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Waste paper disposed in landfills notably contributes to greenhouse gas (GHG) emissions and impedes more sustainable, circular alternatives, such as recycling. In Hong Kong, this unsustainable approach is currently dominant as 68% of waste paper products are treated in landfills in 2020. To contextualize the impact of local waste paper management and explore mitigation potentials of circular alternatives, this paper develops a quantitative assessment framework around GHG emissions development trajectories. Combining guidelines of the Intergovernmental Panel on Climate Change (IPCC), national GHG inventories, and local parameters from life cycle analysis, five GHG emissions projections were simulated along the Shared Socioeconomic Pathways (SSPs) until 2060. Most recent baselines indicate that Hong Kong's current waste paper treatment generated 638,360 tons CO 2 -eq in 2020, comprising 1,821,040 tons CO 2 -eq from landfill and 671,320 tons CO 2 -eq from recycling, and −1,854,000 tons CO 2 -eq from primary material replacement. Proceeding along a Business-as-Usual scenario under SSP5, GHG emissions will dramatically increase to a net 1,072,270 tons CO 2 -eq by 2060, whereas a recycling-intensive scenario will lead to a net saving of −4,323,190 tons CO 2 -eq. To complement the quantitative evidence on the benefits of waste paper recycling, field research was conducted to explore the feasibility of circular policy innovation from the perspective of recycling stakeholders. These empirical qualitative and quantitative findings from stakeholders' business routines and material transactions provide crucial indications for policy and institutional innovation: Essentially, for Hong Kong to improve waste paper recycling capacities and facilitate a circular economy (CE), local stakeholders require support via fiscal policy measures (financial subsidies or tax reductions) and infrastructure improvements (delivery access and material storage). In sum, this study employs a novel analytical framework combining original qualitative and quantitative evidence to provide policy innovation towards circular, GHG emission-saving waste paper management. [Display omitted] • A new framework assessing greenhouse gas emissions from waste paper treatment. • Exploring long-term carbon neutrality pathways for managing waste paper. • Using empirical evidence from recycling stakeholders for circular policy innovation. [ABSTRACT FROM AUTHOR]

Collection, as the first stage of the recycling process, and pre-processing to increase waste stream purity constitute key operations for achieving high recycling rates and a closed loop in the circular economy. For wastepaper recycling in Hong Kong, it is the semi-formal-informal recycling (SFIR) sector that plays a central role in both stages. This study analyses the so far little explored SFIR stakeholder network in Hong Kong and sheds light on value chains, recovered waste paper quantities and the impact of policies on the sector. The findings show that SFIR stakeholders reclaim 55.6 t/recycling station/month of wastepaper and thereby achieve about a 5.7 times higher reclaim rate than the formal-private recycling system. Critical to this achievement is the SFIR transaction network, which enables its stakeholders to develop a value chain and thereby generate an added value of around 1900 HKD (243.9 USD/t) for wastepaper. Given limited circular structures in Hong Kong, financial subsidies are critical to sustain the SFIR's operations over the short-term. However, over the long-term legalization and integration of its stakeholders are necessary to ensure an economically and socially sustainable wastepaper recovery system in Hong Kong. [Display omitted] • Qualitative & quantitative analysis of the semi-formal-informal recycling (SFIR) sector. • The SFIR's wastepaper value chain achieves a cumulative added value of 1900 HKD/t. • The SFIR recovered 5.7 times higher capacities than the formal system in 2021. • Financial support can enhance the SFIR sector's recovery capacities. [ABSTRACT FROM AUTHOR]

Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban–rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban–rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level. [ABSTRACT FROM AUTHOR]

In the countryside, where intensive rural development and rising property values have been observed since the late 1970s, the preservation and meaning of traditional dwellings have emerged as crucial considerations in shaping Hong Kong's countryside conservation policy. The focus of this paper is on the conservation of temples, as we are drawn to these structures not only for their architectural merit but also for their role as public buildings within the countryside. Consequently, their cultural significance is heavily influenced by the stakeholders they are associated with, distinguishing them from other types of dwellings. This paper examines the coastal landscape of South Lantau, which includes several villages with distinct traditions that have undergone significant transformations since the 1960s. Located at the southwest edge of Hong Kong, Lantau Island had a rural lifestyle that was relatively undisturbed before WWII because of the difficulty in accessing the island. However, in response to the large people influx from China's mainland after 1949, the Hong Kong government introduced the Lantau Development Plan in 1953, mainly to open up new sources of freshwater resources for Hong Kong Island. The construction of South Lantau Road in 1955 and Shek Pik Reservoir in 1957 gradually led to the decline of agricultural activities in South Lantau and the population exodus of local villagers, resulting in a rupture to regional traditions. In multilineage communities such as the villages on South Lantau, worship of a common deity provides a vital source of social bonding, particularly for an area long beset by typhoons, piracy and banditry. Local people pray for safety and prosperity through collective rituals and celebratory activities. Therefore, temples are not only religious establishments but also embody the sense of social solidarity among villagers on South Lantau. Through the case study of three rural villages on South Lantau, this paper examines how temples play an important role in the continuity of the communal network amid repercussions from the postwar disruption of traditional rural life. Significantly, this pilot study also draws attention to the functions of temples in their historical context. It emphasises the importance of recognising and re-evaluating these buildings for cultural enhancement as part of community engagement. This study provides valuable insights for future conservation efforts for religious buildings in the countryside. [ABSTRACT FROM AUTHOR]

The concept of proper equilibrium was established as a strict refinement of perfect equilibrium. This establishment has significantly advanced the development of game theory and its applications. Nonetheless, it remains a challenging problem to compute such an equilibrium. This paper develops a differentiable path-following method with a compact formulation to compute a proper equilibrium. The method incorporates square-root-barrier terms into payoff functions with an extra variable and constitutes a square-root-barrier game. As a result of this barrier game, we acquire a smooth path to a proper equilibrium. To further reduce the computational burden, we present a compact formulation of an ε-proper equilibrium with a polynomial number of variables and equations. Numerical results show that the differentiable path-following method is numerically stable and efficient. Moreover, by relaxing the requirements of proper equilibrium and imposing Selten's perfection, we come up with the notion of perfect d-proper equilibrium, which approximates a proper equilibrium and is less costly to compute. Numerical examples demonstrate that even when d is rather large, a perfect d-proper equilibrium remains to be a proper equilibrium. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms-Continuous. Funding: This work was partially supported by General Research Fund (GRF) CityU 11306821 of Hong Kong SAR Government. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0148) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0148). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the "super typhoon direct-hit" scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO's PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper. [ABSTRACT FROM AUTHOR]

A ground-based augmentation system (GBAS) is a critical component in civil aviation that augments the Global Positioning System (GPS) in providing precision approach and landing capabilities with guaranteed accuracy and integrity. The GBAS ground facility broadcasts a parameter known as σvig to the aircraft, which is used to compute vertical protection levels for evaluating navigation integrity. σvig represents the standard deviation of the vertical ionospheric gradients, which bounds the spatial gradients under nominal conditions. Although the time-step method has been widely utilized to estimate ionospheric spatial gradients, this strategy suffers from temporal effects. In this paper, an improved time-step method is developed for separating temporal gradients from spatial gradients using observation data collected from the Hong Kong Satellite Positioning Reference Station Network. We investigated two parameters: σtg, which bounds the standard deviation of temporal gradients, and σvig. The results show that a constant value of 5.5 mm/km can serve as an upper bound for all σtg values. However, the results of σvig vary seasonally, with maximum and minimum values occurring at the equinoxes and summer, respectively. To reflect this seasonality, quadratic polynomial expressions, given as functions of the day of the year, were derived to provide an upper bound for all σvig values. [ABSTRACT FROM AUTHOR]

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy "Levy flight" and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm's limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. [ABSTRACT FROM AUTHOR]

Background: Health communication in the COVID-19 pandemic can be effectively implemented if all members of the populations, including marginalized population such as migrant domestic workers (MDWs), have good eHealth literacy. Lessons learned during this critical period may help improve planning and mitigation of the impacts of future health crises. Methods: This study aimed to examine and explore the eHealth literacy levels of the MDWs in Hong Kong during the COVID-19 pandemic by using a convergent mixed methods research design. A total of 1156 Hong Kong MDWs participated in a paper-based survey using a multistage cluster random sampling design for the quantitative component. eHealth literacy was measured using an eHealth literacy Scale (eHEALS). For the qualitative component, a purposive sampling of 19 MDWs participated in face-to-face, semi-structured, in-depth interviews. Descriptive statistics and multiple regression analyses were used to carry out the quantitative analysis, while thematic analysis was used for the qualitative analysis. Both quantitative and qualitative data were merged and integrated for mixed-methods analysis. Results: The meta-inferences of the quantitative and qualitative results mainly confirmed that MDWs in Hong Kong had good levels of eHealth literacy. The use of Instagram, YouTube and WhatsApp as the COVID-19 information sources, in addition to having an interest in the topic of the current spread of COVID-19 together with the Hong Kong government's policies related to COVID-19, were found to be associated with eHealth literacy. Conclusions: The eHealth literacy level of MDWs in Hong Kong was shown to be good and it was influenced by the use of popular social media platforms including Instagram, YouTube, and WhatsApp. It is realistic to suggest that such platforms should be harnessed for health communication during the pandemic. Yet, regulations to combat false information on these media are also urgently needed. [ABSTRACT FROM AUTHOR]

In multipartner projects, team members from two or more organizations often have different skills and behavioral preferences. A major purpose of managing such teams is fostering cooperation among different types of team members, which is critical for the successful completion of a project. Traditional studies assumed that the preferences of members within a team are homogeneous and examined their effects on cooperation. Motivated by the heterogeneity of team members' preferences within a team, we propose a model of effort expenditures in multipartner project teams with two heterogeneous members to explore how the cooperative effort expenditure (CEE) of one agent is affected by the preference type of a coworker. We theoretically formulate the problem, provide the solution, and conduct the parameter sensitivity analysis. Furthermore, we present a numerical example to demonstrate the usage of the model and gain insight into the mechanism of the effect of heterogeneous preferences on the team cooperation. The theoretical analysis and experimental results showed that the effort cost and potential value of collaboration are the most critical factors that determine the interaction among the team member's optimal CEE, project outputs, and the coworker's preference type. The payoff differences between team members play a complex moderating role in the effect of team members' heterogeneity preference types on team cooperation. This paper contributes to the body of knowledge on project management by understanding the cooperative behavior of team members with heterogeneous behavioral preferences, and providing managerial insights for project managers to promote cooperation in multipartner project teams. Multipartner projects have become increasingly common due to the increasing complexity and difficulty of the projects. These projects, such as mega construction projects, require the input from professionals of diverse disciplines. But the heterogeneities of partners (stakeholders) increase the difficulty of cooperation. This paper provides managerial insights and suggestions for the project managers to promote cooperation between team members with heterogeneous preference types and marginal effort contributions in multipartner projects. First, for low-complexity projects (such as simple housing construction projects) and high-complexity projects (such as the Hong Kong–Zhuhai–Macao Bridge project), the project managers may pay less attention to the heterogeneity of team members' behavioral preferences. Second, for other moderate-complexity projects, project managers should emphasize soft management to reduce the negative influence caused by behavioral preference heterogeneity, such as hiring team members with high risk tolerance, reducing payoff information transparency between team members and concealing their payoffs, fostering reciprocity among the agents, reducing their level of spitefulness through team building, and reducing competition, as well as encouraging the positive emotions (such as altruism) through publicity, education, praise, and rewards. Third, to avoid negative influence caused by profit-sharing differences (PSDs), it is better for project managers to set a higher profit-sharing rate for team members with a higher marginal contribution. [ABSTRACT FROM AUTHOR]

Collecting feedback from students through course, program and other evaluation questionnaires has become a costly and time consuming process for most colleges. Converting to data collection through the internet, rather than completion on paper, can result in a cheaper and more efficient process. This article examines several research questions which need to be answered to establish that results collected by the two modes of administration are equivalent. Data were gathered for a program evaluation questionnaire from undergraduate students at a university in Hong Kong. Students were able to choose between completion on paper or through the internet. In six of the seven Faculties the number of responses through each mode was roughly the same. Students in the Engineering Faculty favored the internet. Scores on the 14 out of 18 scales in the instrument showed small differences by mode of response, which became smaller still with controls for pertinent demographic variables. The main response question addressed in the study was whether there was any difference in the way respondents to the two modes interpreted the questions. The study demonstrated the equivalence of the two data sets by showing that both could be fitted to a common model with structural equation modeling (SEM). Five levels of tests of invariance further confirmed the comparability of data by mode of administration. This study, therefore suggests that changing to internet collection for course and program evaluations will not affect the comparability of ratings. [ABSTRACT FROM AUTHOR]

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0112). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the 2009 Conference on Advances in Microfluidics and Nanofluidics at the Hong Kong University of Science & Technology in Hong Kong, China on January 5-7. Among the supporters of the conference include the Institute for Advanced Study, the Institute of Complex Adaptive Matter and the Institute of Integrated Micro Systems. The conference consisted of sessions, contributed talks and poster presentations. Topics discussed include microfluidic science, nanofluidic science and microfludic and nanofluidic applications.

This paper discusses the cocreation of an emergent place-identity in a Hakka household in the Hong Kong countryside triggered by architectural restoration. Located in Kuk Po, a quintessential Hakka village that was established over 300 years ago, Yeung House was restored by the research team under a government-funded initiative to regenerate architectural values. Rebuilt in 1967, Yeung House has experienced decay and evolution over the years. It signifies the adaptive lifestyle of Hakka immigrants through its adapted siting, hybrid tectonics and modernised features. After restoration, Yeung House was repurposed into a base for education and research managed by the three house-owning villagers. Different activities were initiated by the villagers and coarranged by the research team. As a serendipitous outcome, the front yard of Yeung House has been activated into a multifunctional communal space. Subsequently, it is argued that a place-identity has emerged in Ng To and is influenced by the distinctiveness of the restored Yeung House and the continuity of the front yard. New meanings of the place have been created by the villagers through the practice of a potential, alternative lifestyle in the rural-urban parallel. [ABSTRACT FROM AUTHOR]

In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DGB), etc. placed on Zhi'wan, Wai'ling'ding, Dan'gan, and Miao'Wan islands of the WSCS. The PGSs data of Zhi'wan and Wai'ling'ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCS. The DGB was used to transfer the datum from the PGSs to the ATGs of Zhi'wan, Wai'ling'ding, and Dan'gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99jb, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMTIDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan'gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was −16.7 ± 45.2 mm, with a drift of 0.5 mm/year. The HY-2C biases were −18.9 ± 48.0 mm with drifts of 0.0 mm/year and −5.6 ± 49.3 mm with −0.3 mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was −4.1 ± 78.7 mm for Pass No. 153 and −25.8 ± 85.5 mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be −16.5 ± 46.3 mm with a drift of −0.6 mm/year and −9.8 ± 30.1 mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS. [ABSTRACT FROM AUTHOR]

For an unbalanced cooperative game, its grand coalition can be stabilized by some instruments, such as subsidization and penalization, that impose new cost terms to certain coalitions. In this paper, we study an alternative instrument, referred to as cost adjustment, that does not need to impose any new coalition-specific cost terms. Specifically, our approach is to adjust existing cost coefficients of the game under which (i) the game becomes balanced so that the grand coalition becomes stable, (ii) a desired way of cooperation is optimal for the grand coalition to adopt, and (iii) the total cost to be shared by the grand coalition is within a prescribed range. Focusing on a broad class of cooperative games, known as integer minimization games, we formulate the problem on how to optimize the cost adjustment as a constrained inverse optimization problem. We prove NP -hardness and derive easy-to-check feasibility conditions for the problem. Based on two linear programming reformulations, we develop two solution algorithms. One is a cutting-plane algorithm, which runs in polynomial time when the corresponding separation problem is polynomial time solvable. The other needs to explicitly derive all the inequalities of a linear program, which runs in polynomial time when the linear program contains only a polynomial number of inequalities. We apply our models and solution algorithms to two typical unbalanced games, including a weighted matching game and an uncapacitated facility location game, showing that their optimal cost adjustments can be obtained in polynomial time. History: Accepted by Area Editor Andrea Lodi for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72022018 and 72091210]; the Research Grants Council of the Hong Kong SAR, China [Grant 16210020]; Hong Kong Polytechnic University [Grant P0032007]; and the Youth Innovation Promotion Association, Chinese Academy of Sciences [Grant 2021454]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0268. [ABSTRACT FROM AUTHOR]

In this study, the ill-conditioning of the iterative method for nonlinear models is discussed. Due to the effectiveness of ridge estimation for ill-conditioned problems and the lack of a combination of the H-K formula with the iterative method, the improvement of the LM algorithm is studied in this paper. Considering the LM algorithm for ill-conditioned nonlinear least squares, an improved LM algorithm based on the H-K formula is proposed for image distortion correction using self-calibration. Three finite difference methods are used to approximate the Jacobian matrix, and the H-K formula is used to calculate the damping factor in each iteration. The Brown model, quadratic polynomial model and Fourier model are applied to the self-calibration, and the improved LM algorithm is used to solve the model parameters. In the simulation experiment of space resection of a single image, we evaluate the performance of the LM algorithm based on the gain ratio (LMh) and the improved LM algorithm based on the H-K formula (LMHK), and the accuracy of different models and algorithms is compared. A ridge trace analysis is carried out on the damping factor to illustrate the effects of the improved algorithm in handling ill-conditioning. In the second experiment, the improved algorithm is applied to measure the diameter of a coin using a single camera. The experimental results show that the improved LM algorithm can reach the same or higher accuracy as the LMh algorithm, and it can weaken the ill-conditioning to a certain extent and enhance the stability of the solution. Meanwhile, the applicability of the improved LM algorithm in self-calibration is verified. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and deep learning techniques are playing an increasing role in astronomy to deal with the data avalanche. Here we describe an application for finding resolved planetary nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane, to test and facilitate more objective, reproducible, efficient and reliable trawls for them. PNe are important for studying the late-stage stellar evolution of low-mass to intermediate-mass stars. However, the confirmed ∼3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming because of the large data size and areal coverage of modern astronomical surveys. The training and validation data set of our algorithm was built with the INT Photometric Hα Survey (IPHAS) and true PNe from the Hong Kong/AAO/Strasbourg Hα (HASH) data base. Our algorithm correctly identified 444 PNe in the validation set of 454 PNe, with only 16 explicable 'false' positives, achieving a precision rate of 96.5 per cent and a recall rate of 97.8 per cent. After transfer learning, it was then applied to the VST Photometric Hα Survey of the Southern Galactic plane and bulge (VPHAS+), examining 979 out of 2284 survey fields, each covering 1° × 1°. It returned ∼20 000 detections, including 2637 known PNe and other kinds of catalogued non-PNe. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for optical spectroscopic follow-up. We found that 74 per cent of them are true, likely, and possible PNe. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques, with full details to be given in our forthcoming paper. [ABSTRACT FROM AUTHOR]

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issuance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone (TC) warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over the coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and dropsonde data suggested boundary layer inflow, warm core structure, eyewall updraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated the occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study highlights the forecasting performance of the AI‐based Pangu‐Weather model, which could outperform the conventional global numerical weather prediction models in forecasting TC track in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclone. Key Points: Observations from meteorological aircraft, ocean radar, and synthetic aperture radar show the structure of Severe Typhoon KoinuWeather radar observations suggest the occurrence of a waterspout in the vicinity of KoinuAI‐based Pangu‐Weather model could outperform the conventional global numerical weather prediction models in forecasting Koinu's track [ABSTRACT FROM AUTHOR]

The current paper replicates findings related to the analytical heuristic of 'metalinguistic density'. It provides updated theoretical motivations for an analytical focus on explicit metalanguage in online text-based settings, based on notions of symbolic interactionism. An analysis of online user comment data of the 2019 Hong Kong protests reconfirms that the relative occurrence of metalanguage focusing on certain terms indicates the extent to which these terms' denotational norms are shared amongst a community of language users. While most terms in the corpus do not regularly attract explicit metalanguage, several terms thematically related to the protests did. This disproportionate metalinguistic attention may be explained by political disputes on the movement's legitimacy, which led to normative debates on the discursive representation of the protest movement. Finally, certain phrases had become indexical of a certain political position to the extent that they were the frequent target of irony. In general, the results suggests that 'metalinguistic density' is a useful heuristic to employ in text-analytical investigations that seek to explain participants' interpretations of discursive interactions. • 'Metalinguistic density' measures the sharedness of terms' denotations. • Results of earlier research are replicated in a thematically distinct dataset. • Metalanguage reflects ideological clashes over discursive representation. [ABSTRACT FROM AUTHOR]

Increased glycolysis is a key characteristic of malignant cells that contributes to their high proliferation rates and ability to develop drug resistance. The glycolysis rate-limiting enzyme hexokinase II (HK II) is overexpressed in most tumor cells and significantly affects tumor development. This paper examines the structure of HK II and the specific biological factors that influence its role in tumor development, as well as the potential of HK II inhibitors in antitumor therapy. Furthermore, we identify and discuss the inhibitors of HK II that have been reported in the literature. [ABSTRACT FROM AUTHOR]

Hong Kong was under the direct hit of Severe Typhoon Koinu (2314) on 8 and 9 October 2023, necessitating the issuance of the Increasing Gale or Storm Signal, No. 9. Koinu was a very challenging case for TC forecasting and warning services due to its compact size and erratic movement over the northern part of the South China Sea. This paper reviews the difficulties and challenges of the forecasting aspect of the severe typhoon. The predicted tropical cyclone track and intensity from both conventional models and emerging artificial intelligence models are examined, as well as local wind and rainfall forecast. Experience in this case study showed that while deterministic global models only performed moderately and were not able to adequately support early warning, a regional model and AI models could more effectively support decision making for an operational tropical cyclone warning service. [ABSTRACT FROM AUTHOR]

Centimeter-level or better positioning accuracy is needed in engineering surveying applications. When employing the Global Navigation Satellite System (GNSS) in engineering surveying, the high-precision real-time kinematic (RTK) positioning method must be used to achieve such positioning accuracy. Currently, precise point positioning (PPP) cannot reliably achieve the positioning accuracy needed for engineering surveying in real time. The high-precision RTK positioning method needs carrier-phase measurements and a reference station/network. Surveyors may not need a GNSS receiver in their organizations/companies to act as the reference station. Continuously Operating Reference Stations (CORS), run by international/national organizations/agencies or private companies such as GNSS receiver manufacturers, let users freely access the raw GNSS measurements or corrections for real-time and post-processing applications. The positioning accuracy of the GNSS rover is affected by the data quality of the reference stations, including virtual reference stations (VRS). The International GNSS Service (IGS) currently provides the number of cycle slips and the L1 and L2 average pseudorange multipath errors per station daily. The US National Geodetic Survey (NGS) provides daily station coordinate residuals. Carrier-phase data quality of the CORS stations is not provided by their organizations/agencies. Nowadays, many CORS stations track multi-GNSS satellites. This paper proposes a multi-GNSS and multi-frequency data quality assessment approach for CORS stations with a focus on carrier-phase data quality. The proposed approach is demonstrated with case studies on IGS/CORS networks in Hong Kong and Canada/USA. In other words, a strategy to obtain non-linear combined carrier-phase multipath errors and noise is proposed in this work. The data quality of a CORS station depends on the site environment, monument type and height, and GNSS receiver/antenna. An account of the data quality at some selected stations is given; the main focus of the paper is on the proposed data quality assessment approach. [ABSTRACT FROM AUTHOR]

The use of infrared thermography (IRT) technique combining other remoting sensing techniques such as photogrammetry and unmanned aerial vehicle (UAV) platforms to perform geotechnical studies has been attempted by several previous researchers and encouraging results were obtained. However, studies using time-lapse IRT survey via a UAV equipped with a thermal camera are limited. Given the unique setting of Hong Kong, which has a high population living in largely hilly terrain with little natural flat land, steep man-made slopes and natural hillsides have caused significant geotechnical problems which pose hazards to life and facilities. This paper presents the adoption of a time-lapse IRT survey using a UAV in such challenging geotechnical conditions. Snapshot and time-lapse IRT studies of a selected site in Hong Kong, where landslides had occurred were carried out, and visual inspection, photogrammetry, and IRT techniques were also conducted. 3D terrain models of the selected sites were created by using data collected from the photogrammetry and single (snapshot) and continuous monitoring (time-lapse) infrared imaging methods applied in this study. The results have successfully identified various thermal infrared signatures attributed to the existence of moisture patches, seepage, cracks/discontinuities, vegetation, and man-made structures. Open cracks/discontinuities, moisture, vegetation, and rock surfaces with staining can be identified in snapshot thermal image, while the gradient of temperature decay plotted in ln(T) vs. ln(t) enables quantifiable identifications of the above materials via time-lapse thermography and analysis. [ABSTRACT FROM AUTHOR]

Accurate positioning in urban canyons remains a challenging problem. To facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, we built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Kong. The dataset provides multi-sensor data, including data from multi-frequency global navigation satellite system (GNSS) receivers, an inertial measurement unit (IMU), multiple light detection and ranging (lidar) units, and cameras. Meanwhile, the ground truth of the positioning (with centimeter-level accuracy) is postprocessed by commercial software from NovAtel using an integrated GNSS real-time kinematic and fiber optics gyroscope inertial system. In this paper, the sensor systems, spatial and temporal calibration, data formats, and scenario descriptions are presented in detail. Meanwhile, the benchmark performance of several existing positioning methods is provided as a baseline. Based on the evaluations, we conclude that GNSS can provide satisfactory results in a middle-class urban canyon if an appropriate receiver and algorithms are applied. Both visual and lidar odometry are satisfactory in deep urban canyons, whereas tunnels are still a major challenge. Multisensory integration with the aid of an IMU is a promising solution for achieving seamless positioning in cities. The dataset in its entirety can be found on GitHub at https://github.com/ IPNL-POLYU/UrbanNavDataset. [ABSTRACT FROM AUTHOR]

The ℓ 0 trend filtering ( ℓ 0 -TF) is a new effective tool for nonparametric regression with the power of automatic knot detection in function values or derivatives. It overcomes the drawback of ℓ 1 -TF that is known to have bias issues. To solve the ℓ 0 -TF problem, we propose an alternating minimization induced active set (AMIAS) search method based on the necessary optimality conditions derived from an augmented Lagrangian framework. The proposed method takes full advantage of the primal and dual variables with complementary supports, and decouples the high-dimensional problem into two subsystems on the active and inactive sets, respectively. A sequential AMIAS algorithm with warm start initialization is developed for efficient determination of the cardinality parameter, along with the output of solution paths. Theoretically, the oracle estimator of ℓ 0 -TF is justified to behave like regression splines under the continuous time setting with mild conditions. Our numerical experiments include simulation studies for comparing ℓ 0 -TF to ℓ 1 -TF and free-knot splines on several synthetic examples, and a real data application of time series segmentation on Hong Kong PM2.5 indexes. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Funding: This work was supported in part by Hong Kong General Research Fund [No. 17306519]. C. Wen's research is partially supported by National Science Foundation of China [12171449] and Fundamental Research Funds for the Central Universities [WK3470000027, YD2040002019]. X. Wang's research is partially supported by National Natural Science Foundation of China [Grants 72171216, 12231017, 71921001, and 71991474], and the National Key R&D Program of China [No. 2022YFA1003803]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0313. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0313) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0313). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas. [ABSTRACT FROM AUTHOR]

Many warehouses involved in e-commerce order fulfillment use robotic mobile fulfillment systems. Because demand and variability can be high, scheduling orders, robots, and storage pods in interaction with manual workstations are critical to obtaining high performance. Simultaneously, the scheduling problem is extremely complicated because of interactions between decisions, many of which must be taken timely because of short planning horizons and a constantly changing environment. This paper models all such scheduling decisions in combination to minimize order fulfillment time. We propose two decision methods for the above scheduling problem. The models batch the orders using different batching methods and assign orders and batches to pods and workstations in sequence and robots to jobs. Order picking and stock replenishment operations are included in the models. We conduct numerical experiments based on a real-world case to validate the efficacy and efficiency of the model and algorithm. Instances with 14 workstations, 400 orders, 300 stock-keeping units (SKUs), 160 pods, and 160 robots can be solved to near optimality within four minutes. Our methods can be applied to large instances, for example, using a rolling horizon. Because our model can be solved relatively fast, it can be used to take managerial decisions and obtain executive insights. Our results show that making integrated decisions, even when done heuristically, is more beneficial than sequential, isolated optimization. We also find that positioning pick stations close together along one of the system's long sides is efficient. The replenishment stations can be grouped along another side. Another finding is that SKU diversity per pod and SKU dispersion over pods have strong and positive impacts on the total completion time of handling order batches. Funding: This work was supported by National Natural Science Foundation of China [72025103, 72361137001, 71831008, 72071173] and the Research Grants Council of the Hong Kong Special Administrative Region, China [HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0265. [ABSTRACT FROM AUTHOR]

Simple Summary: Genome-wide DNA methylation profiling has emerged as an important diagnostic tool that complements histopathology for characterizing central nervous system (CNS) tumors. The literature describing its application in Asian countries is limited. We report the feasibility and utility of adopting such profiling for children with diagnosed CNS tumors in Hong Kong. The study included samples of 97 patients with CNS embryonal or high-grade neuroepithelial tumors (HGNETs). In our DNA methylation-profiled cohort, 67% of results correlated with the diagnosis and assigned molecular subgroup and 12% of results led to revision/reassignment of diagnosis. Patient outcomes correlated significantly with methylation-based subgroups. In addition, novel clinical–histologic–molecular associations were described. Taken together, epigenomic profiling enabled clinically relevant refinement of disease classification of pediatric CNS tumors. The availability of such methodology in Asia sets the stage for international collaborations in molecularly driven trials. This paper examines the link between CNS tumor biology and heterogeneity and the use of genome-wide DNA methylation profiling as a clinical diagnostic platform. CNS tumors are the most common solid tumors in children, and their prognosis remains poor. This study retrospectively analyzed pediatric patients with CNS embryonal tumors in Hong Kong between 1999 and 2017, using data from the territory-wide registry and available formalin-fixed paraffin-embedded tumor tissue. After processing archival tumor tissue via DNA extraction, quantification, and methylation profiling, the data were analyzed by using the web-based DKFZ classifier (Molecular Neuropathology (MNP) 2.0 v11b4) and t-SNE analysis. Methylation profiles were deemed informative in 85 samples. Epigenetic data allowed molecular subgrouping and confirmed diagnosis in 65 samples, verified histologic diagnosis in 8, and suggested an alternative diagnosis in 12. This study demonstrates the potential of DNA methylation profiling in characterizing pediatric CNS embryonal tumors in a large cohort from Hong Kong, which should enable regional and international collaboration in future pediatric neuro-oncology research. [ABSTRACT FROM AUTHOR]

Excessive settlement may induce structural damage and water leakage in immersed tunnels, seriously threatening the tunnels' safety. However, making accurate assessment of the settlement in immersed tunnels is difficult due to the incomplete knowledge of the geotechnical parameters and the inadequacy of the model itself. This paper proposes an effective method to accurately assess the settlement in immersed tunnels. An enhanced beam on elastic foundation model (E-BEFM) is developed for the settlement assessment, with the Bayesian adaptive direct search algorithm adopted to estimate unknown model parameters based on previous observations. The proposed method is applied to a field case of the Hong Kong-Zhuhai-Macao immersed tunnel. The original BEFM is used for comparison to highlight the better assessment performance of E-BEFM, particularly for joints' differential settlement. Results show that the proposed method can provide accurate predictions of the total settlement, angular distortion (a representation of tubes' relatively differential settlement), and joints' differential settlement, which consequently supports the associated maintenance decision-making and potential risk prevention for immersed tunnels in service. [ABSTRACT FROM AUTHOR]

The integrated development of culture and tourism is one of the important ways for China to promote rural revitalization in the new era. Guangdong-Hong Kong-Macao Greater Bay Area uses the unique lychee cultural resources of the Lingnan region for tourism development, and has formed many local experiences and typical models on the road of integrated development of culture and tourism. This paper summarized the development connotation of litchi cultural resources in Guangdong-Hong Kong-Macao Greater Bay Area. Based on the existing studies on the integration of lychee cultural tourism, from the perspective of industrial economy, the experience of the integrated development of lychee cultural tourism in the Greater Bay Area is divided into four typical models: the integrated development mode of popular science research, the integrated development mode of cultural festival, the integrated development mode of picking experience, and the integrated development mode of industry and tourism joint operation. Through in-depth analysis of the main characteristics of different modes and typical case practice, the specific path and industry dynamics of the integrated development of lychee cultural tourism in the Greater Bay Area were clarified. Finally, from the perspective of problems, it is proposed that the Greater Bay Area can carry out mechanism innovation in the following aspects, such as government role, platform construction and public participation, so as to promote the integration of lychee culture and tourism to achieve a unified development direction of social benefits, cultural benefits and economic benefits. For the first time, this paper makes a comprehensive interpretation of lychee cultural tour, which relies on local fruit cultural resources to design rural revitalization road. The research results can provide some enlightenment for other areas to implement rural revitalization integrated development of cultural tourism. [ABSTRACT FROM AUTHOR]

Food classification is an important part of food safety standards. In this paper, we propose a novel visual comparative analysis method for food classification trees (FCTs) in pesticide maximum residue limit (MRL) standards, called TreeMerge, to lay the foundation for a comprehensive comparison of pesticide MRL standards. First, a union tree is constructed by combining the two FCTs to be compared. Then, sunburst with an embedded chordal graph (SECG) and overlapping circular treemap (OCT), which are two new visualization solutions designed in this paper, are used to show the similarities and differences in a union tree. SECG can express the hierarchical structure and the similarity between corresponding nodes in the union tree at the same time. OCT uses an improved nested Venn diagram (overlapping circle) to express the attribute values in each layer of the union tree and uses a circle-filling layout algorithm based on the testing circle to improve the readability and space utilization of the view. Finally, a visual analysis system for comparing FCT, named FCTvis, is designed and implemented to support the exploration of the structural difference pattern of food classification in the two MRL standards and the quantity or scale of residue limits in various foods. The effectiveness of TreeMerge was verified by case studies on pesticide MRL standards in the Chinese Mainland and Chinese Hong Kong. [ABSTRACT FROM AUTHOR]

This paper investigates an 11-dimensional slow–fast system of multiscale COVID-19 model with interval parameters. For the slow subsystem, the local dynamics of disease-free equilibrium (DFE) and endemic equilibrium (EE) are studied by using the fast–slow theory. Two criteria of the existence of forward and backward bifurcations are further obtained. When the basic reproduction number R 0 is less than unity, we show that bistabilities of DFE and EE for R c < R 0 < 1 can be caused by backward bifurcation. Thus, controlling R 0 below critical value R c is effective to eliminate endemic diseases. Meanwhile, the slow subsystem undergoes saddle-node bifurcation at R 0 = R c and undergoes pitchfork bifurcation at R 0 = 1 for EE. Moreover, the stability and bifurcation of the multiscale full system are established. As an application, the numerical simulations of real data of COVID-19 in Hong Kong are used to verify these results, which show that increasing vaccination rate, improving vaccine effectiveness and decreasing the fraction of individuals in risky state 2 are necessary to control the COVID-19. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A multidepot capacitated vehicle routing problem aims to serve customers' demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer's demand can be served by more than one vehicle, and the total number of depots, denoted by k ≥ 2 , is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of (6 − 4 / k) and a polynomial running time for any fixed constant k ≥ 2. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with k ≥ 2 being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0193) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0193). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Considering how difficult it is for a pin in the ocean to be found, painstaking searches among historical documents and eyewitness accounts often end up with more unknowns and questions. We developed a three-tier geo-spatial tech-based approach to discover and unfold the lost WWII heritage features in the countryside of Hong Kong that can be applied in other contexts. It started with an analysis of historical texts, old maps, aerial photos, and military plans in the historical geographic information system (HGIS) Project 'The Battle of Hong Kong 1941: a Spatial History Project' by Hong Kong Baptist University to define regions/points of interest. Then, 3D point clouds extracted from the government's airborne LiDAR were migrated to form a digital terrain model (DTM) for geo-registration in GIS. All point clouds were geo-referenced in HK1980 Grid via accurate positioning using the global navigation satellite system—real-time kinematics (GNSS-RTK). A red relief image map (RRIM) was then used to image the tunnels, trenches, and pillboxes in great detail by calculating the topographical openness. The last tier of the tech work was field work involving ground validation of the findings from the previous two tiers and on-site imaging using terrestrial LiDAR. The ground 3D LiDAR model of the heritage feature was then built and integrated into the DTM. The three-tier tech-based approach developed in this paper is standardised and adopted to streamline the workflow of historical and archaeological studies not only in Hong Kong but also elsewhere. [ABSTRACT FROM AUTHOR]

In this paper, a three-dimensional vision-aided method is proposed to improve global navigation satellite system (GNSS) real-time kinematic (RTK) positioning. To mitigate the impact of reflected non-line-of-sight (NLOS) reception, a sky-pointing camera with a deep neural network was employed to exclude these measurements. However, NLOS exclusion results in distorted satellite geometry. To fill this gap, complementarity between the low-lying visual landmarks and the healthy but high-elevation satellite measurements was explored to improve the geometric constraints. Specifically, inertial measurement units, visual landmarks captured by a forward-looking camera, and healthy GNSS measurements were tightly integrated via sliding window optimization to estimate the GNSS-RTK float solution. The integer ambiguities and the fixed GNSS-RTK solution were then resolved. The effectiveness of the proposed method was verified using several challenging data sets collected in urban canyons in Hong Kong. [ABSTRACT FROM AUTHOR]

A major source of risk in green building projects (GBPs) is inaccurate human prediction of the final project cost and duration, which in turn results in cost and schedule overruns (i.e., poor project performance). This paper presents promising new models to mitigate such risk based upon machine learning (ML). Historical data from 198 GBPs in Hong Kong were used to develop and train two fully connected deep neural networks (DNN) models to learn and predict cost and duration, respectively, based on green building rating (GBR) and other project parameters. The models can predict cost and duration with mean absolute percentage error (MAPE) values of 0.07 and 0.09, respectively. They were then integrated with support vector regression (SVR), and results indicated that the integrated DNN-SVR models improve prediction accuracy, decreasing the MAPE from 0.07 to 0.06 (cost) and 0.09 to 0.07 (duration), respectively. The validated models were for the first time deployed as a ML-based web application for automated, fast, and accurate GBP cost and duration prediction. The feature importance analysis results revealed that the most influential parameters on the GBP cost and duration are project area and weather, respectively, not the GBR. Theoretically, the outcomes of this study provide new insights into the impact of GBR on project cost and duration, which are useful for the promotion of GBPs to improve sustainability. Practically, the study provides policymakers and practitioners with novel ML-based models and a web application to improve GBP delivery performance. Green building projects help to combat climate change and improve our health, wellbeing, and quality of life, but they face two key challenges in their execution: cost overruns and schedule delays. To address these challenges, there is a need for accurate prediction of the final project cost and duration from the early stages of the project. The practical relevance of this study is in the development of the first data-driven and machine learning-based web application for addressing this need. New integrated optimized machine learning models are developed. For practitioners to have access to and use these models without the need to possess machine learning expertise, a corresponding easy-to-use web application is offered. From the design and construction stages of their project, practitioners only need to input the green building ratings in sustainable site, materials and waste, energy use, water use, health and wellbeing, and innovations and additions they want to achieve for the project. The project type and area (type and size), original budget, planned duration, and start month (SM) and year should also be input. Once this project data input is completed, the web application automatically and instantaneously predicts with a high level of accuracy the total costs and time needed to deliver the project successfully—on time and on budget. These cost and duration prediction outputs are a valuable tool in helping practitioners adjust green building project plans and budgets to develop more realistic and accurate project budgets and programs to avoid cost overruns and schedule delays. [ABSTRACT FROM AUTHOR]

Education is recognised as an integral part of building the capacity of all ages to drive the transformation toward sustainable development. While the Hong Kong government aspires to achieve carbon neutrality by 2050 and has ramped up efforts to integrate sustainability into school curricula and raise public awareness through educational activities, this policy brief argues that Hong Kong's existing measures failed to empower and motivate individuals and the community to keep pace with its climate action ambition. Drawing on Sterling's framework of education for sustainability (EFS), this paper presents a diagnosis of Hong Kong's major problems in sustainability education policies and suggests that the Hong Kong government should learn from the Italian experience to take a more systemic, empowering, and democratic approach in advancing its EFS policies. [ABSTRACT FROM AUTHOR]

Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA's superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities. [ABSTRACT FROM AUTHOR]

Water vapor plays an extremely important role in the monitoring and prediction of weather, and GNSS tomography can obtain 3D spatiotemporal change information and reliable water vapor profiles. In this paper, an improved global navigation satellite system (GNSS) tropospheric tomography technique using an ERA5 (the fifth generation ECMWF reanalysis) product is developed. First, the ERA5 product was adopted to analyze the spatiotemporal distribution of water vapor, and a water vapor density threshold defining the top of the tomography was determined; then, the height of the grid top (GT) of different seasons was obtained through linear fitting; finally, the water vapor value between GT and tropopause is constrained using the data of the ERA5 product as the initial value. The new method for using the ERA5 product to determine the height of the GT of the tomographic grid reduces the height of the top layer of the grid and increases the number of effective GNSS rays. Data from nine CORS stations in Hong Kong in 2019 were selected for experiments. The results showed that the new algorithm increased the number of effective satellite signals by 14%. In addition, the ERA5 data, the radiosonde data, and the COSMIC‐2 data were used as reference values. The accuracy of the water vapor density obtained by the algorithm was improved by 25%, 17% and 9%, respectively. [ABSTRACT FROM AUTHOR]

Presents a study on the wastepaper-recycling behavior among college students in Hong Kong, China. Overview of the theory of planned behavior; Method; Results; Discussion.

3D lidar-based simultaneous localization and mapping (SLAM) is a well-recognized solution for mapping and localization applications. However, the typical 3D lidar sensor (e.g., Velodyne HDL-32E) only provides a very limited field of view vertically. As a result, the vertical accuracy of pose estimation suffers. This paper aims to alleviate this problem by detecting the absolute ground plane to constrain vertical pose estimation. Different from the conventional relative plane constraint, this paper employs the absolute plane distance to refine the position in the z-axis and the norm vector of the ground plane to constrain the attitude drift. Finally, relative positioning from lidar odometry, constraint from ground plane detection, and loop closure are integrated under a proposed factor graph-based 3D lidar SLAM framework (AGPC-SLAM). The effectiveness is verified using several data sets collected in Hong Kong. [ABSTRACT FROM AUTHOR]

• This study compared drivers of the same ethnic group driving across within-country borders. • The existing theoretical framework of cross-border risky behavior patterns has been expanded. • Gender, age, illumination, and weather do not individually affect cross-border driving risk. • Conclusions are of representative reference value for cross-border risk management policies. Introduction: Due to globalization and the acceleration of cross-border exchanges, cross-border risk behaviors have received widespread attention. Previous research has concluded that foreign cross-border drivers engage in relatively more risk-taking behavior patterns and are likely to experience a higher crash rate or be more inclined to cause severe crashes. However, there is little evidence on the comparison of drivers who belong to the same ethnic group driving across within-country borders. Method : Based on the cross-border motor-vehicle crash reports in 2006–2010 from the Road Traffic Accident Database of the China Ministry of Public Security, this paper examines the risk factors of being at fault and getting killed or seriously injured in cross-border traffic crashes and casual paths toward crash liability and injury severity for Hong Kong and Macao drivers driving in the Chinese mainland. Results : There are extremely complex factors behind drivers from Hong Kong and Macao causing at-fault crashes or sustaining fatal and serious injuries in the Chinese mainland. Factors such as gender, age, illumination, and weather conditions do not individually affect the risk of driver at-fault crashes or severe casualties in the crashes among Hong Kong and Macao drivers driving in the Chinese mainland. Nonetheless, collectively, these factors influence them along with different vehicle types, roads, and environmental factors. Conclusion s: This paper provides more theoretical findings for understanding the compound effect of multiple risk factors involving cross-border at-fault crashes or serious casualties. The conclusions of this research are valuable as representative references for cross-border risk management policies. Practical applications : To reduce the effects of different factors on cross-border risky driving behaviors and/or injurious crashes, various measures should be focused on, including specialized driver training, enhancement of the roads/environment, development of effective road safety campaigns, and directives facilitating cross-border cooperation in the field of road safety. [ABSTRACT FROM AUTHOR]

A typhoon passed through Hong Kong suddenly and unexpectedly on 18 September 1906 (the "Typhoon 1906") and had a disastrous impact on Victoria Harbour and its surroundings in just a couple of hours. Since the year 1906 was the "Bingwu" year in the Chinese calendar, the typhoon is also known historically as the "Bingwu Typhoon". Tremendous loss of lives and property resulted, and the shipping and fishing communities were devastated. Two mysteries arising from this calamitous typhoon have existed to date: 1) Why the Hong Kong Observatory was not able to provide any forewarning? 2) whether the storm surge reported in some contemporary records is entirely credible? This paper will focus on both of these. In this paper, we re-analyse historical weather observations recorded in various historical documents and estimate the possible storm size, intensity and track of Typhoon 1906 using tropical cyclone models. Based on the re-analyses, the storm surges, storm tides and wave heights in Hong Kong are also estimated using storm surge and wave models. The results reveal that Typhoon 1906 was a midget typhoon, with a radius of maximum winds of 11 km or smaller, during its passage through Hong Kong. This explains why it was technically impossible for a forewarning to be given at that time when real-time weather observations from ships, meteorological satellites and radars were non-existent. We also estimate that the maximum storm surges (storm tides) in Hong Kong were not higher than 0.82 m (2.43 mCD) and 1.98 m (4.15 mCD) in Victoria Harbour and Tolo Harbour respectively. These figures are found to be limited by the intensity and the storm size of the typhoon. Therefore, we conclude that the previously documented storm surge figures are not supported by the present study. [ABSTRACT FROM AUTHOR]

The outline of the development plan issued by CPC Central Committee and the State Council for the Guangdong Hong Kong Macao Bay Area clearly pointed out that the Guangdong-Hong Kong-Macao Bay Area should be built into a vibrant world-class urban agglomeration, an international science and technology innovation center, an important support for the construction of the "the Belt and Road," and a demonstration area for in-depth cooperation between the mainland and Hong Kong and Macao; and a high-quality life circle suitable for living, working, and traveling should be created to become a model of high-quality sustainable development. In order to develop the Guangdong-Hong Kong-Macao Bay Area, it is extremely important to enhance the regional business environment. This paper focuses on five influencing factors: policy support, talent guarantee, market supervision, financial support, and cultural promotion. Based on one-way ANOVA and Cobb Douglas function, this paper makes an empirical study on the two important node cities of Zhuhai and Macao. The results show that (1) different factors have different degrees of significant impact on the regional business environment, among which the industry type has the greatest impact, and the duration of enterprise registration has the least impact; (2) when the factors act alone, there is a significant positive correlation between the evaluation dimensions, but most of the correlation degrees are not much different. Finance and talents are the weaknesses of the business environment in the two cities. (3) When the factors are combined, different factors have significant differences in different regions. Zhuhai mainly benefits from policy support, while Macao mainly benefits from market supervision. (4) Regional integration can produce significant association relationship, among which cultural promotion is an important factor. These five important factors have been effectively identified, which have played a leading role in the further design and development direction of the Guangdong-Hong Kong-Macao Bay Area in the future and have an extremely important enlightenment for the study of the business environment in this region. [ABSTRACT FROM AUTHOR]

In order to improve the analysis effect of traditional arts and crafts, this paper analyzes traditional arts and crafts combined with digital technology, builds an intelligent analysis system to improve the digital processing effect of traditional arts and crafts, and takes the Guangdong-Hong Kong-Macao Greater Bay Area as an example to verify the system effect. In order to improve the accuracy of subsequent image alignment and defect detection, this paper compares the effects of the pixel-level edge detection algorithm and the subpixel-level edge detection algorithm and finally selects the subpixel-based edge detection algorithm to extract the edge of the image. In addition, this paper compares the traditional defect detection algorithm through research and experiment and proposes an improved image phase difference method according to the actual situation. The experimental research shows that the traditional arts and crafts intelligent analysis system based on digital technology proposed in this paper has a very good effect. At the same time, with the support of this system, the intelligent analysis of traditional arts and crafts in the Guangdong-Hong Kong-Macao Greater Bay Area can be carried out efficiently. [ABSTRACT FROM AUTHOR]