Unveiling the practices and challenges of professional learning community in a Malaysia Chinese School

Professional learning community (PLC) studies in the Asian Chinese nations remain scarce despite the emerging interest in the practice of PLC beyond the Western context. This study attempts to provide an understanding of the practices of PLC and challenges in implementing PLC in a Malaysian Chinese culture–dominated secondary school. This qualitative study used a phenomenological constructivist approach as a strategy of inquiry. Semi-structured interview data were collected from six middle leaders and ordinary teachers in a national-type Chinese secondary school in the northern region. Findings informed three existing PLC practices at the school level, namely, (a) peer coaching, (b) sharing of personal practices, and (c) professional development courses. However, the practice of PLC encounters various challenges, including excessive workload, teachers’ passive attitudes, unsupportive conditions in the school, poor execution of PLC by the school community, and a vague understanding of PLC. Interestingly, this study identified two uncovered challenges hindering the development of PLC: misconception about PLC and lack of supervision from the authority. Implications and future studies are presented

Unveiling the Practices and Challenges of Professional Learning Community in a Malaysian Chinese Secondary School

Professional learning community (PLC) studies in the Asian Chinese nations remain scarce despite the emerging interest in the practice of PLC beyond the Western context. This study attempts to provide an understanding of the practices of PLC and challenges in implementing PLC in a Malaysian Chinese culture– dominated secondary school. This qualitative study used a phenomenological constructivist approach as a strategy of inquiry. Semi-structured interview data were collected from six middle leaders and ordinary teachers in a national-type Chinese secondary school in the northern region. Findings informed three existing PLC practices at the school level, namely, (a) peer coaching, (b) sharing of personal practices, and (c) professional development courses. However, the practice of PLC encounters various challenges, including excessive workload, teachers’ passive attitudes, unsupportive conditions in the school, poor execution of PLC by the school community, and a vague understanding of PLC. Interestingly, this study identified two uncovered challenges hindering the development of PLC: misconception about PLC and lack of supervision from the authority. Implications and future studies are presented

Hugus 哈格斯

Hugus is a pair of fluffy dolls designed for two parties who need to spend extended time periods separated by distance. Each of the parties owns one of the dolls. To connect the Hugus, all the parties have to do is to link up their smartphones with the Hugus App via Bluetooth, and set up a reunion date with a pairing code... 哈格斯是一對兩隻的毛毛玩偶，專為長期分隔異地的人而設計。二人雙方各擁一隻哈格斯玩偶，以智能手機透過藍芽接入哈格斯應用程式，再以配對碼設定相聚的日期，就能連結彼此的哈格斯... Award: Merit奬項: 優異

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Exploring machine learning to aid the development of manufacturing execution software

The rapid development of Internet of Things technology allows cross-communication between modern machines via a common cloud platform. This technology has increasingly been adopted in manufacturing environments giving rise to what we know as Industry 4.0. Manufacturing environments that were successful in adopting the new technology in their production systems are often referred to as Cyber-Physical Production Systems. Cyber-Physical Production Systems are built on existing production monitoring platforms such as the Manufacturing Execution Software and go a step further to connect the data to a cloud environment. Allowing the manufacturing environment to have complete control and analytic capability of their smart machinery over the cloud. Improvements over the state of existing Manufacturing Execution Software are key to the adoption Cyber-Physical Production Systems. However, it is necessary to improve the existing software services to meet the inherent design criteria for the adoption of Cyber-Physical Production Systems. With the onset of Industrial 4.0, there is increasing demand for the development of better Manufacturing Execution Software to better support the integration Cyber-Physical Production Systems. This surge in demand prompts the need for a quicker development cycle for Manufacturing Execution Software. In the development of bespoke Manufacturing Execution Software for individual production plants, a large amount of qualitative feedback data was gathered from the manufacturing operators and the software implementers. This poses an issue for software implementors as they are required to expend significant time analysing a large volume of qualitative data to generate meaningful developmental insights. This process can be expedited if we can classify the qualitative feedback data into priorities allowing developmental efforts to be better focused on meaningful critical developments. That can be achieved using Natural Language Processing to extract information from unstructured qualitative feedback data and classify them based on their attributes using a Machine Learning model. This task had been replicated in various applications to conduct sentiment analysis where text is classified based on the emotional sentiments that it demonstrates, and also in-text classification to sort a large volume of information into various categories. This project aims to evaluate the capabilities of Machine Learning in classifying qualitative feedback data of Manufacturing Software. A binary classification method is adopted to predict the feedback data into two categories, feedback involving improvement requests and feedback not involving improvement requests. This approach constitutes the usage of classification algorithms and neural networks. Deep learning and classification are possible with TensorFlow as TensorFlow is capable of building neural networks used for Classification, Perception, and Prediction. GloVe word embedding was used for word vectorisation, word vectorisation is important to present unstructured data in vectors allowing the Machine Learning model to mathematically compute their relationship with each other. The Keras library is used to manage and operate TensorFlow as it is an API that allows a more user-friendly approach due to its nature as a high-level API.Bachelor of Engineering (Mechanical Engineering

The long way home

“Help unlock the second prison.” This slogan, a hallmark of the Yellow Ribbon Project, is a silent companion of the ex-offender, presenting itself at various stages of reintegration. It watches from the walls of transitional shelters, from the offices of volunteer welfare organisations and is a source of purpose for counsellors and social workers. It advertises at bus stops and on television screens, urging the public to keep an open mind and reminding ex-offenders of the difficult journey that lies ahead. It is a mark of the progress made and challenges that remain.Bachelor of Communication Studie