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With the continuous development of the economy, the trade volume of customs clearance by land transportation between Guangdong and Hong Kong is increasing, but there are many problems in customs clearance by land transportation between Guangdong and Hong Kong. Therefore, it is necessary to start with the logistics mode of land transportation between Guangdong and Hong Kong, reduce the cost of customs clearance by land transportation between Guangdong and Hong Kong, and increase the trade volume by land transportation between the two places. This paper systematically analyzes the present situation of customs clearance efficiency in China and introduces BPNN (BP neural network) algorithm to optimize customs clearance. The detailed idea and process of the algorithm are introduced, and the BPNN trained by the algorithm is applied to function approximation. The simulation results show that this scheme can not only improve the convergence speed of the algorithm in the training process but also the trained BPNN has strong adaptive and self-learning ability. Comparing the customs clearance efficiency before and after the improvement, the results show that the improved customs clearance efficiency is higher and can save a lot of time, thus verifying the effectiveness and applicability of the model-solving algorithm. [ABSTRACT FROM AUTHOR]

Street-level landmarks are an important basis for street-level IP geolocation, and the web-based landmark is one of the main sources of street-level landmarks. Considering the existing street-level landmark evaluation methods having low accuracy and strict constraints, this paper analyses the causes and evaluation idea of invalid web-based candidate landmarks and proposes Evaluator, a web-based landmark evaluation approach. Evaluator adopts the idea of the decision tree to filter invalid landmarks layer by layer and comprehensively estimates the quantitative reliability of candidate landmarks with public data and services to obtain reliable landmarks. This paper proposes the domain name system (DNS) distributed query algorithm to effectively resolve all IP addresses of a domain name, which provides data support for Evaluator to filter candidate landmarks. Meanwhile, this paper also proposes a reverse verification algorithm to obtain all domain names of an IP address, which provides an important reference to calculate the reliability of a reliable landmark. In addition, gradient descent is used to assess the parameters of the reliability estimating model, which effectively improves the robustness of Evaluator. Experiments show that reliable landmarks from Evaluator reduce the geolocation error of 100 targets in Hong Kong from 7.30 km to 3.91 km, compared with the landmark verifying method (LVM), one of the latest web-based landmark evaluation methods. Moreover, Evaluator significantly improves the evaluation coverage based on the same geolocation accuracy with street-level landmark evaluation (SLE), one of the latest landmark evaluation methods. [ABSTRACT FROM AUTHOR]

This paper proposes an elastic demand network equilibrium model for networks with transit and walking modes. In Hong Kong, the multi-mode transit system services over 90% of the total journeys and the demand on it is continuously increasing. Transit and walking modes are related to each other as transit passengers have to walk to and from transit stops. In this paper, the multi-mode elastic-demand network equilibrium problem is formulated as a variational inequality problem where the combined mode and route choices are modeled in a hierarchical logit structures and the total travel demand for each origin-destination pair is explicitly given by an elastic demand function. In addition, the capacity constraint for transit vehicles and the effects of bi-directional flows on walkways are considered in the proposed model. All these congestion effects are taken into account for modeling the travel choices. A solution algorithm is developed to solve the multi-mode elastic-demand network equilibrium model. It is based on a Block Gauss-Seidel decomposition approach coupled with the method of successive averages. A numerical example is used to illustrate the application of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

In this paper,we propose a class of general partially linear varying-coefficient transformation models for ranking data. In the models, the functional coefficients are viewed as nuisance parameters and approximated by B-spline smoothing approximation technique. The B-spline coefficients and regression parameters are estimated by rank-based maximum marginal likelihood method. The three-stage Monte Carlo Markov Chain stochastic approximation algorithm based on ranking data is used to compute estimates and the corresponding variances for all the B-spline coefficients and regression parameters. Through three simulation studies and a Hong Kong horse racing data application, the proposed procedure is illustrated to be accurate, stable and practical. [ABSTRACT FROM PUBLISHER]

Background: The misestimation of surgical risk is a serious threat to the lives of patients when implementing surgical risk calculator. Improving the accuracy of postoperative risk prediction has received much attention and many methods have been proposed to cope with this problem in the past decades. However, those linear approaches are inable to capture the non-linear interactions between risk factors, which have been proved to play an important role in the complex physiology of the human body, and thus may attenuate the performance of surgical risk calculators.Methods: In this paper, we presented a new surgical risk calculator based on a non-linear ensemble algorithm named Gradient Boosting Decision Tree (GBDT) model, and explored the corresponding pipeline to support it. In order to improve the practicability of our approach, we designed three different modes to deal with different data situations. Meanwhile, considering that one of the obstacles to clinical acceptance of surgical risk calculators was that the model was too complex to be used in practice, we reduced the number of input risk factors according to the importance of them in GBDT. In addition, we also built some baseline models and similar models to compare with our approach.Results: The data we used was three-year clinical data from Surgical Outcome Monitoring and Improvement Program (SOMIP) launched by the Hospital Authority of Hong Kong. In all experiments our approach shows excellent performance, among which the best result of area under curve (AUC), Hosmer-Lemeshow test ([Formula: see text]) and brier score (BS) can reach 0.902, 7.398 and 0.047 respectively. After feature reduction, the best result of AUC, [Formula: see text] and BS of our approach can still be maintained at 0.894, 7.638 and 0.060, respectively. In addition, we also performed multiple groups of comparative experiments. The results show that our approach has a stable advantage in each evaluation indicator.Conclusions: The experimental results demonstrate that NL-SRC can not only improve the accuracy of predicting the surgical risk of patients, but also effectively capture important risk factors and their interactions. Meanwhile, it also has excellent performance on the mixed data from multiple surgical fields. [ABSTRACT FROM AUTHOR]

• Data-driven Bayesian network classifier as a real-time predictor of the coming ships for PSC inspection. • Dynamic programming for optimal discretization the input data in the TAN classifier. • Prove random selection of root attribute variable in the TAN classifier will not influence the classification process. • Identify the sub-standard ships with more deficiencies more accurately compared with the currently used Ship Risk Profile ship selection scheme. Maritime transportation plays a pivotal role in the economy and globalization, while it poses threats and risks to the maritime environment. In order to maintain maritime safety, one of the most important mitigation solutions is the Port State Control (PSC) inspection. In this paper, a data-driven Bayesian network classifier named Tree Augmented Naive Bayes (TAN) classifier is developed to identify high-risk foreign vessels coming to the PSC inspection authorities. By using data on 250 PSC inspection records from Hong Kong port in 2017, we construct the structure and quantitative parts of the TAN classifier. Then the proposed classifier is validated by another 50 PSC inspection records from the same port. The results show that, compared with the Ship Risk Profile selection scheme that is currently implemented in practice, the TAN classifier can discover 130% more deficiencies on average. The proposed classifier can help the PSC authorities to better identify substandard ships as well as to allocate inspection resources. [ABSTRACT FROM AUTHOR]

The importance of monitoring sea level in coastal zones becomes more and more obvious in the era of global climate change, because, in coastal zones, although satellite altimetry is an ideal tool in measuring sea level over open ocean, but its accuracy often decreases significantly at coast due to land contamination. Although the accuracy of waveform processing algorithms for synthetic aperture altimeters has been improved in the last decade, the computational speed is still not fast enough to meet the requirements of real-time processing, and the accuracy cannot meet the needs of nearshore areas within 1 km from the coast. To improve the efficiency and accuracy in the coastal zone, this study proposed an innovative waveform retracking scheme for the coastal zone based on a multiple optimization parabolic cylinder algorithm (MOPCA) integrated with machine learning algorithms such as recurrent neural network and Bayesian estimation. The algorithm was validated using 153-pass repeat cycle data from Sentinel-6 over Qianliyan Island and Hong Kong–Wanshan Archipelago. The computational speed of the proposed algorithm was four to five times faster than the current operational synthetic aperture radar (SAR) retracking algorithm, and its accuracy within 0–20 km from the island was comparable to the most popular SAMOSA+ algorithm, better than the official data product provided by Sentinel-6. Especially, the proposed algorithm demonstrates remarkable stability in the sense of proceeding speed. It maintains consistent performance, even when dealing with intricate wave patterns within a proximity of 1 km from the coast. The results showed that the proposed scheme greatly improved the quality of coastal altimetry waveform retracking. [ABSTRACT FROM AUTHOR]

Abstract: Building energy computer simulation software is a useful tool for achieving sophisticated design and evaluation of the thermal performance of buildings. For successful thermal and energy simulation of buildings, it requires hourly weather data such as dry bulb air temperature, relative humidity, solar radiation, wind speed, etc. Nowadays, an urban city faces a problem of an urban heat island which causes the urban area to have a higher air temperature than the rural region. Since the currently available weather dataset used in building simulation software mainly comes from weather stations located in remote and rural areas, the impact of the urban heat island on thermal and energy performance of buildings may not be effectively reflected. This paper reports an approach to construct a modified typical meteorological weather file, taking into account the urban heat island effect in the summer season. Field measurements have been carried out in the summer months and the corresponding urban heat island intensities were then determined. With a morphing algorithm, an existing typical meteorological year weather file was modified. An office building and a typical residential flat were modeled with a renowned building energy simulation program EnergyPlus. Computer simulations were conducted using the existing and modified typical meteorological year weather files. It was found that there was around a 10% increase in air-conditioning demand caused by the urban heat island effect in both cases. The implications of this and further work will also be discussed in this paper. [Copyright &y& Elsevier]

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In this paper, reasonable paths in transit networks are defined as possible paths that satisfy the acceptable time criterion and transfer-walk criterion. A recursive algorithm for finding all of the reasonable paths in a transit network that does not involve a rapid increase in program run-time with network size is presented. Realistic transit networks in Hong Kong and Guangzhou were selected as case studies of the different phases of the development of a trip planning system. Transport planning practitioners and potential users were invited to test the system to evaluate its performance. The results of the prototype evaluation were satisfactory, and the viability of the system as a useful tool for supporting decision-making has been confirmed by the positive feedback that was obtained from survey questionnaires. [ABSTRACT FROM AUTHOR]

In this paper we present a scale model for fitting an ideal shape to an object. The measurements of the object are taken corresponding to a fixed coordinate system at a set of well-defined locations on the surface of the object. We propose an algorithm to estimate the model parameters and hypothesis tests to make statistical inferences about several possible special cases of the general model. The model is tested with an example that analyzes data on the feet of people in Hong Kong. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air conditioning (HVAC) systems are the second largest energy consumers on cruise ships after the propulsion system. As a kind of HVAC system, active chilled beam (ACB) systems have been widely used for cabin service due to their performance of energy efficiency and good thermal comfort. However, conventional control strategies for ships' ACB systems are not intelligent enough and cannot balance energy consumption and cabin comfort during the voyages of ships. This study developed an optimal control strategy for cabins' ACB systems. First, a simulation environment considering dynamic conditions is established in TRNSYS. Second, an artificial neural network model is utilized to predict the energy consumption of the ACB system, while the predicted percentage dissatisfied is adopted to represent cabins' thermal discomfort. Third, an improved multi-objective salp swarm algorithm is proposed to dynamically minimize both energy consumption and thermal discomfort. A TRNSYS–MATLAB co-simulation testbed is established to simulate the cabins served by an ACB system on a small cruise ship navigating from Hong Kong to Shanghai for validation tests and a comparison study. Compared to the conventional strategies, the proposed strategy can achieve a maximum energy savings of 12% while maintaining a predicted mean vote index less than 0.5, meeting the comfort requirements set by ASHRAE. [ABSTRACT FROM AUTHOR]

Whether triage targets can be achieved has been an imperative assessment of service qualities for an emergency department in healthcare management. In this research, we focus on triage targets and try to fully meet the target of fast emergency response for critical patients subject to triage requirements for other category patients by optimising the medical staff allocation in the emergency department. Main challenges stem from multiple stochastic constraints and the time-consuming simulation. To solve the stochastically constrained discrete optimisation via simulation problem, we develop a discrete-event simulation model and propose a simulated-annealing-based algorithm called ConSA that adopts a special searching mechanism and an efficient simulation budget allocation rule to find a high-quality configuration of medical staff. A case study based on the data from a public hospital in Hong Kong is carried out. Numerical experiments demonstrate that our algorithm leads to a 38.28% improvement in the main performance compared to the current staff allocation and dominates other algorithms in terms of computational efficiency and output accuracy. It indicates that our method is a good decision tool for hospital managers. [ABSTRACT FROM AUTHOR]

• Algorithms affect practices of reading and writing as well as broader patterns of language use, communication and consumption. • This paper describes a participatory project, in which university students explored their 'folk beliefs' about algorithms. • The participants articulated six metaphors through which they and their classmates understand how algorithms work • Engaging learners in 'folk beliefs' about algorithms can contribute to critical online reading practices. Most accounts of the way digital technologies have changed practices of reading and writing have focused on surface aspects of digital texts (such as hypertextuality, multimodality and the development of new registers). There are, however, less visible aspects of digital communication environments that have had an equally profound effect on reading and writing – namely the algorithms that lie behind texts that monitor the actions of readers and writers and alter the form and content of the texts they are exposed to. Algorithms have the potential to affect not just local communication practices, but also broader social practices, as they work to encourage and reinforce patterns of language use, communication and consumption. This paper describes the results of a two-year long participatory project, in which university students in Hong Kong and the United Kingdom explored the communication and inference forming practices they engage in when interacting with algorithms. The participants articulated six primary metaphors through which they and their classmates understand how algorithms work: (1) Algorithm as agent; (2) Algorithm as authority; (3) Algorithm as adversary; (4) Algorithm as communicative resource; (5) Algorithm as audience; and (6) Algorithm as oracle. Engaging learners in articulating the 'folk beliefs' that govern people's interaction with algorithms, it is argued, can contribute to the development of the kinds of digital literacies they will need to better understand the ways algorithms affect the kinds of information they are exposed to, the kinds of inferences they form about this information, and the ways their own acts of reading and writing can be used by algorithms to manipulate them. [ABSTRACT FROM AUTHOR]

The worldwide spread of COVID-19 has caused significant damage to people's health and economics. Many works have leveraged machine learning models to facilitate the control and treatment of COVID-19. However, most of them focus on clinical medicine and few on understanding the spatial dynamics of the high-risk population for transmission of COVID-19 in real-world settings. This study aims to investigate the association between population features and COVID-19 transmission risk in Hong Kong, which can help guide the allocation of medical resources and the implementation of preventative measures to control the spread of the pandemic. First, we built machine learning models to predict the number of COVID-19 cases based on the population features of different tertiary planning units (TPUs). Then, we analyzed the distribution of cases and the prediction results to find specific characteristics of TPUs leading to large-scale outbreaks of COVID-19. We further evaluated the importance and influence of various population features on the prediction results using SHAP values to identify indicators for high-risk populations for COVID-19 transmission. The evaluation of COVID-19 cases and the TPU dataset in Hong Kong shows the effectiveness of the proposed methods. The top three most important indicators are identified as people in accommodation and food services, low income, and high population density. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, firstly a modified particle swarm optimization algorithm (MPSO) is developed, in which the mean value of past optimal positions for each particle and the mutation operation are considered for avoiding premature. In the optimization test, MPSO performs better than particle swarm optimization algorithm (PSO). Then MPSO is applied to solve four portfolio optimization models with the real data from the Hong Kong Stock Market, and optimal values are obtained when the number of swarm , respectively. Finally, actual return rates of these models are calculated in numerical experiments, and it is illustrated from these graphs of actual return rates that when considering higher return, Cai’s model performs better in short-term investment. [Copyright &y& Elsevier]

Abstract: The objective of this study is to investigate the air ventilation impacts of the so called “wall effect” caused by the alignment of high-rise buildings in complex building clusters. The research method employs the numerical algorithm of computational fluid dynamics (CFD – FLUENT) to simulate the steady-state wind field in a typical Hong Kong urban setting and investigate pollutant dispersion inside the street canyon utilizing a pollutant transport model. The model settings of validation study were accomplished by comparing the simulation wind field around a single building block to wind tunnel data. The results revealed that our model simulation is fairly close to the wind tunnel measurements. In this paper, a typical dense building distribution in Hong Kong with 2 incident wind directions (0° and 22.5°) is studied. Two performance indicators are used to quantify the air ventilation impacts, namely the velocity ratio (VR) and the retention time (Tr ) of pollutants at the street level. The results indicated that the velocity ratio at 2m above ground was reduced 40% and retention time of pollutants increased 80% inside the street canyon when high-rise buildings with 4 times height of the street canyon were aligned as a “wall” upstream. While this reduction of air ventilation was anticipated, the magnitude is significant and this result clearly has important implications for building and urban planning. [Copyright &y& Elsevier]

Real estate is one of the most important forms of portfolio, which plays an essential role in the economy of Hong Kong. Extensive investigations indicate that jump points in the private domestic price indices are subject to various factors such as international events, regional incidents, and government policies. Detecting jump points and identifying their relationships with these factors are indispensable since such findings can facilitate policymakers and investors to look forward. In this paper, we propose a novel two-stage variational methodology using the total variation minimization algorithm and derivative-based detector with the thresholding function for detecting jump points in the Hong Kong private domestic price indices. Our algorithm can weaken statistical fluctuation of the price indices via the total variation formulation and accurately detect jump points based on its rate of change. Comparative experimental results based on the monthly private domestic price indices from January 2010 to July 2020 reveal that the proposed method can detect 40 jump points corresponding to the aforementioned factors whereas the current state-of-the-art empirical mode decomposition approach can only detect 29 jump points, which indicates the superior performance of the proposed method. • A total variation signal denoising scheme is proposed. • A derivative-based jump point detector is proposed. • A two-stage variational jump point detection algorithm is proposed. [ABSTRACT FROM AUTHOR]

• M ultiple sources of uncertainties for battery electric vehicle routing. • Max imization of energy consumption reliability and travel time reliability. • An exact solution algorithm for determining all non-dominated paths efficiently. • Computational experiments on a real case in Hong Kong for the entire road network. This paper proposes a bi-objective reliable path-finding algorithm for routing battery electric vehicles on a road network, with vehicles' energy consumption uncertainty and travel time uncertainty. A bi-objective stochastic optimization problem is proposed and formulated to simultaneously maximize energy consumption reliability (ECR) and travel time reliability (TTR). ECR is defined as the probability of finishing a trip without exhausting a given battery energy budget, while TTR is the on-time arrival probability with the travel time budget. In this study, the proposed optimization problem is decomposed into two sub-problems: (1) finding K most reliable paths for maximizing the TTR objective and (2) finding the most reliable path for optimizing the ECR objective. Then, a novel ranking algorithm is proposed to exactly solve the formulated optimization problem. A case study is carried out on Hong Kong's road network to demonstrate the efficacy and efficiency of the proposed algorithm for real-world applications. [ABSTRACT FROM AUTHOR]

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called "gentle ramps") which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The "gentle ramp removal" (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world. [ABSTRACT FROM AUTHOR]

Presents information on a study which applied a neural based crowd estimation system at the platforms of the mass transit railway stations in Hong Kong, China using the hybrid global learning algorithm (HGL). Feature extraction for crowd estimation; Discussion on neural model and HGL algorithm; Results; Conclusion.

• We proposed ultrasound HK imaging with NCA for monitoring microwave ablation (MWA). • NCA HK imaging was comparable to NCA Nakagami and CED imaging for monitoring MWA. • NCA HK imaging may be used as a new method for monitoring MWA. In this paper, we proposed ultrasound homodyned K (HK) imaging based on the noise-assisted correlation algorithm (NCA) for monitoring microwave ablation of porcine liver ex vivo. The NCA-based HK (α NCA and k NCA) imaging was compared with NCA-based Nakagami (m NCA) imaging and NCA-based cumulative echo decorrelation (CED NCA) imaging. Backscattered ultrasound radiofrequency signals of porcine liver ex vivo during and after the heating of microwave ablation were collected (n = 15), which were processed for constructing B-mode imaging, NCA-based HK imaging, NCA-based Nakagami imaging, and NCA-based CED imaging. To quantitatively evaluate the final coagulation zone, the polynomial approximation (PAX) technique was applied. The accuracy of detecting coagulation area with α NCA , k NCA , m NCA , and CED NCA parametric imaging was evaluated by comparing the PAX imaging with the gross pathology. The receiver operating characteristic (ROC) curve was used to further evaluate the performance of the three quantitative ultrasound imaging methods for detecting the coagulation zone. Experimental results showed that the average accuracies of α NCA , k NCA , m NCA , and CED NCA parametric imaging combined with PAX imaging were 89.6%, 83.25%, 89.23%, and 91.6%, respectively. The average areas under the ROC curve (AUROCs) of α NCA , k NCA , m NCA , and CED NCA parametric imaging were 0.83, 0.77, 0.83, and 0.86, respectively. The proposed NCA-based HK imaging may be used as a new method for monitoring microwave ablation. [ABSTRACT FROM AUTHOR]

Small water bodies have always been an important part of water ecology systems. In the past, due to the limitations of satellite spatial resolution and recognition method precision, there have been few satisfactory remote sensing small water bodies extraction methods. In this article, a method based on index composition and HSI (hue, saturation, and intensity) colour space transformation is proposed to precisely extract small water bodies. An easy-to-deploy, fast, universal, and effective algorithm is used to accurately identify paddy fields and exclude shadows. This method is tested and verified with Sentinel-2 MSI (MultiSpectral Imager) images in seven cities in the Guangdong-Hong Kong-Macao Greater Bay Area. Compared with the traditional modified normalized difference water index (MNDWI) and enhanced water index (EWI) water extraction methods, the proposed HSI method has shown a better performance in small water bodies mapping with a kappa coefficient of 0.94, overall accuracy of 97%, producer's accuracy of 96%, and user's accuracy of 98% in test regions, which is significantly higher than the benchmarking water extraction methods. It provides a powerful supplement for the remote sensing monitoring of water resources in surface water bodies. The method proposed in this study exhibits extendibility, it also has the potential to extract other small features with minor modifications of the method. [ABSTRACT FROM AUTHOR]

Regular medical records are useful for medical practitioners to analyze and monitor patient's health status especially for those with chronic disease. However, such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based models have been developed for missing data imputation in recent papers. This approach makes use of the low-rank tensor assumption for highly correlated data in a short-time interval. Nevertheless, when the time intervals are long, data correlation may not be high between consecutive time stamps so that such assumption is not valid. To address this problem, we propose to decompose matrices with missing data over time into their latent factors. Then, the locally linear constraint is imposed on the latent factors for temporal matrix completion. By using three publicly available medical datasets and two medical datasets collected from Prince of Wales Hospital in Hong Kong, experimental results show that the proposed algorithm achieves the best performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: To evaluate whether the common risk factors and risk scores (FRAX, QFracture, and Garvan) can predict hip fracture in the oldest old (defined as people aged 80 and older) and to develop an oldest-old-specific 10-year hip fracture prediction risk algorithm.Methods: Subjects aged 80 years and older without history of hip fracture were studied. For the derivation cohort (N = 251, mean age = 83), participants were enrolled with a median follow-up time of 8.9 years. For the validation cohort (N = 599, mean age = 85), outpatients were enrolled with a median follow-up of 2.6 years. A five-factor risk score (the Hong Kong Osteoporosis Study [HKOS] score) for incident hip fracture was derived and validated, and its predictive accuracy was evaluated and compared with other risk scores.Results: In the derivation cohort, the C-statistics were .65, .61, .65, .76, and .78 for FRAX with bone mineral density (BMD), FRAX without BMD, QFracture, Garvan, and the HKOS score, respectively. The category-less net reclassification index and integrated discrimination improvement of the HKOS score showed a better reclassification of hip fracture than FRAX and QFracture (all p < .001) but not Garvan, while Garvan, but not HKOS score, showed a significant over-estimation in fracture risk (Hosmer-Lemeshow test p < .001). In the validation cohort, the HKOS score had a C-statistic of .81 and a considerable agreement between expected and observed fracture risk in calibration.Conclusion: The HKOS score can predict 10-year incident hip fracture among the oldest old in Hong Kong. The score may be useful in identifying the oldest old patients at risk of hip fracture in both community-dwelling and hospital settings. [ABSTRACT FROM AUTHOR]

Background: Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis.Methods: We included all people with diabetes (age at diagnosis 1.5-100 years during 2002-15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria ("combination algorithms"). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort.Results: There were 10,196 (T1D n = 60, T2D n = 10,136) and 5101 (T1D n = 43, T2D n = 5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9 years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The "high sensitivity for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, or at least 1 insulin prescription within 90 days) had a sensitivity of 95.3% (95% confidence interval 84.2-99.4%; PPV 12.8%, 9.3-16.9%), while the "high PPV for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4-100.0%; sensitivity 37.2%, 23.0-53.3%), and the "optimized" algorithm (ratio of type 1 to type 2 codes ≥ 4, and at least 1 insulin prescription within 90 days) had a sensitivity of 65.1% (49.1-79.0%) and PPV of 75.7% (58.8-88.2%) across all ages. Accuracy of T2D classification was high for all algorithms.Conclusions: Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question. [ABSTRACT FROM AUTHOR]

Most studies of students' understanding of decimals have been conducted within Western cultural settings. The broad aim of the present research was to gain insight into Chinese Hong Kong grade 6 students' general performance on a variety of decimals tasks. More specifically, the study aimed to explore students' mathematical reasoning for their use of 'rules' and algorithms and to determine whether connections exist between students' conceptual and procedural knowledge when completing decimals tasks. Results indicated that conceptual understanding for rules and procedures were built into the students' knowledge system for most of the items concerned with place value in decimals-ordering decimals, translating fractions into decimals, the representation of place value in decimals, the concept of place value in decimals on number line and the concept of continuous quantity in decimals. However, the students were not able to provide such clear explanations for the use of algorithms for the multiplication and division items. The findings are discussed in the light of Chinese perspectives on procedural and conceptual understanding. [ABSTRACT FROM AUTHOR]

The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases. [ABSTRACT FROM AUTHOR]

Objective: While access and utilization form core components in assessing the effectiveness of a health service, the concept of coverage is often neglected. In this study we propose to develop a GIS-based methodological framework for the measurement of district-based geographic coverage to examine the service effectiveness of methadone treatment programme (MTP) in Hong Kong on a regular basis. Methods: To overcome the incompatibility of spatial units, population data and data of heroin addiction of the year 2001 are interpolated by population-weighted and area-weighted algorithms. Standard overlay and proximity analytical functions are used to delineate altogether 20 accessible zones around each methadone clinic at a fixed 1.5 km Euclidean distance. Geographic coverage here is defined as the percentage of heroin addicts covered by a methadone clinic within the accessible zone by district. Results: A total of 6413 out of 11000 reported heroin addicts are found geographically covered. The average geographic coverage in Hong Kong is 44.6%, with the figure varying from 0% to 96% by district. One district having no clinic results in 0% coverage whereas another without a clinic yields 15.3% coverage from the clinic in adjacent district. Maps illustrating district-based geographic coverage are generated. Conclusion: As continuous data collection is required for a monitoring system, the simplified approach facilitates the handling of large volume data and relevant data analysis. It is concluded that the number of methadone clinics is as important as their locations. Geographic coverage could become an important consideration for monitoring harm reduction. [ABSTRACT FROM AUTHOR]

The article presents the effectiveness of using an algorithmic approach to error correction. This is a research to help Hong Kong English-as-a-second-language (ESL) learners overcome lexico-grammatical problems. Ten error types were used for the experiment with one set of remedial instructional materials designed for each. More than 450 secondary and tertiary level students were administered with pretests, posttests, and delayed posttest. A plenary review meeting was organized to gather feedback for the effectiveness of the approach.

Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography. [ABSTRACT FROM AUTHOR]