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Corrosion evolutions of UNS C90300 bronzes with and without artificial patina were investigated in the simulated acid rain of Hong Kong. The corrosion products mainly composed of cuprite were formed on the surface exhibiting slight protection for the bronze substrate. The ratios of Sn and Zn in the corrosion products are lower than in the alloy. The artificial patina effectively enhances the corrosion resistance of bronze substrate, even after 30 days of immersion. For both bare and patinated bronzes the Sn‐ and Zn‐based species are absent in the outer layers of corrosion products, and Cu2O species in the outer layer can partially transform into Cu (II) ionic state due to the abundant supply of dissolved oxygen. [ABSTRACT FROM AUTHOR]

Overcrowding at emergency departments in Hong Kong has been a critical issue for hospital managers recently. In this study, we focus on optimizing the medical staff configuration to alleviate overcrowding. According to the service requirements proposed by the Hong Kong government, 90% of urgent patients should receive treatment within 30 min. However, this condition is rarely satisfied in the practical situation. Therefore, we formulate the problem as minimizing the proportion of urgent patients that violate the service requirements while satisfying the service requirements of the other categories and cost constraints, thereby resulting in an optimization problem with a stochastic objective and several stochastic constraints. To solve this problem efficiently, we proposed a multi-fidelity simulation optimization framework containing a low- and a high-fidelity process. We utilize an evolutionary algorithm with violation-constrained handling assisted by a surrogate model as a low-fidelity process to shrink the solution space and generate an elite population. In the high-fidelity process, we exploit the optimal computing budget allocation method to identify the best solution in the elite population based on a data-driven simulation model. A case study is also discussed, and the results demonstrated that with a limited labor cost, there is a 52.05% reduction on average in the waiting time of urgent patients. Meanwhile, our proposed multi-fidelity simulation optimization framework proves to save 98.4% of the simulation time. [ABSTRACT FROM AUTHOR]

Understanding of its correlation to some relevant factors is of paramount importance for modeling and predication of the air pollution process. Compared with the traditional cross-correlation analysis, multifractal detrended cross-correlation analysis (MFDCCA) was argued to be a more suitable method to analyze air pollutant time series due to their non-stationarity nature. Multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) was proposed to improve the shortcomings of MFDCCA. In this study, we apply MF-TWXDFA to investigate the cross-correlation between pollutants ( PM 1 0 and NO X ) and meteorological factors (temperature, pressure, wind speed (WS) and relative humidity (RH)). The results on the dataset from 1 January 2005 to 31 December 2014 in urban and rural areas of Hong Kong show the existence of multifractal cross-correlation between all pairs of pollutants and meteorological factors in both urban and rural areas. Different from the previous MFDCCA results, we found that the multifractal degree of cross-correlation between PM 1 0 and (temperature, pressure) is more obvious in urban area. The multifractal strength of cross-correlation between NO X and WS is very weak in either urban or rural area. Furthermore, the MF-TWXDFA cross-correlation coefficient ρ MF-TWXDFA can capture negative correlation between pollutants and meteorological factors. For PM 1 0 , ρ MF-TWXDFA in urban area is less than or close to that in rural area with respect to these four meteorological factors. The ρ MF-TWXDFA of NO X in urban and rural areas shows more complex patterns for varied meteorological factors. Compared with MFDCCA, MF-TWXDFA can provide much richer information about the relationships between pollutants and meteorological factors, which is beneficial to further modeling and prediction of the air pollution process. [ABSTRACT FROM AUTHOR]

• An integrated model to jointly determine line configuration, line frequency and passenger assignment on a urban rail transit (URT) network is proposed by taking interplay between operator-oriented cost and passenger-oriented cost into account. It specifically considers (i) transfer waiting time, (ii) the use of the same tracks for trains belonging to different lines, and (iii) capacity restriction of trains. • The line planning problem in an URT network with passenger path assignment is formulated as a mixed-integer non-linear programming (MINLP) model with linear constraints. • An outer approximation method is developed to convert the original MINLP to mixed-integer linear programming (MILP). The experiment shows that approximation is actually very tight with a gap under 5.6 × 10–6. • A real case study based on the Hong Kong MTR network is conducted to test the performance of our approach. We also propose a method to determine the appropriate coefficients for the model and test the impacts of different candidate line pool size and the number of paths considered on the model. This paper considers a line planning problem in an urban rail transit (URT) network with passenger path assignment. The line planning problem involves both line configuration and frequency setting for the optimization of a combination of operator-oriented objective (minimization of operating costs or maximization of profits) and passenger-oriented objective (minimization of total travel times). The interplay between the line planning and the passenger assignment is explicitly taken into account and the problem is formulated as a mixed-integer non-linear programming (MINLP) model with linear constraints. In order to take advantage of existing commercial mix-integer linear programming (MILP) solvers, we propose an outer approximation method to linearize the objective. Finally, the model and solution algorithm are illustrated with a simplified version of the Hong Kong Mass Transit Railway (MTR) network. [ABSTRACT FROM AUTHOR]

We investigate the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong. Using fluctuation functions as a measure for their variability, we develop several simple data models and test their predictive power. We discuss two relevant dynamical properties, namely, the scaling of fluctuations, which is associated with long memory, and the deviations from the Gaussian distribution. While the scaling of fluctuations can be shown to be an artifact of a relatively regular seasonal cycle, the process does not follow a normal distribution even when corrected for correlations and non-stationarity due to random (Poissonian) spikes. We compare predictability and other fitted model parameters between stations and pollutants. [ABSTRACT FROM AUTHOR]

As the urban rail transit (URT) system plays an increasingly important role in supporting large cities' mobility around the world, service disruptions have become more prevalent, potentially resulting in severe economic losses and passenger safety issues. It is imperative to investigate effective response strategies to mitigate the effects of such disruptions. In response to URT service disruptions, this paper systematically investigates the bus bridging service design (BBSD) problem, which concerns the integration of bus bridging route design, frequency determination, and passenger assignment in the integrated URT and bus network. The problem is formulated as a path-based integer linear programming (ILP) model with the goal of simultaneously minimizing operator-oriented and passenger-oriented costs. A column generation-based approach is proposed to solve this model efficiently, allowing nonintuitive bus routes to be freely generated on the network dynamically. Our method has been tested with two different case studies based on real data from the Hong Kong Mass Transit Railway (MTR). Experiments demonstrate that our proposed approach can assist public transit (PT) operators in developing efficient emergency response plans for various potential disruption situations in advance. Even in the face of unexpected disruptions that necessitate a quick response, our approach can generate high-quality solutions in a matter of minutes. • A novel integrated model optimizes the bus bridging route design, frequency and passenger assignment simultaneously. • The trade-off between operator-oriented cost and passenger-oriented cost is considered. • The proposed column generation-based algorithm allows nonintuitive bus routes to be generated freely. • Extensive numerical experiments are carried out to seek valuable managerial insights for the public transit operators. [ABSTRACT FROM AUTHOR]

With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence increasing, it is necessary to identify patients with advanced fibrosis (F3-F4 stages). We evaluated the performance of new biomarkers and algorithms for diagnosing advanced fibrosis in an Asian population. Data from two Asian cohorts (including 851 biopsy-proven MAFLD [578 from Wenzhou, 273 from Hong Kong]) were studied. The association between N-terminal propeptide of type 3 collagen (PRO-C3) and the histologic stage of liver fibrosis was analyzed by multivariable linear regression. The area under the receiver operating characteristic curve (AUROC) was used to test the diagnostic performance of serum PRO-C3 and the ADAPT score for advanced fibrosis and compared them to other established non-invasive tests. Serum PRO-C3 levels increased progressively across liver fibrosis stages and correlated with advanced fibrosis (P < 0.001). The ADAPT score had an AUROC of 0.865 (95% confidence interval 0.829–0.901) for advanced fibrosis; the accuracy, sensitivity and negative predictive values were 81.4%, 82.2% and 96.1%, respectively. This result was better compared to that of PRO-C3 alone or other non-invasive fibrosis biomarkers (aspartate aminotransferase-to-platelet ratio index, Fibrosis-4, BARD, and NAFLD fibrosis score). In subgroup analyses (including sex, age, diabetes, NAFLD activity score, body mass index or serum alanine aminotransferase levels), the ADAPT score had good diagnostic performance. PRO-C3 and the ADAPT score reliably exclude advanced fibrosis in MAFLD patients and reduce the need for liver biopsy. • The N-terminal propeptide of type 3 collagen can be used for the staging fibrosis. • The ADAPT score has better diagnostic performance for identifying advanced fibrosis. • The ADAPT score can be used as new noninvasive tests for diagnosing advanced fibrosis. [ABSTRACT FROM AUTHOR]

Air quality forecasting for Hong Kong is a challenge. Even taking the advantages of auto-regressive integrated moving average and some state-of-the-art numerical models, a recently-developed hybrid method for one-day (two- and three-day) ahead forecasting performs similarly to (slightly better than) a simple persistence forecasting. Long-term forecasting also remains an important issue, especially for policy decision for better control of air pollution and for evaluation of the long-term impacts on public health. Given the well-recognized negative effects of PM 2.5 , NO 2 and O 3 on public health, we study their time series under the multi-scale framework with empirical mode decomposition and nonstationary oscillation resampling to explore the possibility of long-term forecasting and to improve short-term forecasts in Hong Kong. Applied to a dataset from January 2016 to December 2018, the long-term forecasting (with lead time about 100 days) of the multi-scale framework has the root-mean-square error (RMSE) comparable with that of the short-term (with lead time of one or two days) forecasting by the persistence method, while its improvement for short-term forecasting (with lead time of one, two or three days) is quite substantial over the persistence forecasting, with RMSEs reduced by respectively 44%–47%, 30%–45%, and 40%–60% for PM 2.5 , NO 2 , and O 3. Compared to the hybrid method, it turns out that, for short-term forecasting for the same data, the multi-scale framework can reduce RMSE by about 25% (respectively 30%) for PM 2.5 (respectively NO 2 and O 3). In addition, we find no significant difference in the forecasting performance of the multi-scale framework among different types of stations. The multi-scale framework is feasible for time series forecasting and applicable to other pollutants in other cities. Image 1 • A multi-scale framework is applied to forecast PM 2.5 , NO 2 and O 3. • It uses empirical mode decomposition to extract relatively regular components. • It uses nonstationary oscillation resampling to forecast each component. • It satisfactorily forecasts long-term (about 100 days) variation of air pollutants. • It improves forecasting of air pollutants on the next 1–3 days. Use a multi-scale framework with empirical mode decomposition and nonstationary oscillation resampling to satisfactorily predict long-term variation and to improve short-term forecasts of air quality. [ABSTRACT FROM AUTHOR]

This study focuses on modeling air quality with an adaptive utilization of spatio-temporal information from multiple air quality monitoring stations under a multi-scale framework. To this end, it is necessary to consider different strategies to combine different methods to decompose the given series and to fuse multi-site information. Based on a systematic comparative analysis, we propose a novel multi-scale and multi-site modeling method named the multivariate empirical mode decomposition and spatial cosine-attention-based long short-term memory (MEMD-SCA-LSTM). The MEMD-SCA-LSTM first employs MEMD to decompose the multi-site air quality series into the scale-aligned components and then models the components at different scales. The high-frequency components are modeled by a novel SCA-LSTM, which employs LSTM with residual blocks to extract the temporal information and utilizes an attention mechanism based on the cosine similarity to adaptively extract interactions among different sites. Other relatively regular components are modeled by the LSTM. Empirical study on PM 2.5 in Hong Kong has demonstrated the effectiveness of fusing multi-site information using the spatial attention (SA) mechanism under the multi-scale framework with MEMD. The proposed MEMD-SCA-LSTM can improve the one-day ahead modeling performance with the mean absolute error and the root mean square error reduced over 10%, compared to the baseline modeling methods. For the two-day and three-day ahead performance, the MEMD-SCA-LSTM is still the best one. Furthermore, by visualizing the attention weights, we illustrate that our proposed SCA-LSTM can overcome some limitations of the traditional attention mechanisms and that the attention weights exhibit more informative patterns which could be used to analysis the transport of air pollutant between sites. The proposed modeling method is a general method, which is feasible and applicable to other pollutants in other cities or regions. [Display omitted] • A multi-site and multi-scale modeling method for air quality is proposed. • This modeling method is based on MEMD and attention mechanism. • MEMD is more suitable to reveal the coupled spatial information among sites. • LSTM with spatial cosine attention can better model the high frequency components. • Attention weights can mine more informative patterns of interactions among sites. [ABSTRACT FROM AUTHOR]