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Landslides cause severe economic damage and a large number of casualties every year around the world. In many cases, it is not possible to avoid them, and the task of engineers and geologists is to mitigate their effects using measures such as building diverting structures or preparing escape roads to be used when an alarm is triggered. It is necessary, therefore, to predict the path of the landslide, its depth and velocity, and the runout. These objectives are usually are attained by using mathematical, numerical and rheological models. An important limitation of the analysis is the lack of data, specially when few laboratory tests are available, and in cases where their present important variations. This leads to performing sensitivity analyses in which analysts study the influence of several magnitudes of interest, such as friction angle, porosity, basal pore pressure and geometry of the sliding mass, just to mention a few, leading in turn to perform a large number of simulations. We propose in this paper a methodology to speed up the process, which is based on: (i) using depth integrated models, which provide a good combination of accuracy and computer effort and (ii) using artificial intelligence tools to reduce the number of simulations. Let us consider a case where we have N mag main variables to explore; for each of them we select a number of cases, which can differ from one magnitude to another. The number of cases will be where N cases (i) is the number of cases we have selected for magnitude i. We can consider these variables as nodes belonging to a hypercube of dimension N mag . We will refer from now on as "hypercube" to the set of all cases generated in this way. The paper presents two cases where these techniques will be applied: (i) a 1D dam break problem and (ii) a case of a real debris flow which happened in Hong Kong, for which there is available information. [ABSTRACT FROM AUTHOR]

An appropriate urban density is a vital part of a sustainable urban fabric. However, when it comes to measuring the built urban fabric and how people walk through it and use, a difficulty has been observed in defining applicable measurement tools. With the intention of identifying the variables that will allow the best characterization of this fabric and movement, a multi-variable analysis methodology from the field of artificial intelligence (AI) is proposed. The main objective of this paper is to prove the capacity of AI as an evolving research method in urban morphology and specifically to evaluate the capacity of such a methodology to measure the way in which people travel through defined multi-levels of typologies of public urban space. The research uses the case of Hong Kong as a dense city that is three-dimensionally activated in terms of its public realm, not just at street level, but also via below ground subways and upper-level walkways, public and quasipublic spaces. This includes the three-dimensional volumetric assessment of public and quasi-public space typologies within a highly dense city. For the purpose of the study, a characterization and term definition of these spaces has been further developed: "Junctions", "Landmarks", "Intersections", "Districts", "Passages" and "Lobbies" (both outdoor and indoor) based on Lynch's 5 main key elements (District, landmark, path, edges, node). The results obtained using AI prove to be more robust and rational than those based on a more limited range of tools, evidencing that using AI can offer operational opportunities for better understanding of morphological and typological evolution within the vertical and volumetric built urban fabric. [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]

Technology in the hospitality industry is becoming increasingly important nowadays, because it has a close relationship with guest satisfaction and behavioral intention. This research aims to investigate hotel guests' preferences of in-room technology amenities by different demographic groups, and to find out how technology amenities including robots, artificial intelligence, and service automation, affect hotel guests' satisfaction, which in turn influence future behavioral intention to choose hotel and destination. This study uses a mixed quantitative and qualitative approach. Data are analyzed using SPSS and NVIVO. Results show that hotel guests' preferences of technology amenities vary significantly by their education levels and will influence guests' satisfaction and behavioral intention with regard to both hotel selection and destination selection. This paper appears to be the first attempt to investigate the influence of hotel guests' technology preferences on their behavioral intention in both the hotel and the tourism destination context. With the expectation to help the hotel industry, managerial implications and suggestions on technology adoption were provided on the basis of data analysis. [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]

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]

In the era of digitalisation, e-commerce retail sites have become decisive channels for reaching millions of potential customers worldwide. Digital marketing strategies are formulated by the marketing teams in order to increase the traffic on their e-commerce sites, thereby boosting the sales of the products. With the massive amount of data available from the cloud, which were conventionally made with a high degree of intuition based on decision makers' knowledge and experience, can now be supported with the application of artificial intelligence techniques. This paper introduces a novel approach in applying the fuzzy association rule mining approach and the fuzzy logic technique, for discovering the factors influencing the pricing decision of products launched in e-commerce retail site, and in formulating flexible, dynamic pricing strategies for each product launched in an e-commerce site. A pricing decision support system for B2B e-commerce retail businesses, namely Smart-Quo, is developed and implemented in a Hong Kong-based B2B e-commerce retail company. A six-month pilot run reveals a significant improvement in terms of the efficiency and effectiveness in making pricing decisions on each product. The case study demonstrates the feasibility and potential benefits of applying artificial intelligence techniques in marketing management in today's digital age. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy.Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy.Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes.What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions.Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy.Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy.Practitioners are able to use the AILQ to assess students' AI literacy development. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by investigating how children were engaged in AI literacy activities that are supported by intelligent agents. These activities were implemented in a Hong Kong kindergarten with a class of six 5‐year‐olds (Mage = 62.83 months, SD = 2.91) over a 6‐week period. Methods: The study gathered data from multiple sources, including classroom observations, teacher interviews, and documents/artefacts. Results and Conclusions: The results showed that children could learn about AI through interaction with intelligent agents in embodied learning contexts. The findings of this study have implications for the broader field of digital technology education, particularly in the context of early childhood education. Lay Description: What is already known about this topic: Artificial Intelligence (AI) literacy is essential in today's world.Little research exists on engaging young children in learning AI literacy. What this paper adds: Technology‐enhanced embodied learning approach affords multimodality in enabling young children's learning with AI‐related agents.Project‐based learning and real‐life examples can enhance children's learning. Implications for practice and/or policy: Early exposure to AI can help children navigate the increasingly digital world.Practical approaches, such as embodied and multimodal learning, seem to be effective.Teachers can enhance children's digital learning by using pedagogical strategies like project‐based learning and real‐life examples. [ABSTRACT FROM AUTHOR]

Rock classification provides vital information to geosciences and geological engineering practices. Reaping the benefits of the advances of computer vision-based deep learning artificial intelligence (AI) technology, this study aims to develop a next-generation convolutional neural network (CNN) to perform automatic rock classification. Two major challenging issues have been particularly addressed. First, most of the previous rock classifications are simply transfer learning of CNNs that are trained by life-like scenarios. Second, classifying rock types with similar textures leads to severe overfitting of CNNs. In this study, a novel CNN called HKUDES_Net is proposed and implemented to classify seven common Hong Kong rock types, namely fine-grained granite, medium-grained granite, coarse-grained granite, coarse ash tuff, fine ash tuff, feldsparphyric rhyolite, and granodiorite. With the aid of dynamic expansion, squeeze and excitation, and other strategies, HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. As compared with the other ten landmark CNNs and seven feature-based algorithms, HKUDES_Net has the best performance in precision (90.9%), recall (90.4%), and f1-score (90.5%). By implementing the alerting level, which restricts the training loss hovering above a small constant and prevents the validation loss from rising, the overfitting has been efficiently eliminated. The proposal and implementation of HKUDES_Net highlight the value of interdisciplinary research and will continuously pave the way for better coupling AI and geosciences. Highlights: A next-generation convolutional neural network (CNN) called HKUDES_Net is developed to perform automatic rock classification. HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. By implementing the alerting level in HKUDES_Net, the overfitting has been efficiently eliminated. Plain Language Summary: Rock classification provides vital information to geosciences and geological engineering practices. However, the traditional way of rock classification is to a certain degree experience-based. Computer vision-based artificial intelligence (AI), especially convolutional neural network (CNN), a sub-branch of AI, may help people automatically classify rock types with the hope to train AI models (CNNs), which can instantly and precisely perceive rock images similar to how human beings observe rocks. In other words, by putting a rock image into a CNN, the CNN will read the information from the image and tell people what rock type in the image is. This study develops and implements a next-generation CNN called HKUDES_Net to classify seven different rock types. Adopting different computational strategies, HKUDES_Net can classify rock types of different grain sizes with similar texture patterns/colors, which is challenging for other CNNs. As compared with the other landmark CNNs and machine learning algorithms, HKUDES_Net has much a better prediction accuracy performance. The proposal and implementation of HKUDES_Net exemplify an interdisciplinary research study, and we are confident that this paper will be instrumental to pave the way for further coupling AI and geosciences. [ABSTRACT FROM AUTHOR]

Intelligently predicting the frequency and volume of natural hazards, including boulder falls, attracts widespread attention in earth science and engineering communities. Taking Hong Kong, China, which is a highly developed and densely populated city, as a research example, the present paper statistically studies the occurrence of boulder falls based on power law relationships and supervised machine learning. The 2002–2016 boulder fall inventory of Hong Kong, which consists of 194 boulder falls, is compiled and analyzed in detail, including volume and spatial distributions. Based on the findings, a power law relationship with the b-value greater than one is particularly proposed for Hong Kong, which then allows major boulder falls (volume ≥1 m3) occurring in two subsequent years to be accurately predicted. In order to predict volume ranges of potential boulder falls, eight supervised machine learning algorithms are trained and validated based on sixty-five filtered samples with five represented features. Two key conclusions are drawn from the findings. First, the geological settings, altitudes, and gradients of source locations are demonstrated to be three triggering factors of different volume ranges of boulder falls. Second, the logistic regression algorithm has the best performance in predicting boulder fall volumes among all the algorithms. The f1-score of 5-fold cross-validation is 72.9%, and the prediction accuracy of the test dataset is 75.8%. We are confident that the methodologies and results in this paper will pave the way for analyzing other types of natural terrain hazards in addition to boulder falls in the long run. [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]

Background: The number of artificial intelligence (AI) literacy studies in K‐12 education has recently increased, with most research focusing on primary and secondary education contexts. Little research focuses on AI literacy programs in early childhood education. Objectives: The aim of this mixed‐methods study is to examine the feasibility of an AI literacy program called "AI4KG" and explore how it might affect kindergarteners' perceptions of robots and attitudes towards engineering and science. Methods: A total of 26 child–parent dyads recruited from a Hong Kong kindergarten were involved in this study, consisting of 26 children (Mage = 4 years, SD = 0.28) and their parents. Quantitative and qualitative data were collected through surveys and interviews designed to explore children's perceptions of robots and attitudes towards engineering and science, and parents' perceptions of the AI4KG intervention. Results: It is found that children have increased their perceptions of robots after the AI literacy program, but the AI4KG curriculum had no significant effects on kindergarten children's engineering and science attitudes. Most parents (22 out of 26) agreed that their children's AI knowledge, AI skills, and AI attitudes have been enhanced after learning through the AI4KG curriculum. Conclusions: This study suggests that the AI4KG curriculum is potentially effective in promoting early AI literacy and favourable attitudes towards the technology, but further research is needed to develop age‐appropriate measures and assess its long‐term impact on children's education and career paths. Lay Description: What is currently known about this topic?: AI literacy is becoming important as AI transforms society, but few studies have explored AI literacy in early childhood education.Prior research found AI activities can positively influence older children's views of robots and engineering. What does this paper add?: It evaluates an 8‐week AI literacy curriculum called AI4KG for kindergarteners in Hong Kong.The curriculum used tools like Teachable Machine and taught concepts through play and storytelling.It improved children's perceptions of robots but did not significantly impact engineering/science attitudes.Parents perceived improvements in children's AI knowledge, skills, and attitudes after the curriculum. Implications for practice/policy: The curriculum provides an example for developing age‐appropriate AI literacy activities.More rigorous research on impacts over time is needed, but results suggest potential benefits of AI literacy in early childhood.Policymakers could consider incorporating AI literacy into kindergarten curricula to prepare children for the future. [ABSTRACT FROM AUTHOR]

The dissemination of fake news on social media platforms is an issue of considerable interest, as it can be used to misinform people or lead them astray, which is particularly concerning when it comes to political events. The recent event of Hong Kong protests triggered an outburst of fake news posts that were identified on Twitter, which were then promptly removed and compiled into datasets to promote research. These datasets focusing on linguistic content were used in previous work to classify between tweets spreading fake and real news using traditional machine learning algorithms (Zervopoulos et al., in: IFIP international conference on artificial intelligence applications and innovations, Springer, Berlin, 2020). In this paper, the experimentation process on the previously constructed dataset is extended using deep learning algorithms along with a diverse set of input features, ranging from raw text to handcrafted features. Experiments showed that the deep learning algorithms outperformed the traditional approaches, reaching scores as high as 99.3% F1 Score, with the multilingual state-of-the-art model XLM-RoBERTa outperforming other algorithms using raw untranslated text. The combination of both traditional and deep learning algorithms allows for increased performance through the latter, while also gaining insight regarding tweet structure from the interpretability of the former. [ABSTRACT FROM AUTHOR]

Tourism demand forecasting is a critical process in the planning of tourism utilities. In recent years, Internet search indices have been popularly used as tourism demand indicators. However, due to the complex relationship between tourism demand and the search index and the vast amounts of search engine data, the traditional econometric and artificial intelligence models could not be enough to complete the prediction task. Under this scenario, this paper proposes a novel two-step method to improve tourism demand prediction accuracy. Firstly, a double-boosting algorithm is proposed to select the keywords and their lags from the potential relevant high-dimensional search queries. Second, the ensemble Support Vector Regression (SVR) based Deep belief Network (DBN) approach is adopted to capture the possible non-linear relationship and to improve the forecasting performance through deep learning combination. The empirical results demonstrate that this procedure significantly outperforms other benchmark models when forecasting monthly Hong Kong tourist arrivals. [ABSTRACT FROM AUTHOR]

This paper uses a new measurement of AI's intelligence related to task performance to examine how expectations about the operating efficiency of an AI technology influence the intention to adopt it. We suggest four levels of user acceptance/rejection of AI services, including the level of compensatory acceptance. Our conceptual model is specifically designed for the AI context, with two key variables: cybersecurity and anthropomorphism, and three mediating constructs: i) perceived level of AI's intelligence, ii) perceived performance expectancy, and iii) perceived effort expectancy. The hypotheses were tested by surveying 494 potential virtual banking users in Hong Kong and analyzing the data with Structural Equation Modelling (SEM). We find that consumer acceptance of AI services is positively related to perceived performance expectancy and effort expectancy and to the perceived level of AI's intelligence. These findings support an extended behavioral intention: the compensatory level of AI acceptance. Our empirically tested and generalizable results have implications for academics and practitioners. [ABSTRACT FROM AUTHOR]

The editorial introduces issue 21.1 of Technoetic Arts via a critical reflection on the artificial intelligence hype (AI hype) that emerged in 2022 and gives an overview of each of the issue's ten articles. The first four articles engage with new technologies from different positions in art and architecture. These articles include an exploration of the indexical function of images as a means for revealing cultural premises governing public space during the pandemic in Hong Kong, a neuroarchitectural perspective on immersive architectural environments, and an investigation of the link between the popularity of immersive art installations and the emotions these installations generate. The 'Cryptoart' section features an ethnographic study of the DADA digital art collective, shedding light on the role of non-fungible tokens (NFTs) in community building. Guest-edited by Professor Tanu Gupta, the Special Section 'Perspectives from Chandigarh' constitutes the second part of the journal issue. Based on contributions to the national conference on Contemporary Perspectives in English Language, Literature, and Cultural Studies held on 15–16 July 2022, at Chandigarh University in Punjab, India, the six articles reflect a search for meaningful existence within colonial, patriarchal and biopolitical structures that frame everyday practices of exclusion and oppression. Discussing works of literature and cinema from the European, Japanese and the US American canon, the articles contribute a distinctive perspective to the critical analysis of contemporary societies that are shaped by the idea that progress is technological invention. [ABSTRACT FROM AUTHOR]

• This paper focuses on daily natural gas consumption forecasting. • A novel hybrid model is developed for daily natural gas consumption forecasting. • The most recent datasets of four cities in three climate zones are collected. • An improved singular spectrum analysis is first proposed. In daily natural gas consumption forecasting, the accuracy of forecasting models is vulnerably affected by the noise data in historical time series. Singular spectrum analysis (SSA) is often introduced into hybrid models for denoising. However, as a deterministic-based algorithm, SSA does not give good results when a time series is contaminated with a high noise level. Considering this fact, this paper proposes an improved SSA (ISSA) that modifies the determination method of subseries selection in the reconstruction stage of SSA. Combining ISSA with long short-term memory (LSTM), a novel hybrid model, ISSA-LSTM, is thus developed. Additionally, for validating the robustness and superiority of ISSA-LSTM, the historical datasets of four representative cities located in three climate zones are collected as the training and testing datasets, and a comparison of ISSA-LSTM with five advanced models is performed. The results reveal that SSA would generate negative values when time series close to zero and the contribution of SSA in improving the forecasting accuracy of LSTM is insignificant. In contrast, ISSA avoids generating negative values and reduces the mean absolute range normalized error (MARNE) of LSTM by a range of 0.86–11.86%. Among the models, ISSA-LSTM achieves the best performance and its MARNEs for London (temperate zone), Melbourne (subtropical zone), Karditsa (subtropical zone), and Hong Kong (tropical zone) are 4.68%, 5.72%, 5.76%, and 14.10%, respectively. The MARNE of the tropical city is higher than that of others, which is caused by the complex natural gas consumption pattern of itself. [ABSTRACT FROM AUTHOR]

• Examine the use of metaverse for transportation and logistics operations. • Establish an AI supported digital technological framework. • Propose and discuss future research areas. In the digital age, supported by disruptive technologies such as artificial intelligence (AI), blockchain (BC), digital twins (DT), and extended reality (ER), the use of metaverse for real-world operations has emerged. In this paper, we examine the use of metaverse for transportation and logistics operations. To be specific, we first examine the related literature by a selective critical review. Then, we discuss how the use of metaverse can facilitate transportation operations with some relevant cases such as the metaverse project of MTR (from Hong Kong). We establish an AI supported digital technological framework, called the ABCDE framework, for transportation and logistics companies to adopt with their use of metaverse. Finally, we propose and discuss several important future research areas. To the best of our knowledge, this is the first study which comprehensively examines the use of metaverse for transportation and logistics operations. The theoretical framework also lays the foundation for future studies. The findings and insights provide constructive and helpful guidance to both practitioners and academics on the future development of the topic. [ABSTRACT FROM AUTHOR]

Spatial extension of lands through rapid reclamation is attractive for congested coastal megacities, although reclamations might suffer from project delay and budget overrun, often due to encountering unforeseen ground conditions. To accelerate reclamations, accurate prediction of soil consolidation over a construction duration of multiple years is needed for reclaimed lands, which often contain spatially varying subsurface stratigraphy and soil parameters. This calls for a spatio-temporal analysis of consolidation with a sound understanding of subsurface stratigraphic alternations of fine/coarse-grained soils and spatial variability of consolidation parameters (e.g. permeability). In this study, a unified framework, capable of simultaneously modelling stratigraphic variation and spatial variability of soil properties through machine learning of limited site investigation data, is combined with the finite-element method and Monte Carlo simulation for spatio-temporal consolidation analysis of reclaimed lands. The proposed method is applied to a real reclamation project in Hong Kong. Results indicate that the proposed method can accurately characterise subsurface geological cross-sections and spatially varying soil permeability with quantified uncertainty. Ignorance of spatial variability of soil permeability may result in an underestimation of consolidation time and an overestimation of undrained shear strength gain, and thus pose significant risks to reclamation projects. [ABSTRACT FROM AUTHOR]

• Big data and artificial intelligence for air pollution monitoring and health management • Personalized and synchronized data for personalized advice and alert • Smart interventions for effecting behavioral change • Causal relationship between PM 1.0,2.5 vs FEV 1 and FVC All people in the world are entitled to enjoy a clean environment and a good quality of life. With big data and artificial intelligence technologies, it is possible to estimate personalized air pollution exposure and synchronize it with activity, health, quality of life and behavioural data, and provide real-time, personalized and interactive alert and advice to improve the health and well-being of individual citizens. In this paper, we propose an overarching framework outlining five major challenges to personalized air pollution monitoring and health management, and respective methodologies in an integrated interdisciplinary manner. First, urban air quality data is sparse, rendering it difficult to provide timely personalized alert and advice. Second, collected data, especially those involving human inputs such as health perception, are often missing and erroneous. Third, the data collected are heterogeneous, and highly complex, not easily comprehensible to facilitate individual and collective decision-making. Fourth, the causal relationships between personal air pollutants exposure (specifically, PM 2.5 and PM 1.0 and NO 2) and personal health conditions, and health-related quality of life perception, of young asthmatics and young healthy citizens in Hong Kong (HK), are yet to be established. Fifth, whether personalized and smart information and advice provided can induce behavioural change and improve health and quality of life are yet to be determined. To overcome these challenges, our first novelty is to develop an AI and big data framework to estimate and forecast air quality in high temporal-spatial resolution and real-time. Our second novelty includes the deployment of mobile pollution sensor platforms to substantially improve the accuracy of estimated and forecasted air quality data, and the collection of activity, health condition and perception data. Our third novelty is the development of visualization tools and comprehensible indexes, by correlating personal exposure with four types of personal data, to provide timely, personalized pollution, health and travel alerts and advice. Our fourth novelty is determining causal relationship, if any, between personal pollutants, PM 1.0 and PM 2.5 , NO 2 exposure and personal health condition, and personal health perception, based on a clinical experiment of 150 young asthmatics and 150 young healthy citizens in HK. Our fifth novelty is an intervention study to determine if smart information, presented via our proposed visualized platform, will induce personal behavioural change. Our novel big data AI-driven approach, when integrated with other analytical approaches, provides an integrated interdisciplinary framework for personalized air pollution monitoring and health management, easily transferrable to and applicable in other domains and countries. [ABSTRACT FROM AUTHOR]

Emerging memories (EMs) have been intensively explored for embedded non-volatile memory (eNVM) applications because of their advantages over conventional eNVMs. There has been limited production of microcontrollers with embedded EM (eEMs) but the eEMs market has not taken off yet. The disruptive innovation undertaken by advanced digital CMOS technologies now provides a compelling motivation for a radical change, giving eEMs a great opportunity for success. Phase change memory (PCM) has been proven, in stand-alone memories, to be the most mature among the various kinds of EM. This paper illustrates the enormous potential of PCM in the field of eNVM. The evolution of embedded PCM (ePCM) is reviewed, showing evidence for the effort spent to enlarge the operating temperature range to cover an automotive mission profile. The recently disclosed state-of-the-art 28 nm HK metal gate fully depleted silicon-on insulator ePCM is presented as the demonstration of the ePCM potential to become the mainstream eNVM technology at 28 nm and below, at least for automotive grade applications. Finally, the opportunity for ePCM to play a relevant role in the field of edge artificial intelligence is briefly discussed. [ABSTRACT FROM AUTHOR]

In light of advancements in big data analysis and artificial intelligence (AI), there are emerging opportunities to harness these technologies to address perceptive biases. This study examines the potential perceptive biases that may arise when construction mediation is quasi-imposed on the disputing parties. This can happen when mediation attempts are stipulated in the construction contract or court-directed. It is argued that, under such circumstances, a negative perception might arise over whether a bona fide mediation can be realised. Concerns include the fairness and timeliness of the process, as well as the practice of opportunistic mediating behaviours. With data collected from practising construction mediation practitioners in Hong Kong, the constructs of Perceptions of Bona Fide Mediation, Quasi-Imposition, and Positive Mediation Outcomes were first developed. Applying partial least square structural equation modelling to the relationship frameworks of the constructs, it was found that quasi-imposition is not as damaging as envisaged as far as having a bona fide mediation and attaining positive mediation outcomes are concerned. Moreover, a negative perception of the fairness and timeliness of the quasi-imposed mediation would jeopardise the integrity of a bona fide mediation. In this regard, utilizing NLP and machine learning algorithms offers a pioneering AI-driven approach to informing mediating parties, as well as reminding mediators to uphold the fairness and timeliness of the process for the purposes of reaching positive mediation outcomes. [ABSTRACT FROM AUTHOR]

The recent advances in artificial intelligence, coupled with the popularity of personal computers, have led to its widespread application in different domain problems. This paper describes the development of a prototype knowledge-based expert system - an advanced technique of artificial intelligence - as a tool of knowledge transfer and training for personnel in water resources planning and management. Details and a description of the application of the knowledge-based system on design of river network in Hong Kong are also presented. [ABSTRACT FROM AUTHOR]

Rain-induced man-made slope failures pose great threats to public safety as most man-made slopes are formed in densely populated areas. A critical step in managing landslide risks is to predict the time, locations and consequences of slope failures in future rainstorms. Based on comprehensive databases of in-service man-made slopes, rainstorms and landslides in Hong Kong during the past 35 years, a spatio-temporal landslide forecasting model for man-made slopes is developed in this study within a unified machine learning framework. With a storm-based data integration strategy and multiclass classification on landslide scales, the framework incorporates landslide time and consequences in landslide susceptibility mapping to successfully achieve spatio-temporal landslide forecasting. The machine learning-based landslide forecasting model is validated against historical landslide incidents both temporally and spatially and through a case study of the June 2008 storm; the model significantly outperforms the prevailing statistical rainfall–landslide correlations in terms of prediction accuracy. The model can predict the real-time evolution of probabilities, scales and spatial distribution of landslides during the progression of a rainstorm, which can never be achieved by statistical methods. It can serve as an essential module for state-of-the-art landslide risk assessment and early warning. [ABSTRACT FROM AUTHOR]

As occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private market occurred to meet the need for a more coordinated occupational rehabilitation practice. However, there is no clear service standard in private occupational rehabilitation services nor concrete suggestions on how to offer rehabilitation plans to injured workers. Electronic Health Records (EHRs) data can provide a foundation for developing a model to improve this situation. This project aims at using a machine-learning-based approach to enhance the traditional prediction of disability duration and rehabilitation plans for work-related injury and illness. To help patients and therapists to understand the machine learning result, we also developed an interactive dashboard to visualize machine learning results. The outcome is promising. Using the variational autoencoder, our system performed better in predicting disability duration. We have around 30% improvement compared with the human prediction error. We also proposed further development to construct a better system to manage the work injury case. [ABSTRACT FROM AUTHOR]

We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values of these features is studied against discharge status and disease severity as a preliminary step to identify those features with a more pronounced effect on the patient outcome. Once identified, they constitute the inputs of four machine learning models, Decision Tree, Random Forest, Gradient and RUSBoosting, which predict both the Mortality and Severity associated with the disease. We test the accuracy of the models when the number of input features is varied, demonstrating their stability; i.e., the models are already highly predictive when run over a core set of (6) features. We show that Random Forest and Gradient Boosting classifiers are highly accurate in predicting patients' Mortality (average accuracy ∼99%) as well as categorize patients (average accuracy ∼91%) into four distinct risk classes (Severity of COVID-19 infection). Our methodical and broad approach combines statistical insights with various machine learning models, which paves the way forward in the AI-assisted triage and prognosis of COVID-19 cases, which is potentially generalizable to other seasonal flus. [ABSTRACT FROM AUTHOR]

In the context of aggravating environmental problems, the concept of green economy, advocated by policies introduced to solve such problems, is being increasingly recognized and emphasized. In addition, digital technologies, represented by the Internet, big data, and artificial intelligence, are constantly integrating with economic and social fields, gradually becoming an important driving force for economic and social development, and also bringing certain positive environmental effects. This study investigated the spatiotemporal evolution characteristics of the green economy of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in the past three decades (1987-2018) under the background of administrative division adjustment and artificial intelligence development. First, the overall green economic development level of the GBA from 1987 to 2018 was analyzed using the superefficiency slack-based measure model. Then, the spatial pattern evolution characteristics and regional differences of green economy in the GBA in this period were assessed by the exploratory spatial data analysis model. According to the results of this study, (1) the green economic development levels of Guangzhou, Shenzhen, Foshan, Zhaoqing, and Hong Kong in the GBA always followed an increasing trend from 1987 to 2018. To be specific, Foshan, Zhongshan, Hong Kong, and Macao ranked among the top areas in the GBA in terms of average green economic development level; moreover, a strong positive correlation was found between economic strength and green economic development. (2) The overall green economic development level of the GBA roughly followed a trend of first decreasing and then increasing, fluctuating around 0.45 on average. (3) Clearly, the green economy of various cities in the GBA showed certain correlations in 1987-2005, but with great fluctuations. However, no spatial correlation was observed after 2006. On the whole, in the GBA, the spatial agglomeration of green economy was weak. (4) There was a general lack of obvious spatial agglomeration in the overall green economic development of the GBA, especially in western Guangdong. In contrast, spatial agglomeration was obvious in eastern Guangdong. [ABSTRACT FROM AUTHOR]

In July 2019, Hong Kong issued a comprehensive regulation on robo-advisors in the capital markets, with a view to striking a balance between investor protection and innovation promotion. This regulation has generated some noticeable positive impact on the market, but some problems remain to be addressed. It is critically assessed by way of a comparison with the laws in the US, the UK, Singapore, and Mainland China. It has strengths in terms of regulatory form and approach, the comprehensiveness of guidance provided and clear allocation of responsibility. There are also several weaknesses and challenges. Firstly, greater efforts need to be made to ensure proper compliance and enforcement. Secondly, more attention should be paid to the increasing complexity of algorithms. Thirdly, the issue of outsourcing arrangements requires further thoughts. Finally, Hong Kong should consider a staged reform on its financial regulatory architecture to address the issue of regulatory arbitrage. [ABSTRACT FROM AUTHOR]

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created the first pretertiary AI curriculum at the secondary school level for Hong Kong and evaluated its efficacy. This study added to the AI education community by introducing a new AI curriculum framework. The preposttest multifactors evaluation about students’ perceptions of AI learning confirmed that the curriculum is effective in promoting AI learning. The teachers also confirmed the co-creation process enhanced their capacity to implement AI education. Background: AI4Future is a cross-sector project that engages five major partners—CUHK’s Faculty of Engineering and Faculty of Education, secondary schools, Hong Kong government, and AI industry. A team of 14 professors collaborated with 17 principals and teachers from six secondary schools to co-create the curriculum. Research Questions: Would the curriculum significantly improve the student perceived competence, attitude, and motivation toward AI learning? How does the co-creation process benefit the implementation of the curriculum? Methodology: The participants were 335 students and eight teachers from the secondary schools. This study adopted a mix-method with quantitative data measures at pre- and post-questionnaires and qualitative data emphasizes teachers’ perspectives on the co-creation process. Paired ${t}$ -tests and ANCOVAs, and thematic analysis were used to analyze the data. Findings: 1) students perceived greater competence and developed a more positive attitude to learn AI and 2) the co-creation process enhanced teachers’ knowledge in AI, as well as fostered teachers’ autonomy in bringing the subject matter into their classrooms. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, there is an increasing demand for incorporating unstructured narratives in decision support for knowledge-intensive industries such as healthcare and social service organizations. However, most of the current research on decision support systems (DSS) mainly focused on dealing with structured data and are inadequate to dealing with unstructured narratives such as clients’ records and stories. This paper presents a narrative-based reasoning (NBR) algorithm which incorporates the technologies of knowledge-based system (KBS), computational linguistics, and artificial intelligence (AI) for automatic processing unstructured narratives and inferring useful knowledge for decision support. A NBR enabled DSS has been built and was evaluated through a series of experiments conducted in early intervention of mental health of a social service company in Hong Kong. The performance of NBR was measured based on recall and precision and encouraging results were obtained. High recall and precision are achieved in the reasoning of unstructured data, and high recall is achieved for the association analysis. The results show that it is possible for inferring recommendations for problem solving from unstructured narratives automatically. Based on the approach, it helps to support knowledge workers with reliable suggestions on decision making so as to increase the quality of their solutions. [ABSTRACT FROM AUTHOR]

This paper presents the coupling of the recent advancements in artificial intelligence (Al) technology with existing numerical models to constitute an integrated knowledge-based system (KBS) on flow and water quality. A hybrid application of the latest Al technologies, namely, KBS, artificial neural network and fuzzy inference system, in this specific problem domain is adopted here. This prototype system, serving as both a design aid as well as a training tool, enables hydraulic engineers and environmental engineers to become acquainted with up-to-date flow and water quality simulation tools, and fill the existing gaps between researchers and practitioners in the application of recent technology in solving real prototype problems in Hong Kong. Moreover, the system can meet the demand for an integrated system that can quickly assist policy makers in reaching decisions and furnish convenient and open information service on water quality for the general public. [ABSTRACT FROM AUTHOR]

Computers which are used conventionally in numerical models for problem-solving and fast number-crunching are not user-friendly and lack knowledge transfer in model interpretation. Recent advances in artificial intelligence make it possible to systemise the experience and decision-making procedures of human experts. This paper describes the development of a prototype knowledge-based system (an advanced technique of artificial intelligence) as a knowledge-transfer tool for water-resource planning and management in coastal waters. The system can (a) run on a desktop computer and (b) act as a design aid, to assist in numerical modelling to simulate flow and/or water quality. Using the application in Hong Kong coastal waters, it was verified that the prototype system had the capability to undertake the reasoning process to select the model and associated parameters, equivalent to a human expert. [ABSTRACT FROM AUTHOR]

The public specialty ophthalmic clinics in Hong Kong, under the Hospital Authority, receive tens of thousands of referrals each year. Triaging these referrals incurs a significant workload for practitioners and the other clinical duties. It is well-established that Hong Kong is currently facing a shortage of healthcare workers. Thus a more efficient system in triaging will not only free up resources for better use but also improve the satisfaction of both practitioners and patients. Machine learning (ML) has been shown to improve the efficiency of various medical workflows, including triaging, by both reducing the workload and increasing accuracy in some cases. Despite a myriad of studies on medical artificial intelligence, there is no specific framework for a triaging algorithm in ophthalmology clinics. This study proposes a general framework for developing, deploying and evaluating an ML-based triaging algorithm in a clinical setting. Through literature review, this study identifies good practices in various facets of developing such a network and protocols for maintenance and evaluation of the impact concerning clinical utility and external validity out of the laboratory. We hope this framework, albeit not exhaustive, can act as a foundation to accelerate future pilot studies and deployments. [ABSTRACT FROM AUTHOR]

The integration of customer relationship management (CRM) and supplier relationship management (SRM) to facilitate supply chain management in the areas of supplier selection using a help desk approach has become a promising solution for manufacturers to identify appropriate suppliers and trading partners to form a supply network on which they depend for products, services, and distribution. In this paper, an intelligent customer–supplier relationship management system (ISRMS) using the case based reasoning (CBR) technique to select potential suppliers is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order is greatly reduced. [Copyright &y& Elsevier]

Landslide susceptibility assessment is essential for regional landslide risk assessment and mitigation. Most past studies involved cell-based analysis that takes landslide incidents as geo-spatial points. Nevertheless, given that a landslide is a two-dimensional polygon on maps and a three-dimensional object in the real world, an object-wise assessment is more logical. Fusing with artificial intelligence (AI) techniques, this paper proposes a novel AI-powered object-based landslide susceptibility assessment method to address this issue. First, landslide and non-landslide objects are defined based on an optimal object size determined by statistics of historical landslides. Next, landslide and non-landslide samples are constructed by integrating geoenvironmental data layers derived from multi-source data. Subsequently, AI techniques are applied to learn susceptibility prediction based on the prepared samples. To illustrate the proposed method, a comprehensive case study of Hong Kong is conducted, in which six AI algorithms are evaluated including logistic regression (area under curve, AUC = 0.949), random forest (AUC = 0.951), LogitBoost (AUC = 0.958), convolutional neural network (CNN) (AUC = 0.966), bidirectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN) (AUC = 0.966), and CNN-LSTM (AUC = 0.972), among which the BiLSTM-RNN and CNN-LSTM algorithms are applied in landslide susceptibility assessment for the first time. Results confirm that the proposed object-based method outperforms the traditional cell-based method significantly. Equally importantly, the case study produced the first set of AI-based territory-wide landslide susceptibility maps for Hong Kong. These maps can be used as a fundamental tool for quantifying natural terrain landslide risk and identifying susceptible zones where landslide mitigation measures may be needed. • A novel artificial intelligence and object-based landslide susceptibility method is proposed and validated. • The first set of territory-wide landslide susceptibility maps for Hong Kong is produced based on AI techniques. • CNN-LSTM and BiLSTM-RNN are successfully applied to landslide susceptibility analysis for the first time. • The proposed AI-powered object-based method outperforms the cell-based method significantly. [ABSTRACT FROM AUTHOR]

• A pick-to-sort order batching approach is proposed as a system-based solution. • The solution facilitates order handling under both FTWB and VTWB settings. • The solution is a benchmark order fulfilment application with customizable options. • Case study demonstrates system generalizability, applicability and customizability. • Results indicate a significant improvement in terms of order throughput rate. The emergence of e-commerce marketplace, especially the business-to-business (B2B) e-commerce sector, has created a vast market opportunity for retailers and logistics service providers (LSPs). However, facing the structural changes in B2B logistics orders, without any transformation of logistics operating process in distribution centres, LSPs are experiencing serious challenges in handling B2B e-commerce orders. In order for LSPs to grasp the market pie of the logistics and distribution sector of the e-commerce business through improving the core capability of handling B2B e-commerce order in their distribution centres, this paper proposes a novel approach for LSPs to systematically execute B2B order pre-processing so that the B2B e-commerce orders are better managed. An intelligent B2B order handling system (IOHS) is developed for efficient management of discrete, frequently arrived B2B e-commerce orders. By applying the "warehouse postponement" concept through the development of IOHS integrating cloud database management, fuzzy logic, and genetic algorithm approach, B2B order grouping solutions are effectively generated. A case study is conducted in a Hong Kong-based logistics service provider who handles outsourced B2B e-commerce orders, indicating the feasibility of the proposed solution to facilitate e-order fulfilment under both fixed and variable time-window batching settings in distribution centres. A significant improvement in terms of the throughput rate of handling B2B e-commerce order in the distribution centre is found, thereby increasing the satisfaction of buyers and sellers throughout the order handling and delivery process, as well as facilitating LSPs' capability to manage a larger number of orders received from B2B e-commerce platform, given the greater product exposure of sellers in today's B2B e-business. [ABSTRACT FROM AUTHOR]

The concept of smart hospital is not new, but its development has unprecedentedly been accelerated by the COVID-19 pandemic. Throughput and performance dashboard on ED operation running in real-time manner would facilitate ED managers to maintain smooth ED operation or even predict and anticipate the service surge. The ED health big data would also have potential impact on the development of geriatric and ambulatory emergency medicine to accurately identify high-acuity patients and stratify low-risk patients for ambulatory care. [Extracted from the article]

This article investigated the learning experiences of mainland Chinese students in master's degree programmes in Hong Kong. Using a qualitative research approach, 20 mainland Chinese students in master's degree programmes in a first-tier Hong Kong university were interviewed. It observed, first, that mainland Chinese students appreciate the teaching and learning approaches adopted in Hong Kong, but that they take some time to adapt. Thanks to heavy financial support from parents and parental expectations, they are pressed to adapt as soon as possible and get high marks in the one-year programmes in Hong Kong. Second, the mainland Chinese students' previous experiences of participating in campus activities in mainland China and their instrumental views of learning restrict their involvement in informal learning through campus activities. Third, their limited interaction with local and foreign students constrains the development of their intercultural competence. [ABSTRACT FROM AUTHOR]

Background: People with traumatic brain injury (TBI) often experience cognitive deficits in attention, memory, executive functioning and problem-solving. The purpose of the present research study was to examine the effectiveness of an artificial intelligent virtual reality (VR)-based vocational problem-solving skill training programme designed to enhance employment opportunities for people with TBI. Method: This was a prospective randomized controlled trial (RCT) comparing the effectiveness of the above programme with that of the conventional psycho-educational approach. Forty participants with mild ( n = 20) or moderate ( n = 20) brain injury were randomly assigned to each training programme. Comparisons of problem-solving skills were performed with the Wisconsin Card Sorting Test, the Tower of London Test and the Vocational Cognitive Rating Scale. Results: Improvement in selective memory processes and perception of memory function were found. Across-group comparison showed that the VR group performed more favourably than the therapist-led one in terms of objective and subjective outcome measures and better vocational outcomes. Conclusions: These results support the potential use of a VR-based approach in memory training in people with MCI. Further VR applications, limitations and future research are described. [ABSTRACT FROM AUTHOR]

Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the "same" ethnic group ("Seen AI"). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the "Unseen AI" paradigm where training and testing are from "different" ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between "Unseen AI" and "Seen AI". Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. "Unseen AI" (training: Japanese, testing: HK or vice versa) and "Seen AI" experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the "Unseen AI" pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for "Unseen AI" pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using "Seen AI", the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that "Unseen AI" was in close proximity (<10%) to "Seen AI", validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s. [ABSTRACT FROM AUTHOR]

In Hong Kong, buildings consume 90% of the electricity generated and over 60% of the city's carbon emissions are attributable to generating power for buildings. In 2018, Hong Kong residential sector consumed 41,965 TJ (26%) of total electricity generated, with private housing accounting for 52% and public housing taking in 26%, making them the two major contributors of greenhouse gas emissions. Furthermore, air conditioning was the major source consuming 38% of the electricity generated for the residential building segment. Strategizing building energy efficiency measures to reduce the cooling energy consumption of the residential building sector can thus have far-reaching benefits. This study proposes a hybrid simulation strategy that integrates artificial intelligence techniques with a building energy simulation tool (EnergyPlus™) to predict the annual cooling energy consumption of residential buildings in Hong Kong. The proposed method predicts long-term thermal energy demand (annual cooling energy consumption) based on short-term (hourly) simulated data. The hybrid simulation model can analyze the impacts of building materials, construction solutions, and indoor–outdoor temperature variations on the cooling energy consumed in apartments. The results indicate that using low thermal conductivity building materials for windows and external walls can reduce the annual cooling energy consumption by 8.19%, and decreasing the window-to-wall ratio from 80% to 40% can give annual cooling energy savings of up to 18%. Moreover, significant net annual cooling energy savings of 13.65% can be achieved by changing the indoor set-point temperature from 24 °C to 26 °C. The proposed model will serve as a reference for building energy efficiency practitioners to identify key relationships between building physical characteristics and operational strategies to minimize cooling energy demand at a minimal time in comparison to traditional energy estimation methods. [ABSTRACT FROM AUTHOR]

Time series forecasting technology and related applications for stock price forecasting are gradually receiving attention. These approaches can be a great help in making decisions based on historical information to predict possible future situations. This research aims at establishing forecasting models with deep learning technology for share price prediction in the logistics industry. The historical share price data of five logistics companies in Hong Kong were collected and trained with various time series forecasting algorithms. Based on the Mean Absolute Percentage Error (MAPE) results, we adopted Long Short-Term Memory (LSTM) as the methodology to further predict share price. The proposed LSTM model was trained with different hyperparameters and validated by the Root Mean Square Error (RMSE). In this study, we found various optimal parameters for the proposed LSTM model for six different logistics stocks in Hong Kong, and the best RMSE result was 0.43%. Finally, we can forecast economic recessions through the prediction of the stocks, using the LSTM model. [ABSTRACT FROM AUTHOR]