Search results for: Sze Wai Veera Fung

Authors: Yu Fang Chen, Jeng Fung Hung

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This study aimed to develop a circular economy scale to assess the level of recognition among high-level executives in businesses regarding the circular economy. The circular economy is crucial for global ESG sustainable development and poses a challenge for corporate social responsibility. The aim of promoting the circular economy is to reduce resource consumption, move towards sustainable development, reduce environmental impact, maintain ecological balance, increase economic value, and promote employment. This study developed a 23-item Circular Economy Scale, which includes three subscales: "Understanding of Circular Economy by Enterprises" (8 items), "Attitudes" (9 items), and "Behaviors" (6 items). The Likert 5-point scale was used to measure responses, with higher scores indicating higher levels of agreement among senior executives with regard to the circular economy. The study tested 105 senior executives and used a structural equation model (SEM) as a measurement indicator to determine the extent to which potential variables were measured. The standard factor loading of the measurement indicator needs to be higher than 0.7, and the average variance explained (AVE) represents the index of convergent validity, which should be greater than 0.5 or at least 0.45 to be acceptable. Out of the 23 items, 12 did not meet the standard, so they were removed, leaving 5 items, 3 items, and 3 items for each of the three subscales, respectively, all with a factor loading greater than 0.7. The AVE for all three subscales was greater than 0.45, indicating good construct validity. The Cronbach's α reliability values for the three subscales were 0.887, 0.787, and 0.734, respectively, and the total scale was 0.860, all of which were higher than 0.7, indicating good reliability. The Circular Economy Scale developed in this study measures three conceptual components that align with the theoretical framework of the literature review and demonstrate good reliability and validity. It can serve as a measurement tool for evaluating the degree of acceptance of the circular economy among senior executives in enterprises. In the future, this scale can be used by senior executives in enterprises as an evaluation tool to further explore its impact on sustainable development and to promote circular economy and sustainable development based on the reference provided.

Keywords: circular economy, corporate social responsibility, scale development, structural equation model

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Authors: Yi-Fung Lin, Shiow-Ching Shun, Wen-Yu Hu

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Background: A skill mix model is regarded as one of the most effective methods of reducing nursing shortages, as well as easing nursing staff workloads and labor costs. Although this model shows several benefits for the health workforce, the relationship between the optimal model of skill mix and the patient mortality rate remains to be discovered. Objectives: This review aimed to explore the relationship between the skill mix model and patient mortality rate in acute care hospitals. Data Sources: A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases and researchers retrieved studies published between January 1986 and March 2022. Review methods: Two independent reviewers screened the titles and abstracts based on selection criteria, extracted the data, and performed critical appraisals using the STROBE checklist of each included study. The studies focused on adult patients in acute care hospitals, and the skill mix model and patient mortality rate were included in the analysis. Results: Six included studies were conducted in the USA, Canada, Italy, Taiwan, and European countries (Belgium, England, Finland, Ireland, Spain, and Switzerland), including patients in medical, surgical, and intensive care units. There were both nurses and nursing assistants in their skill mix team. This main finding is that three studies (324,592 participants) show evidence of fewer mortality rates associated with hospitals with a higher percentage of registered nurse staff (range percentage of registered nurse staff 36.1%-100%), but three articles (1,122,270 participants) did not find the same result (range of percentage of registered nurse staff 46%-96%). However, based on appraisal findings, those showing a significant association all meet good quality standards, but only one-third of their counterparts. Conclusions: In light of the limited amount and quality of published research in this review, it is prudent to treat the findings with caution. Although the evidence is not insufficient certainty to draw conclusions about the relationship between nurse staffing level and patients' mortality, this review lights the direction of relevant studies in the future. The limitation of this article is the variation in skill mix models among countries and institutions, making it impossible to do a meta-analysis to compare them further.

Keywords: nurse staffing level, nursing assistants, mortality, skill mix

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Authors: Zenah Gheblawi, Susan Armijo-Olivo, Elisa B. Pelai, Vaishali Sharma, Musa Tashfeen, Angela Fung, Francisca Claveria

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Due to physiological differences between men and women, pain is experienced differently between the two sexes. Chronic pain disorders, notably temporomandibular disorders (TMDs), disproportionately affect women in diagnosis, and pain severity in opposition of their male counterparts. TMDs are a type of musculoskeletal disorder that target the masticatory muscles, temporalis muscle, and temporomandibular joints, causing considerable orofacial pain which can usually be referred to the neck and back. Therapeutic methods are scarce, and are not TMD-centered, with the latest research suggesting that subjects with chronic musculoskeletal pain disorders have abnormal alterations in the grey matter of their brains which can be remedied with exercise, and thus, decreasing the pain experienced. The aim of the study is to investigate the effects of exercise therapy in TMD female patients experiencing chronic jaw pain and to assess the consequential effects on brain activity. In a randomized controlled trial, the effectiveness of an exercise program to improve brain alterations and clinical outcomes in women with TMD pain will be tested. Women with chronic TMD pain will be randomized to either an intervention arm or a placebo control group. Women in the intervention arm will receive 8 weeks of progressive exercise of motor control training using visual feedback (MCTF) of the cervical muscles, twice per week. Women in the placebo arm will receive innocuous transcutaneous electrical nerve stimulation during 8 weeks as well. The primary outcomes will be changes in 1) pain, measured with the Visual Analogue Scale, 2) brain structure and networks, measured by fractional anisotropy (brain structure) and the blood-oxygen level dependent signal (brain networks). Outcomes will be measured at baseline, after 8 weeks of treatment, and 4 months after treatment ends and will determine effectiveness of MCTF in managing TMD, through improved clinical outcomes. Results will directly inform and guide clinicians in prescribing more effective interventions for women with TMD. This study is underway, and no results are available at this point. The results of this study will have substantial implications on the advancement in understanding the scope of plasticity the brain has in regards with pain, and how it can be used to improve the treatment and pain of women with TMD, and more generally, other musculoskeletal disorders.

Keywords: exercise therapy, musculoskeletal disorders, physical therapy, rehabilitation, tempomandibular disorders

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: K. C. M. Wong, C. P. Y. Cheng, K. Y. Chan, G. S. C. Fung, T. F. O. Lau, K. F. C. Leung, J. P. C. Fok

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Hospital Authority (HA) is the statutory body to manage all public hospitals in Hong Kong. Occupational Care Medicine Service (OMCS) is an in-house multi-disciplinary team responsible for injury management in HA. Hospital administrative services (AS) provides essential support in hospital daily operation to facilitate the provision of quality healthcare services. An occupational health enhancement program in Tai Po Hospital (TPH) domestic service supporting unit (DSSU) was piloted in 2013 with satisfactory outcome, the keys to success were staff engagement and management support. Riding on the success, the program was rolled out to another 5 AS departments of Alice Ho Miu Ling Nethersole Hospital (AHNH) and TPH in 2015. This paper highlights the indispensable components of disability management and occupational health enhancement program in hospital settings. Objectives: 1) Facilitate workplace to support staff with health affecting work problem, 2) Enhance staff’s occupational health. Methodology: Hospital Occupational Safety and Health (OSH) team and AS departments (catering, linen services, and DSSU) of AHNH and TPH worked closely with OMCS. Focus group meetings and worksite visits were conducted with frontline staff engagement. OSH hazards were identified with corresponding OSH improvement measures introduced, e.g., invention of high dusting device to minimize working at height; tailor-made linen cart to minimize back bending at work, etc. Specific MHO trainings were offered to each AS department. A disability management workshop was provided to supervisors in order to enhance their knowledge and skills in return-to-work (RTW) facilitation. Based on injured staff's health condition, OMCS would provide work recommendation, and RTW plan was formulated with engagement of staff and their supervisors. Genuine communication among stakeholders with expectation management paved the way for realistic goals setting and success in our program. Outcome: After implementation of the program, a significant drop of 26% in musculoskeletal disorders related sickness absence day was noted in 2016 as compared to the average of 2013-2015. The improvement was postulated by innovative OSH improvement measures, teamwork, staff engagement and management support. Staff and supervisors’ feedback were very encouraging that 90% respondents rated very satisfactory in program evaluation. This program exemplified good work sharing among departments to support staff in need.

Keywords: disability management, occupational health, return to work, occupational medicine

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Authors: Mohammad Emamjome Kashan, Alan S. Fung

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In Canada, more than 32% of the total energy demand is related to the building sector. Therefore, there is a great opportunity for Greenhouse Gases (GHG) reduction by integrating solar collectors to provide building heating load and domestic hot water (DHW). Despite the cold winter weather, Canada has a good number of sunny and clear days that can be considered for diurnal solar thermal energy storage. Due to the energy mismatch between building heating load and solar irradiation availability, relatively big storage tanks are usually needed to store solar thermal energy during the daytime and then use it at night. On the other hand, water tanks occupy huge space, especially in big cities, space is relatively expensive. This project investigates the possibility of using a specific building construction material (ICF – Insulated Concrete Form) as diurnal solar thermal energy storage that is integrated with a heat pump and microchannel solar thermal collector (MCST). Not much literature has studied the application of building pre-existing walls as active solar thermal energy storage as a feasible and industrialized solution for the solar thermal mismatch. By using ICF walls that are integrated into the building envelope, instead of big storage tanks, excess solar energy can be stored in the concrete of the ICF wall that consists of EPS insulation layers on both sides to store the thermal energy. In this study, two solar-based systems are designed and simulated inTransient Systems Simulation Program(TRNSYS)to compare ICF wall thermal storage benefits over the system without ICF walls. In this study, the heating load and DHW of a Canadian single-family house located in London, Ontario, are provided by solar-based systems. The proposed system integrates the MCST collector, a water-to-water HP, a preheat tank, the main tank, fan coils (to deliver the building heating load), and ICF walls. During the day, excess solar energy is stored in the ICF walls (charging cycle). Thermal energy can be restored from the ICF walls when the preheat tank temperature drops below the ICF wall (discharging process) to increase the COP of the heat pump. The evaporator of the heat pump is taking is coupled with the preheat tank. The provided warm water by the heat pump is stored in the second tank. Fan coil units are in contact with the tank to provide a building heating load. DHW is also delivered is provided from the main tank. It is investigated that the system with ICF walls with an average solar fraction of 82%- 88% can cover the whole heating demand+DHW of nine months and has a 10-15% higher average solar fraction than the system without ICF walls. Sensitivity analysis for different parameters influencing the solar fraction is discussed in detail.

Keywords: net-zero building, renewable energy, solar thermal storage, microchannel solar thermal collector

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Authors: Annis Lai Chu Fung

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While aggression had high stability throughout developmental stages and across generations, it should be the top priority of researchers and frontline helping professionals to develop prevention and intervention programme for aggressive children and children at risk of developing aggressive behaviours. Although there is a substantial amount of anti-bullying programmes, they gave disappointingly small effect sizes. The neglectful practical significance could be attributed to the overly simplistic categorisation of individuals involved as bullies or victims. In the past three decades, the distinction between reactive and proactive aggression has been well-proved. As children displaying reactively aggressive behaviours have distinct social-information processing pattern with those showing proactively aggressive behaviours, it is critical to identify the unique needs of the two subtypes accordingly when designing an intervention. The onset of reactive aggression and proactive aggression was observed at earliest in 4.4 and 6.8 years old respectively. Such findings called for a differential early intervention targeting these high-risk children. However, to the best of the author’s knowledge, the author was the first to establish an evidence-based intervention programme against reactive and proactive aggression. With the largest samples in the world, the author, in the past 10 years, explored three different approaches and their effectiveness against aggression quantitatively and qualitatively with longitudinal design. The three approaches presented are (a) cognitive-behavioral approach, (b) moral education, with Chinese marital arts and ethics as the medium, and (c) bioneurological measures (omega-3 supplementation). The studies adopted a multi-informant approach with repeated measures before and after the intervention, and follow-up assessment. Participants were recruited from primary and secondary schools in Hong Kong. In the cognitive-behavioral approach, 66 reactive aggressors and 63 proactive aggressors, aged from 11 to 17, were identified from 10,096 secondary-school children with questionnaire and subsequent structured interview. Participants underwent 10 group sessions specifically designed for each subtype of aggressor. Results revealed significant declines in aggression levels from the baseline to the follow-up assessment after 1 year. In moral education through the Chinese martial arts, 315 high-risk aggressive children, aged 6 to 12 years, were selected from 3,511 primary-school children and randomly assigned into four types of 10-session intervention group, namely martial-skills-only, martial-ethics-only, both martial-skills-and-ethics, and physical fitness (placebo). Results showed only the martial-skills-and-ethics group had a significant reduction in aggression after treatment and 6 months after treatment comparing with the placebo group. In the bioneurological approach, 218 children, aged from 8 to 17, were randomly assigned to the omega-3 supplement group and the placebo group. Results revealed that compared with the placebo group, the omega-3 supplement group had significant declines in aggression levels at the 6-month follow-up assessment. All three approaches were effective in reducing proactive and reactive aggression. Traditionally, intervention programmes against aggressive behaviour often adapted the cognitive and/or behavioural approach. However, cognitive-behavioural approach for children was recently challenged by its demanding requirement of cognitive ability. Traditional cognitive interventions may not be as beneficial to an older population as in young children. The present study offered an insightful perspective in aggression reduction measures.

Keywords: intervention, outcome effectiveness, proactive aggression, reactive aggression

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Authors: Zdravko Trivic, John Chye Fung, Ruzica Bozovic-Stamenovic

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Within the context of rapidly ageing population in Singapore and the pressing demands on both caregivers and care providers, an integrated approach to ageing-friendly and ability-sensitive enabling environment becomes an imperative. This particularly applies to nursing home environments and their immediate surroundings, as they are becoming one of the main available options of long-term care for many senior adults who are unable to age at home. Yet, despite the considerable efforts to break the still predominant clinical approach to eldercare and to introduce more home-like design and person-centric care model, nursing homes keep being stigmatised and perceived as not so desirable environments to grow old in. The challenges are further emphasised by the associated physical, sensorial, psychological and cognitive declines that are the common consequences of ageing. Such declines have an immense impact on almost all aspects of older adults’ daily functioning, including problems with mobility and spatial orientation, difficulties in communication, withdrawal from social interaction, higher level of depression and decreased sense of independence and autonomy. However, typical nursing home designs tend to neglect the full capacities of balanced and carefully integrated multisensory stimuli as active component of care and ability building. This paper outlines part of a larger multi-disciplinary study of six nursing homes in Singapore, with overarching objectives to create new models of supportive nursing home environments that go beyond the clinical care model and encourage community integration with the nursing home settings. The paper focuses on the largely neglected aspects of sensorial comfort and multi-sensorial properties of nursing homes, including both indoor and immediate outdoor spaces (boundaries). The objective was to investigate the sensory rhythms and explore their role in nursing home users’ daily routine and therapeutic capacities. Socio-sensory rhythms were captured and analysed through a combination of on-site sensory recordings of “objective” quantitative sensory data (air temperature and humidity, sound level and luminance) using multi-function environment meter, perceived experienced data, spatial mapping, first-person observations of nursing home users’ activity patterns, and interviews. This was done in addition to employment of available assessment tools, such as Wisconsin Person Directed Care assessment tool, Dementia Quality of Life [DQoL] instrument, and Resident Environment Impact Scale [REIS], as these tools address the issues of sensorial experience insufficiently and selectively. Key findings indicate varied levels of sensory comfort, as well as diversity, intensity, and customisation of multi-sensory conditions within different nursing home spaces. Sensory stimulation is typically concentrated in communal living areas of the nursing homes or in the areas that often provide controlled or limited access, including specifically designed sensory rooms and outdoor green spaces (gardens and terraces). Opportunities for sensory stimulation are particularly limited for bed-bound senior residents and within more functional areas, such as corridors. This suggests that the capacities of nursing home designs to provide more diverse and better integrated pleasant sensory conditions as integrated “therapeutic devices” to build nursing home residents’ physical and mental abilities, encourage activity and improve wellbeing are far from exhausted.

Keywords: ageing-supportive environment, enabling design, multi-sensory assessment, nursing home environment

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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