated a preferred 9-item version that produced improved factor loadings and reliabilities. The use of a relatively quick and short scale to measure such a complex phenomenon as teacher self-efficacy is discussed. Issues are raised regarding generalizability of scales and the impact of culture, demographics, and edifying issues that may impact on the usefulness of such scales.
Type: article
Keywords: Inclusive education; Macao; scale development; self-efficacy

The Lotus Network – Masterplan and Design Guidelines for Iao Hon and Toi San Community Public Spaces (University of Saint Joseph, 2021)

Southern Inner Harbour Public Domain – Strategic Vision (University of Saint Joseph, 2021)

Southern Inner Harbour Public Domain – Conceptual Master Plan (University of Saint Joseph, 2022)

Southern Inner Harbour Public Domain – Conceptual Design: Zone 06 – Santo Agostinho (University of Saint Joseph, 2022)

Southern Inner Harbour Public Domain – Conceptual Design: Zone 05 - Felicidade (University of Saint Joseph, 2022)

Southern Inner Harbour Public Domain – Conceptual Design Zone 04 – Harbou (University of Saint Joseph, 2022)

Southern Inner Harbour Public Domain – Conceptual Design: Zone 03 – Ponte e Horta (University of Saint Joseph, 2022)

Southern Inner Harbour Public Domain – Conceptual Design: Zone 02 - Padre António (University of Saint Joseph, 2022)

Southern Inner Harbour Public Domain – Conceptual Design: Zone 01 –Manduco (University of Saint Joseph, 2022)

Contribution for the Public Consultation on the Macao SAR Masterplan 2020-2024 (University of Saint Joseph, 2020)

A Fast Lightweight Based Deep Fusion Learning for Detecting Macula Fovea Using Ultra-Widefield Fundus Images - Wang, Han; Yang, Jie; Wu, Yaoyang; Du, Wencai; Fong, Simon; Duan, Yutong; Yao, Xiaoping; Zhou, Xiaoshu; Li, Qingqian; Lin, Chen; Liu, Jiang; Huang, Lina; et al. (MATHEMATICS & COMPUTER SCIENCE, 2021)
Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.