314 research outputs found
Chinese literary works translated into Baba Malay: a bibliographical study
No full textAnalyses 68 unique titles of Baba translated works published between 1889 and 1950. The titles are held in the libraries of the University of Malaya (UM), Science University Malaysia (USM), National University of Malaysia (UKM), the Dewan Bahasa dan Pustaka (DBP), National University of Singapore (NUS), National Library of Singapore (NLS) and the British Library (BL). The results reveal three periods of active publication of Baba translated works. A total of 18 works were translated before World War I, followed by 10 just after the war, 39 titles were published before the break of the World War II and 1 was identified in 1950. There were 103 persons involved in the 68 translated works, some of whom are responsible for more than one title. The most prominent translators were Chan Kim Boon, Wan Boon Seng, Seow Chin San and Lee Seng Poh. Some of the translators were also be editors, illustrators or editors. There were 31 publishers and 21 printing presses involved, all were located in Singapore. The most active publishers were Wan Boon Seng, Kim Seck Chy Press and Nanyang Romanised Malay Book Co. The translated works mainly cover historical classical Chinese stories, chivalrous stories, romances, folklore and legends. The titles were priced between 10 cents to 2 dollars in Straits currency. The University of Malaya Library held the largest number of unique title (62) out of which 15 were unique titles
The 1961 Kampong Bukit Ho Swee fire and the making of modern Singapore
No full textBy 1970, Singapore’s urban landscape was dominated by high-rise blocks of planned public housing built by the People’s Action Party government, signifying the establishment of a high modernist nation-state. A decade earlier, the margins of the City had been dominated by kampongs, home to semi-autonomous communities of low-income Chinese families which freely built, and rebuilt, unauthorised wooden houses. This change was not merely one of housing but belied a more fundamental realignment of state-society relations in the 1960s. Relocated in Housing and Development Board flats, urban kampong families were progressively integrated into the social fabric of the emergent nation-state. This study examines the pivotal role of an event, the great Kampong Bukit Ho Swee fire of 1961, in bringing about this transformation. The redevelopment of the fire site in the aftermath of the calamity brought to completion the British colonial regime’s ‘emergency’ programmes of resettling urban kampong dwellers in planned accommodation, in particular, of building emergency public housing on the sites of major fires in the 1950s. The PAP’s far greater political resolve, and the timing of and state of emergency occasioned by the scale of the 1961 disaster, enabled the government to rehouse the Bukit Ho Swee fire victims in emergency housing in record time. This in turn provided the HDB with a strategic platform for clearing other kampongs and for transforming their residents into model citizens of the nation-state. The 1961 fire’s symbolic usefulness extended into the 1980s and beyond, in sanctioning the PAP’s new housing redevelopment schemes. The official account of the inferno has also become politically useful for the government of today for disciplining a new generation of Singaporeans against taking the nation’s progress for granted. Against these exalted claims of the fire’s role in the Singapore Story, this study also examines the degree of actual change and continuity in the social and economic lives of the people of Bukit Ho Swee after the inferno. In some crucial ways, the residents continued to occupy a marginal place in society while pondering, too, over the unresolved question of the cause of the fire. These continuities of everyday life reflect the ambivalence with which the citizenry regarded the high modernist state in contemporary Singapore
Design and development of an alchemy-related puzzle video game (A)
No full textWith video games’ increasing popularity among most age groups, it is expected to have a strong growth and demand in the upcoming years. This project aims to develop an Alchemy-themed 2D platformer puzzle game playable on Windows PC that provides a more intriguing experience for players. Instead of limiting the players’ creativity due to the limiting number of solutions to solve a puzzle game, the project explores the possibility of expanding the range of potential actions players can take to solve a puzzle using a wider variety of tools.
With the collaboration among 4 students – Khoo Kai Yi Chloe, Leo Boon Yin, Wayne, Loke Seng, Theodore and Jeannie Chan Ting Ting, the game was developed with the intention to participate in Game Development World Championship (GDWC) and Independent Games Festival (IGF) in 2022. The team of students handled the full development of the game creation, including the Game flow designs, mechanics, graphics, and sound designs.
This report focuses on the author’s contributions which includes the development of the game designs, consisting of art assets, animations and cutscenes, drawn using Clip studio Paint, which were then integrated onto Unity. In addition, the author also worked on various game mechanics systems being implemented using C# programming language which will also be discussed upon in the report.Bachelor of Engineering (Information Engineering and Media
Singaporean mothers' perception of their three-year-old child's weight status: A cross-sectional study
No full textSingapore National Research Foundation; National Medical Research Council (NMRC), SingaporeFull Author List: Cheng T.S.; Cheng T.; Loy S.; Cheung Y.; Chan J.; Tint M.; Godfrey K.; Gluckman P.; Kwek K.; Saw S.; Chong Y.; Lee Y.; Yap F.; Lek N.; Sheppard A.; Chinnadurai A.; Goh A.; Rifkin-Graboi A.; Qiu A.; Biswas A.; Lee B.; Broekman B.; Quah B.; Shuter B.; Chng C.; Ngo C.; Hsu S.; Bong C.; Henry C.; Chee C.; Fok D.; Yeo G.; Inskip H.; Chen H.; Van Bever H.; Magiati I.; Wong I.; Lau I.; Kapur J.; Richmond J.; Holbrook J.; Gooley J.; Tan K.; Niduvaje K.; Singh L.; Su L.; Daniel L.; Shek L.; Fortier M.; Hanson M.; Chong M.; Rauff M.; Chua M.; Meaney M.; Teoh O.; Wong P.; Agarwal P.; Van Dam R.; Rebello S.; Chong S.; Cai S.; Soh S.; Lim S.; Rajadurai V.; Stunkel W.; Han W.; Pang W.; Goh Y.; Chan Y.</p
Metabolic health status and fecundability in a Singapore preconception cohort study
No full textBackground: obesity compromises metabolic health and female fertility, yet not all obese women are similar in metabolic status. The extent to which fecundability is influenced by the metabolic health status of women who are overweight or obese before conception is unknown.Objective: this study aimed to: (1) determine the metabolic health status, and (2) examine the association between metabolic health status and fecundability of overweight and obese women trying to conceive in the Singapore PREconception Study of long-Term maternal and child Outcomes cohort study.Study Design: we conducted a prospective preconception cohort study of Asian women (Chinese, Malay, and Indian) aged 18 to 45 years trying to conceive who were treated from 2015 to 2017 in KK Women’s and Children’s Hospital in Singapore (n=834). We defined women to have metabolically unhealthy status if they: (1) met 3 or more modified Joint Interim Statement metabolic syndrome criteria; or (2) had homeostasis model assessment-insulin resistance index ≥2.5. Body mass index was categorized as normal (18.5–22.9 kg/m2), overweight (23–27.4 kg/m2), or obese (≥27.5 kg/m2) on the basis of cutoff points for Asian populations. Fecundability was measured by time to pregnancy in menstrual cycles within a year of enrolment. Discrete-time proportional hazards models were used to estimate fecundability odds ratios, with adjustment for confounders and accounting for left truncation and right censoring.Results: of 232 overweight women, 28 (12.1%) and 25 (10.8%) were metabolically unhealthy by metabolic syndrome ≥3 criteria and homeostasis model assessment-insulin resistance ≥2.5, respectively. Of 175 obese women, 54 (30.9%) and 93 (53.1%) were metabolically unhealthy by metabolic syndrome ≥3 criteria and homeostasis model assessment-insulin resistance ≥2.5, respectively. Compared with metabolically healthy normal-weight women, lower fecundability was observed in metabolically unhealthy overweight women on the basis of metabolic syndrome criteria (fecundability odds ratios, 0.38 [95% confidence interval, 0.15–0.92]) and homeostasis model assessment-insulin resistance (fecundability odds ratios, 0.68 [95% confidence interval, 0.33–1.39]), with metabolic syndrome criteria showing a stronger association. Metabolically unhealthy obese women showed lower fecundability than the healthy normal-weight reference group by both metabolic syndrome (fecundability odds ratios, 0.35; 95% confidence interval, 0.17–0.72) and homeostasis model assessment-insulin resistance criteria (fecundability odds ratios, 0.43; 95% confidence interval, 0.26–0.71). Reduced fecundability was not observed in overweight or obese women who showed healthy metabolic profiles by either definition.Conclusion: overweight or obesity was not synonymous with having metabolic syndrome or insulin resistance. In our preconception cohort, metabolically unhealthy overweight and obese women showed reduced fecundability, unlike their counterparts who were metabolically healthy. These findings suggest that metabolic health status, rather than simply being overweight and obese per se, plays an important role in fecundability
Multi-organ plant classification based on convolutional and recurrent neural networks
No full textClassification of plants based on a multi-organ approach is very challenging. Although additional data provide more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Despite promising solutions built using deep learning enable representative features to be learned for plant images, the existing approaches focus mainly on generic features for species classification, disregarding the features representing plant organs. In fact, plants are complex living organisms sustained by a number of organ systems. In our approach, we introduce a hybrid generic-organ convolutional neural network (HGO-CNN), which takes into account both organ and generic information, combining them using a new feature fusion scheme for species classification. Next, instead of using a CNN-based method to operate on one image with a single organ, we extend our approach. We propose a new framework for plant structural learning using the recurrent neural network-based method. This novel approach supports classification based on a varying number of plant views, capturing one or more organs of a plant, by optimizing the contextual dependencies between them. We also present the qualitative results of our proposed models based on feature visualization techniques and show that the outcomes of visualizations depict our hypothesis and expectation. Finally, we show that by leveraging and combining the aforementioned techniques, our best network outperforms the state of the art on the PlantClef2015 benchmark. The source code and models are available at https://github.com/cs-chan/Deep-Plant
TESTS ON NONLINEARITY, LONG MEMORY AND CHAOS IN PACIFIC BASIN STOCK MARKETS
No full textBachelor'sBACHELOR OF SOCIAL SCIENCES (HONOURS
Getting to know low-light images with the Exclusively Dark dataset
No full textLow-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https://github.com/cs-chan/Exclusively-Dark-Image-Dataset
STUDY OF ELECTROCHEMICAL POLYMERIZATION OF THIOPENE AND THIOPHENE-3-ACETIC ACID AND THEIR APPLICATIONS AS CHEMICAL PROBES FOR ANALYSIS
No full textMaster'sMASTER OF SCIENC
The art and science of achieving zero COVID-19 transmissions in staff at a large community care facility in Singapore using implementation science: a retrospective analysis
No full textRepository for Additional Files and Reporting Checklist for paper entitled "The art and science of achieving zero COVID-19 transmissions in staff at a large community care facility in Singapore using implementation science: a retrospective analysis"
Once published, the DOI to the paper will be deposited here as well.
Authors:
Weien Chow, MRCP*(1), Elaine Lum, PhD*(2), Arif Tyebally, MRCPCH (UK)(3), Sze Ling Chan, PhD(2,4), Lai Chee Lee, MPH(5), Moi Lin Ling, FRCPA(5), Hiang Khoon Tan, FRCS†(5), Nigel CK Tan, FRCP (Edin)†(6).
*Joint-First authors
†Joint-Senior authors
Affiliations:
1. Changi General Hospital, SingHealth, 2 Simei Street 3, Singapore 529889
2. Health Services & Systems Research, Duke-NUS Medical School, 8 College Road, Singapore 169857
3. KK Women’s and Children’s Hospital, SingHealth, 100 Bukit Timah Road, Singapore 229899
4. Health Services Research Centre, SingHealth, Academia Building, 20 College Road, Singapore 169856
5. Singapore General Hospital, SingHealth, Outram Road, Singapore 169608
6. National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore 30843
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