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Coastal ship berth planning and management necessitate accurate identification of ship position and size, yet this is complicated by the presence of diverse ship types and mixed distribution patterns. Traditional survey-based and position-tracking ship monitoring methods have limitations in reflecting the precise spatial information of coastal ships and their surroundings. This paper introduces a novel aerial visual data-driven methodology to estimate the berthing capacity of sheltered space for stationary ships in geographically dispersed water areas. To measure the berthing capacity, image data from an unmanned aerial vehicle (UAV) survey is collected to reflect the spatial information of all types of ships. A deep learning-based computer vision algorithm is used to automatically detect, identify, and classify ships in sample images. Additionally, we introduce the occupancy factor to empirically quantify the spatial proportional correlation between the ship size and the requisite berthing capacity. A validation test of the proposed method is conducted across all sheltered spaces in Hong Kong, and the UAV survey yields over 7,000 informative images of all types of local ships. The ship identification algorithm achieves an average identification rate of 98.6%, demonstrating higher accuracy and reduced labor and time consumption than observational survey and manual counting methods. Findings indicate that the suggested sheltered space for individual ships is 7.75 times the ship size. The approach provides greater flexibility in estimating berthing capacity for mixed berthing forms in extensive or geographically dispersed water bodies, thereby having the potential to support the design of berths, anchorage areas, and typhoon shelter facilities in coastal regions. [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]

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]

• 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]

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]

• 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]

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]