Publications & Projects

Authors:
Lam, H. Y., Ho, G. T. S., Mo, D. Y., & Wong, L.

Abstract:
As global demand for perishable goods rises, logistics providers face challenges in managing temperature-sensitive products. Traditional vehicle routing models often neglect cold chain requirements, including temperature control and strict delivery schedules. Additionally, sustainability concerns drive the need for eco-friendly logistics practices, as passive packaging methods contribute to waste. This study introduces a reversible cold chain vehicle routing model that integrates the Internet of Things and a multi-objective optimisation framework for real-time monitoring. Incorporating cold chain time windows, recycling constraints, and social sustainability, the model demonstrates how technology can enhance cold chain logistics and support circular economy goals.

»Cite this article: Lam, H. Y., Ho, G. T. S., Mo, D. Y., & Wong, L. (2025). Transforming cold chain logistics: a reversible vehicle routing approach for sustainable and efficient delivery of perishable goods. Enterprise Information Systems, 19(5-6), 2492737. 

»DOI: 10.1080/17517575.2025.2492737

Authors:
Lam, Y. M., & Lam, B. S. Y.

Abstract:
Climate lexical choices are crucial for effective communication and understanding in climate change discussions. Variations in these lexical choices may lead to misunderstandings among scientists, journalists, and policymakers. To reduce potential ambiguities in climate communications, this study aims to provide empirical evidence of the usage patterns of five lexical choices related to climate change, namely, “climate change,” “climate crisis,” “climate emergency,” “global warming,” and “global heating,” in three UK newspapers spanning from 2018-2025. Using Python, we calculate the co-occurrence frequency of the selected lexical choices and construct confidence intervals, then investigate, quantitatively and qualitatively, when and how the lexical choices were used. We found that despite the different political leanings, no clear lexical choice preferences were identified among the three newspapers. Contrary to common perceptions, we discovered an unrecognised common usage pattern in “climate change” and “global warming”―the two are not interchangeable in their applications in the UK newspaper context. In providing this timely empirical assessment of the usage patterns of lexical choices related to climate change, we invite journalists, scientists, and policymakers to reflect on whether the lexical choices, particularly “climate change” and “global warming”, should be used interchangeably in the future.

»Cite this article: Lam, Y. M., & Lam, B. S. Y. (2025). Does “climate change” equal “global warming”? A corpus-based study of lexical choices related to climate change in three UK newspapers from 2018 to 2025. Humanities and Social Sciences Communications, 12(1), 1852.

»DOI: 10.1057/s41599-025-06112-z

Authors:
Li, T., & Song, L.

Abstract:
Hierarchical federated learning (HFL) provides a new client-edge-cloud paradigm to reduce the network burden brought by centralized FL. However, the current HFL framework mainly adopts orthogonal multiple access (OMA) techniques for client-edge communication, failing to cope with massive connections with limited bandwidth resources. Besides, the highly skewed data distribution across the clients can undermine the convergence, which leads to more rounds to reach the target performance. In this paper, we consider a non-orthogonal multiple access (NOMA)-enabled HFL framework that provides a spectrum-efficient approach to enable massive client connectivity. Under this framework, we investigate the joint client-edge association and resource allocation problem to optimize the dual-efficiency. We first reformulate the original problem as a bi-objective optimization problem using dynamic weights to balance the resource and data heterogeneity. After that, we formulate the edge-client association problem as a many-to-one matching game with externalities and swap the connected pairs until a stable matching is obtained. For each temporary edge-client association, we solve the CPU frequency and transmission power control problem for all clients using convex optimization methods. We conduct experiments under various unbalanced data distributions and communication environment settings. The results demonstrate that our method outperforms other benchmarks regarding client scheduling, transmission and resource allocation.

»Cite this article: Li, T., & Song, L. (2025). Client Association and Resource Allocation for NOMA-Enabled Hierarchical Federated Learning With Non-IID Data. IEEE Transactions on Network Science and Engineering.

»DOI: 10.1109/TNSE.2025.3582366

Authors:
Mo, D. Y., Tsang, Y. P., Lam, H. Y., & Chung, K. T.

Abstract:
This study investigates the deep reinforcement learning (DRL)-based approach to manage dynamic same-day delivery orders in e-fulfilment systems connected to physical and internet logistics network. To enable real-time response to ad-hoc changes, a DRL-based approach which employs the actor–critic mechanism is proposed to generate dynamic vehicle routing solutions for q-commerce order additions and cancellations. Computational experiments were conducted to compare the performance of OR-Tools (ORT) and the proposed method in static and dynamic environments. The experimental results showed that the proposed deep reinforcement learning q-fulfilment routing optimizer (DRLQRO) offered similar performance to ORT in a static environment with higher vehicle capacity, while it outperformed ORT in a dynamic environment with lower vehicle capacity, achieving cost savings of 12.3–19.55%. The DRLQRO features adaptability and robustness, making them suitable for e-fulfilment systems in the digital twin era.

»Cite this article: Mo, D. Y., Tsang, Y. P., Lam, H. Y., & Chung, K. T. (2025). Deep reinforcement learning-based approach for dynamic routing in quick-commerce e-fulfilment systems. International Journal of Logistics Research and Applications, 1-24. 

»DOI: 10.1080/13675567.2025.2589897

Authors:
Ng, T. C., Choy, S. K., Tang, M. L., Regelskis, V., & Lam, S. Y.

Abstract:
This paper presents a superpixel segmentation algorithm that integrates anisotropic total variation regularization within a fuzzy clustering framework. While isotropic total variation is well-known for its edge-preserving properties, its non-adaptive nature often leads to over-regularization. In contrast, the anisotropic model formulates superpixel regularity in relation to image contours, thereby preventing the loss of image details in areas of high contour density during optimization. Compared to classical segmentation algorithms that employ non-adaptive regularization, the proposed content-adaptive approach enhances superpixel regularity while maintaining boundary adherence to image contours. Furthermore, to optimize the functional effectively, an alternating direction method of multipliers along with the enhanced Chambolle’s fast duality projection algorithm are employed. Competitive experiments against existing regular segmentation algorithms demonstrate that our proposed methodology achieves superior performance in terms of boundary recall, compactness, and shape regularity criteria, outperforming these methods by an average of at least 3%, 5%, and 3%, respectively. Furthermore, when compared with irregular segmentation algorithms, our approach achieves the best results in terms of compactness, contour density, and shape regularity criteria, with average improvements of at least 56%, 22%, and 45%, respectively.

»Cite this article: Ng, T. C., Choy, S. K., Tang, M. L., Regelskis, V., & Lam, S. Y. (2026). Fuzzy Superpixel Segmentation with Anisotropic Total Variation Regularization. Mathematics, 14(3), 404.

»DOI:10.3390/math14030404

Authors:
Liu, H., Ye, S., Ma, Y. T., Wang, Y., & Chan, T. T.

Abstract:
With the rapid advancement of robotics technologies, a group of robots are able to communicate with one another by wireless transmissions and form a robot swarm. Robot swarm has many applications and a typical one is target search in which swarm robots are sent to places that might be dangerous for human workers, and they coordinate with one another to search for targets such as survivors in a disaster. However, motion coordination of swarm robots for target search has received little attention especially when the targets are mobile. In this work, we develop a motion coordination algorithm for swarm robots to search for targets in an unknown area. Our basic idea is to divide the search area into grids and build a gray-scale map in which each grid is associated with a gray scale indicating the efficiency of searching targets in this grid. The Voronoi diagram is adopted to coordinate swarm robots to search different portions of the search area for maximizing search efficiency. By theoretical analysis, our motion coordination algorithm is validated to ensure that all static targets are guaranteed to be found. We derive an upper-bound on the total time for robots to traverse all the grids in the search area. Extensive simulations are conducted and the results show that the proposed motion coordination algorithm outperforms the state-of-the-art and achieves a success rate of over 90% in finding all mobile targets with low search latency. Note to Practitioners—This paper was motivated by the problem of target search in an unknown area (e.g., the search for Malaysia Airlines MH370). With advanced robotics technologies, swarm robots could be sent to the area to perform a search mission. This work suggests a motion coordination algorithm for swarm robots to search for both static and mobile targets in an unknown area with possible obstacles. The proposed algorithm divides the search area into grids and each grid is associated with a gray scale indicating the efficiency of se…

»Cite this article: Liu, H., Ye, S., Ma, Y. T., Wang, Y., & Chan, T. T. (2025). Motion coordination of swarm robots for mobile target search. IEEE Transactions on Automation Science and Engineering.

»DOI: 10.1109/TASE.2025.3600954

Authors:
Tang, X. P., Tian, Y. Z., Wang, Y., & Wu, C. H.

Abstract:
Count data is a type of data derived from the number of times an event occurs per unit of time, and zero-truncated count data refers to count data without zero. Modeling for this type of data primarily utilizes a single zero-truncated distribution, such as the zero-truncated Poisson (ZTP), zero-truncated Bell (ZTBell), and zero-truncated negative binomial (ZTNB) distribution. However, except for ZTNB model, fewer relevant models involving heterogeneous count data have been studied. Therefore, in this article, we propose a zero-truncated Bell-Poisson mixture (ZTBPM) regression model based on Bell and Poisson distributions and study the parameter estimation method of this model. Monte Carlo simulation verifies that the ZTBPM model fits better than the ZTP, ZTBell, and ZTNB regression models, providing more options for modeling zero-truncated count data and solving the heterogeneity problem more effectively. Finally, the ZTBPM regression model is applied to a set of outpatient data analysis to study the risk factors affecting the number of residents’ outpatient visits. This is of great significance for better understanding the health status and medical needs of residents, optimizing the allocation of medical resources, and guiding the formulation of medical policies.

»Cite this article: Tang, X. P., Tian, Y. Z., Wang, Y., & Wu, C. H. (2025). Zero-truncated Bell-Poisson mixture regression model and its application to outpatient data. Communications in Statistics-Simulation and Computation, 1-10.

»DOI: 10.1080/03610918.2025.2482730

Authors:
Luo, F., Jiang, P., Ho, G. T. S., & Zeng, W.

Abstract:
Multiobject tracking of ships is crucial for various applications, such as maritime security and the development of ship autopilot systems. However, existing ship visual datasets primarily focus on ship detection tasks, lacking a fully open-source dataset for multiobject tracking research. Furthermore, current methods often struggle with extracting appearance features under complex sea conditions, varying scales and different ship types, affecting tracking precision. To address these issues, we propose ShipsMOT, a new benchmark dataset containing 121 video sequences with an average of 15.45 s per sequence, covering 15 distinct ship types and a total of 237,999 annotated bounding boxes. Additionally, we propose JDR-CSTrack, a ship multiobject tracking framework that improves feature extraction at different scales by optimising a joint detection and Re-ID network. JDR-CSTrack utilises the fusion of appearance and motion features for multilevel data association, thereby minimising track loss and ID switches. Experimental results confirm that ShipsMOT can serve as a benchmark for future research in ship multiobject tracking and validate the superiority of the proposed JDR-CSTrack framework. The dataset and code can be found on https://github.com/jpj0916/ShipsMOT.

»Cite this article: Luo, F., Jiang, P., Ho, G. T. S., & Zeng, W. (2025). ShipsMOT: A Comprehensive Benchmark and Framework for Multiobject Tracking of Ships. IET Computer Vision, 19(1), e70042.

»DOI:10.1049/cvi2.70042

730

Authors:
Dong, H., Huang, M., Lam, H. Y., Mo, L., Zhuang, X., & Zuo, M.

Abstract:
Over 80% of e-commerce warehouses use the picker-to-parts model, where employees collect items within the warehouse. Despite its widespread use, research that integrates the order lifecycle and employee behavior is limited. This article addresses this gap by exploring operational behavior computing and human-machine integration to optimize picker-to-parts efficiency. First, we outline the order-planning workflow from an order lifecycle perspective. We then conduct a literature review to identify practical applications of academic insights. The article concludes by suggesting future research directions, such as developing online coordinated optimization techniques and integrating multiple technologies to address data quality and uncertainty.

»Cite this article: Dong, H., Huang, M., Lam, H. Y., Mo, L., Zhuang, X., & Zuo, M. (2025). Enhancing efficiency in the picker-to-parts E-commerce warehouse: a perspective based on order lifecycle and operational behavior computing. Enterprise Information Systems, 19(5-6), 2448832.

»DOI:10.1080/17517575.2024.2448832

Authors:
Qianru Zhou, Bai Yin, Baolei Cheng, Yan Wang, Hai Liu, Jianxi Fan.

Abstract:
Large-scale parallel and distributed applications and platforms rely heavily on high-performance network architectures, which greatly emphasize factors including network latency, bandwidth, communication reliability (e.g., connectivity and fault-tolerant routing), etc. Due to the expansion of network scales and the increase of component failures, the demand for the network’s high reliability has grown substantially. Enhancing fault tolerance is the key to improving the network’s reliability, which can be achieved by establishing the connectivity under the “ g -good-neighbor condition”. In the presence of component failures, to maintain normal network operation and minimize communication delays, effective fault-tolerant routing strategies are required. This paper proposes a new class of recursive networks with small diameter, named generalized recursive match networks (GRMNs). GRMNs include well-known networks like generalized hypercubes, BC networks, BCube, HyperX, FBFLY, 2DFB, etc, and other future networks. We investigate the classical connectivity and g -good-neighbor connectivity ( g≥0 ) of the GRMN. In addition, we present an O(N) fault-tolerant unicast routing algorithm with the g -good-neighbor faulty set, called F-TRouting, where N is the number of edges in the GRMN. The algorithm and results can be directly applied to all networks falling within the description of GRMNs, including generalized hypercubes, BC networks, BCube, HyperX, FBFLY, 2DFB, and other future networks. Finally, we give simulation experiment results on the performances of the GRMN and the F-TRouting algorithm, which demonstrate that the GRMN and the F-TRouting have advantageous performances.

»Cite this article: Zhou, Q., Yin, B., Cheng, B., Wang, Y., Liu, H., & Fan, J. (2025). Reliable Communication Performance of Recursive Networks Based on Inter-Subgraph Matching. IEEE Transactions on Networking.

»DOI:10.1109/TON.2025.3596138

Authors:
Tian, Y., Wu, C.H., Tang, M., & Tian, M.

Abstract:
In this paper, we propose a Bayesian quantile regression (QR) approach to jointly model multivariate ordinal data. Firstly, a multivariate latent variable model is used to link the multivariate ordinal data and latent continuous responses and the multivariate asymmetric Laplace (MAL) distribution is employed to construct the joint QR-based working likelihood for the considered model. Secondly, adaptive- L1/2 penalization priors of regression parameters are incorporated into the working likelihood to implement high-dimensional Bayesian joint QR inference. Markov Chain Monte Carlo (MCMC) algorithm is utilized to derive the fully conditional posterior distributions of all parameters. Thirdly, Bayesian joint relatively QR estimation approach is recommended to result in more efficient estimation results. Finally, Monte Carlo simulation studies and a real instance analysis of multirater agreement data are presented to illustrate the performance of the proposed Bayesian joint relatively QR approach.

»Cite this article: Tian, Y., Wu, C., Tang, M., & Tian, M. (2025). Bayesian joint relatively quantile regression of latent ordinal multivariate linear models with application to multirater agreement analysis. AStA Advances in Statistical Analysis, 109(1), 85-116.

»DOI:10.1007/s10182-024-00509-y

Authors:
Wei, Z., Wu, H., Lin, Z., Wen, Q., Zheng, L., Wen, J., & Liu, H.

Abstract:
The utilization of directional antennas for neighbor discovery in wireless ad hoc networks brings notable benefits, such as extended transmission range, reduced transmission interference, and enhanced antenna gain. However, when nodes use directional antennas for neighbor discovery, the communication range is limited, resulting in a lack of knowledge of potential neighbors. Hence, it is necessary to design a special antenna direction switching strategy for neighbor discovery based on directional antennas. Traditional methods of switching antenna directions are often random or follow predefined sequences, overlooking the historical knowledge of sector exploration for antenna directions. In contrast, existing machine learning approaches aim to leverage observed historical knowledge to adjust antenna directions for faster neighbor discovery. Nonetheless, the latency of neighbor discovery is still high because the node cannot fully utilize the observed historical knowledge (i.e.., only using the knowledge observed by the node in transmission mode, ignoring the knowledge observed by the node in reception mode). Meanwhile, the corresponding reward and penalty mechanisms are still not detailed enough (i.e.., these reward and penalty mechanisms only consider the sectors of discovered and undiscovered neighboring nodes, ignoring the scenario of sectors that have been rewarded). In this paper, the neighbor discovery process is modeled as a reinforcement learning-based learning automaton. We propose an enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery algorithm, called ERTTND. The algorithm consists of a two-way transmit-receive reinforcement learning mechanism (TTRL) and an enhanced reward-and-penalty mechanism (ERAP). This algorithm leverages insights from nodes in transmission and reception modes to refine their tactical decisions. Then, through an enriched reward-and-penalty framework, nodes optimize their strategies, thus expediting neighbor discovery based on directional antennas in wireless ad hoc networks. Simulation results demonstrate that compared to existing representative algorithms, the proposed ERTTND algorithm can achieve over 30% savings in terms of average discovery delay and energy consumption.

»Cite this article: Wei, Z., Wu, H., Lin, Z., Wen, Q., Zheng, L., Wen, J., & Liu, H. (2025). Enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery in wireless ad hoc networks. Ad Hoc Networks, 167, 103689.

»DOI:10.1016/j.adhoc.2024.103689

Authors:
Yang, J., Tian, Y., & Wu, C. H.

Abstract:
Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.

»Cite this article: Yang, J., Tian, Y., & Wu, C. H. (2024). Air quality prediction and ranking assessment based on Bootstrap-XGBoost algorithm and ordinal classification models. Atmosphere, 15(8), 925.

»DOI:10.3390/atmos150809251934

Authors:
Lam H and Tang V.

Abstract:
During the pandemic, the attention and demand for cold chain increased owing to considerable use of low-temperature logistics in transporting perishable goods and vaccines. To ensure the shipping performance for reduced damage, logistics companies are required to track continually and repetitively the status of shipments daily. However, typing various air waybills for searching the shipping status is a cause of frequent errors. Also, tracking the shipping status is labor-intensive, resource intensive, inefficient and repetitive. Moreover, repetitive tasks result in low employee satisfaction. Therefore, robotic process automation (RPA) applications have gained the attention of practitioners in the cold chain logistics industry. This study contributes to (i) determining possible areas requiring automation through the workflow study on cold chain logistics and (ii) streamlining the operation by the develop a robotic process automation bots. A case study tested and evaluated the performance of two unattended RPA bots applied in a freight forwarder company to check shipment status and temperature conditions. The results determined that implementing RPA in the workflow reduces significant data processing time. With the implementation of proposed RPA bots, the company can better comprehend its shipping performance of logistics and can get an immediate notification from RPA bots when an abnormal situation occurs with regard to a shipment.

Authors:
Lam, H. Y., Ho, G. T. S., Mo, D. Y., & Tang, V.

Abstract:
In the rapidly growing e-commerce industry, pallet picking is no longer feasible as the stock keeping units (SKUs) change from the pallet level to the carton or item level. Thus, an effective order-picking process focusing on fast and efficient retrieval of SKUs from shelves is required to fulfil numerous small lot-sized e-commerce orders within a short time. This study investigates a responsive pick face replenishment (RPFR) strategy that divides the high-bay racks in the distribution centres (DCs) into two parts: the upper-deck reserve areas and the pick-face forward areas to improve the operational efficiency in order picking. To address the fluctuating order demand and limited space in the pick-face forward areas, the proposed RPFR system integrates a predictive analytics algorithm with an adaptive network-based fuzzy inference system (ANFIS) and adaptive genetic algorithm-based stock allocation model to generate an optimal stock replenishment plan. By predicting the order demand of each SKU in the next time interval, the types of selected SKUs and their quantities to be loaded into the pick-face forward areas are determined. Numerical experiments are performed to validate the system performance, and comparative analyses are conducted to determine the best parameter settings for the models.

Authors:
Ng, T. C., Choy, S. K., Lam, S. Y., & Yu, K. W.

Abstract:
This article presents a multi-phase image segmentation methodology based on fuzzy superpixel decomposition, aggregation and merging. First, a collection of layers of dense fuzzy superpixels is generated by the variational fuzzy decomposition algorithm. Then a layer of refined superpixels is extracted by aggregating various layers of dense fuzzy superpixels using the hierarchical normalized cuts. Finally, the refined superpixels are projected into the low dimensional feature spaces by the multidimensional scaling and the segmentation result is obtained via the mean-shift-based merging approach with the spatial bandwidth adjustment strategy. Our algorithm utilizes the superimposition of fuzzy superpixels to impose more accurate spatial constraints on the final segmentation through the fuzzy superpixel aggregation. The fuzziness of superpixels also provides spatial features to measure affinities between fuzzy superpixels and refined superpixels, and guide the merging process. Comparative experiments with the existing approaches reveal a superior performance of the proposed method.

Authors:
Ng, S. C. H., Ho, G. T. S., & Wu, C. H.

Abstract:
The literature on quality management system (QMS) assumes that product and process performance data are authentic and easily accessible. This assumption, while ideologically sound, is questionable in practise because the authenticity and accessibility of data cannot be guaranteed in many circumstances. Inaccurate, incomplete, inconsistent, and inaccessible data are common in supply chains and prevent the QMS from achieving its goal: assuring product and process quality to meet customer requirements. This study is one of the first to examine the impact of data quality and data latency on process control and quality analysis which are elemental parts of daily QMS activities, from a supply chain visibility (SCV) perspective. In this study, five propositions are made to show the relationships between technology, SCV, and data issues. More importantly, the study proposes a platform that integrates Blockchain (BC) technology, Industrial Internet of Things (IIoT), and Big Data to solve data problems in SCV and QMS. We further perform fuzzy association rule mining (FARM) to show how the platform can solve quality analysis problems and complete a closed-loop process control cycle in manufacturing. We also explain the contributions of the integrated platform to QMS from four theoretical perspectives. Finally, we discuss the limitations of the platform and provide recommendations for future research.

Authors:
Li, X. J., Tian, G. L., Zhang, M., Ho, G. T. S., & Li, S.

Abstract:
One of the main concerns in multidimensional item response theory (MIRT) is to detect the relationship between observed items and latent traits, which is typically addressed by the exploratory analysis and factor rotation techniques. Recently, an EM-based L1-penalized log-likelihood method (EML1) is proposed as a vital alternative to factor rotation. Based on the observed test response data, EML1 can yield a sparse and interpretable estimate of the loading matrix. However, EML1 suffers from high computational burden. In this paper, we consider the coordinate descent algorithm to optimize a new weighted log-likelihood, and consequently propose an improved EML1 (IEML1) which is more than 30 times faster than EML1. The performance of IEML1 is evaluated through simulation studies and an application on a real data set related to the Eysenck Personality Questionnaire is used to demonstrate our methodologies.Under-dispersed count data often appear in clinical trials, medical studies, demography, actuarial science, ecology, biology, industry and engineering. Although the generalized Poisson (GP) distribution possesses the twin properties of under- and over-dispersion, in the past 50 years, many authors only treat the GP distribution as an alternative to the negative binomial distribution for modeling over-dispersed count data. To our best knowledge, the issues of calculating maximum likelihood estimates (MLEs) of parameters in GP model without covariates and with covariates for the case of under-dispersion were not solved up to now. In this paper, we first develop a new minimization–maximization (MM) algorithm to calculate the MLEs of parameters in the GP distribution with under-dispersion, and then we develop another new MM algorithm to compute the MLEs of the vector of regression coefficients for the GP mean regression model for the case of under-dispersion. Three hypothesis tests (i.e., the likelihood ratio, Wald and score tests) are provided. Some simulations are conducted. The Bangladesh demographic and health surveys dataset is analyzed to illustrate the proposed methods and comparisons with the existing Conway–Maxwell–Poisson regression model are also presented.

Authors:
Shang, L., Xu, P. F., Shan, N., Tang, M. L., & Ho, G. T. S.

Abstract:
One of the main concerns in multidimensional item response theory (MIRT) is to detect the relationship between observed items and latent traits, which is typically addressed by the exploratory analysis and factor rotation techniques. Recently, an EM-based L1-penalized log-likelihood method (EML1) is proposed as a vital alternative to factor rotation. Based on the observed test response data, EML1 can yield a sparse and interpretable estimate of the loading matrix. However, EML1 suffers from high computational burden. In this paper, we consider the coordinate descent algorithm to optimize a new weighted log-likelihood, and consequently propose an improved EML1 (IEML1) which is more than 30 times faster than EML1. The performance of IEML1 is evaluated through simulation studies and an application on a real data set related to the Eysenck Personality Questionnaire is used to demonstrate our methodologies.

Authors:
Yung, K. L., Tsang, Y. P., Wu, C. H., & Ip, W. H.

Abstract:
In recent aerospace missions, space logistics have proven essential in storing, delivering and returning crew and materials between terrestrial facilities and space stations. Unlike classical commercial logistics, space logistics operations are cost-prohibitive and mission-driven, and its replenishment cycle for essential materials is relatively long. Therefore, the complete utilisation of spacecraft payload is of utmost importance. The theory of the inventory packing problem is extended in this study to build autonomous agents that interact with one another within a space logistics decision support system to reinforce the replenishment decision, chunk loading optimisation, and quality inspection. With the long replenishment cycle time, an agent embedded with interval type-2 fuzzy logic is explored to support chaotic time-series demand forecasting to derive re-order quantities in the desired period. Afterwards, the second agent solves the space chunk loading problem using the differential evolution algorithm to utilise payloads and capacities, particularly cylindrical chunks fully. The third agent measures actual item dimensions and quality to deploy the three-dimensional object scanning devices. Feedback is provided to the second agent to derive optimal chunk-loading instructions. Thanks to the autonomous interactions among the above agents, mission-critical decisions for space logistics are supported to achieve operational excellence.

Authors:
Ho, G. T. S., Tang, Y. M., Lam, H. Y., & Tang, V.

Abstract:
The rapidly growing e-commerce sector has created new opportunities and challenges to the logistics industry. Nonetheless, the majority of Hong Kong logistics industry, especially small-and-medium (SMEs) firms, lack operational decision support to adequately handle the seasonal, fragmented, and fluctuating e-commerce orders. To grasp the opportunities of the e-commerce logistics business, logistics service providers (LSPs) should enhance their capability in information exchange and operational planning. With these improvements, the logistics industry would be better able to sustain and expand their e-commerce logistics business. In this paper, a Blockchain-based E-Commerce Analytics Model is developed to enhance digital supply chain integration. Firstly, timely operational decision support would be achieved as blockchain technology, after that, the ML algorithm would enable the logistics industry to manage data efficiently and to forecast dynamic e-commerce order demand. Subsequently, the proposed model allows LSPs to flexibly re-allocate the right number of resources in real time to deal with the hour-to-hour fluctuating arrival of orders in distribution centers. Additionally, the proposed model enables logistics practitioners to predict the sales performance related to e-commerce.

Authors:
Lam, H. Y., Tang, V., & Ho, G. T. S.

Abstract:
Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a “broken” cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong.

Authors:
Men, J., Hou, Y., Sheng, Z., & Chan, T. T.

Abstract:
The high mobility feature of vehicular networks poses tremendous challenges to maintaining network connectivity. In this paper, we investigate the possibility of enhancing the connectivity of Cellular Vehicle-to-Everything (C-V2X) networks through distributed trajectory adjustment. Based on a physical layer abstraction model, we characterize the network connectivity enhancement problem as a network utility maximization and study its concavity. We propose a distributed trajectory updating algorithm that dynamically adjusts the trajectory of vehicles on top of their planned trajectory. The algorithm is distributed and requires only geo-location exchanges, which are readily available in V2X networks. Simulation results show that the mobility updating algorithm converges and improves the aggregated network utility by up to 48% compared to the scenarios without mobility tuning.

Authors:
Wang, Q., Mei, X., Liu, H., Leung, Y. W., Li, Z., & Chu, X.

Abstract:
Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

Authors:
Tsang, Y. P., Wu, C. H., Ip, W. H., & Lee, C. K. M.

Abstract:
Solder paste printing (SPP) is one of the critical processes for the printed circuit board assembly to reliably apply the solder paste on raw PCBs for the component placement through surface mount technology. Although computerised SPP machines have been developed in the past few years, the reliance on domain experts cannot be neglected to fine-tune corresponding process parameters so as to maintain productivity and quality. This study exploits federated learning on the industrial internet of things (IIoT) paradigm to establish an intelligent decision support system across various networked machines. The IIoT-based squeegee blade is deployed in the SPP machines for better machine-to-machine communication and interconnectivity. In contrast, a global machine intelligence model is aggregated in a decentralised and privacy-preserving manner. Consequently, the automated and sustainable manufacturing management for PCBA is achieved, where wastes from trial production runs are eliminated.

Authors:
Ho, G. T. S., Choy, S. K., Tong, P. H., & Tang, V.

Abstract:
Purpose
Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.

Design/methodology/approach
The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.

Findings
A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.

Originality/value
Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

Authors:
Mo, D. Y., Ma, C. Y., Ho, D. C., & Wang, Y.

Abstract:
Despite that reverse logistics of service parts enables the reuse of failed components to achieve greater environmental and economic benefits, the research and successful business cases are inadequate. This study designs a novel reverse logistics system that applies the Internet of Things (IoT) and business intelligence to streamline the reverse logistics process by identifying the appropriate components for sustainable operations of component reuse. Furthermore, an inventory classification scheme and an analytical model are developed to identify the failed components for refurbishment by considering return quantity of the failed component, repair rate of the failed component in the repairing center, reusable rate of refurbished parts, corresponding costs, and the benefit of refurbished parts. Moreover, a mobile application powered by the IoT technology is developed to streamline the process flow and avoid collection of fake components. Lastly, a case study of an electronic product company is conducted, and it is concluded that the proposed approach enabled the company to facilitate the reuse of components and achieve the benefit of cost saving. The results of this study demonstrate the importance of a reverse logistics system for companies to sustain after-market service operations.

Authors:
Liu, H., Yu, L., Poon, C. K., Lin, Z., Leung, Y. W., & Chu, X.

Abstract:
In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish communication links. Most of existing works were devoted to shortening the time-to-rendezvous (TTR) but paid little attention to qualities of the channels on which rendezvous is achieved. In fact, qualities of channels, such as resistance to primary users’ activities, have a great effect on the rendezvous operation. If users achieve a rendezvous on a low-quality channel, the communication link is unstable and the communication performance is poor. In this case, re- rendezvous is required which results in considerable communication overhead and a large latency. In this paper, we first show that actual TTRs of existing rendezvous solutions increase by 65.40-104.38% if qualities of channels are not perfect. Then we propose a Quality-Aware Rendezvous Framework (QARF) that can be applied to any existing ren-dezvous algorithms to achieve rendezvous on high-quality channels. The basic idea of QARF is to expand the set of available channels by selectively duplicating high-quality channels. We prove that QARF can reduce the expected TTR of any rendezvous algorithm when the expanded ratio λ is smaller than the threshold (−3+√1+4(σ/μ)^2)/2, where μ and σ, respectively, are the mean and the standard deviation of qualities of channels. We further prove that QARF can always reduce the expected TTR of Random algorithm by a factor of 1+(σ/μ)^2. Extensive experiments are conducted and the results show that QARF can significantly reduce the TTRs of the existing rendezvous algorithms by 10.50-51.05 % when qualities of channels are taken into account.

Authors:
Dong, N., Qin, M., Chang, J., Wu, C. H., Ip, W. ., & Yung, K. .

Abstract:
Smart living is an emerging technology that has attracted a lot of attention all around the world. As a key technology of smart space, which is the principal part of smart living, the SLAM system has effectively expanded the ability of space intelligent robots to explore unknown environments. Loop closure detection is an important part of SLAM system and plays a very important role in eliminating cumulative errors. The SLAM system without loop closure detection is degraded to an odometer. The state estimation solely relying on an odometer will be seriously deviated in the long-term and large-scale navigation and positioning. This paper proposes a metric learning method that uses deep neural networks for loop closure detection based on triplet loss. The map points obtained by metric learning are fused with all map points in the current keyframe, and the map points that do not meet the filtering conditions are eliminated. Based on the Batch Hard Triplet loss, the weighted triplet loss function avoids suboptimal convergence in the learning process by applying weighted value constraints. At the same time, considering that fixed boundary parameters cannot be well adapted to the diversity of scales between different samples, we use the semantic similarity of anchor samples and negative samples to redefine boundary parameters. Finally, a SLAM system based on metric learning is constructed, and the SLAM dataset TUM and KITTI are used to evaluate the proposed model’s accuracy rate and recall rate. The scene features in this method are extracted automatically through neural networks instead of being artificially set. Finally, a high-precision closed-loop detection method based on weight adaptive triple loss is effectively realised through the closed-loop detection experiment. The minimum relative pose error is 0.00048 m, which is 15.8% less than that of the closed-loop detection algorithm based on the word bag model.

Authors:
Mo, D. Y., Tang, Y. M., Wu, E. Y., & Tang, V.

Abstract:
Electronic assessment (e-assessment) is an essential part of higher education, not only used to manage a large class size of students’ learning performance and particularly in assessing the learning outcomes of students. The e-assessment data generated can not only be used to determine students’ study weaknesses to develop strategies for teaching and learning, but also in the development of essential teaching and learning pedagogies for online teaching and learning. Despite the wider adoption of Information and Communication Technology (ICT) technologies due to the COVID-19 pandemic, universities still encountered numerous problems during the transformation to electronic teaching as most educators struggled with the effective implementation of the Electronic Assessment System (EAS). The successful launch of EAS relied heavily on students’ use intention towards the new and unfamiliar electronic system, which was actually unknown to the project managers of EAS. It is therefore important to understand students’ views and concerns on EAS and the proactive measures taken by universities to enhance students’ acceptance and intention of usage. Although most studies investigate students’ acceptance of online learning, there is still little research on the adoption of e-assessment. In this regard, we propose to develop a theoretical model based on students’ perceptions of EAS. Based on the Technology Acceptance Model (TAM) and a major successor of TAM, an electronic assessment system acceptance model (EASA model) is developed with key measures including system adoption anxiety, e-assessment facilitation, risk reduction amid, etc. The data is obtained through a survey among current students at a local university, and structural equation modeling (SEM) is applied to analyze the quantitative data. This study has a significant impact on improving educators’ use of e-assessment in order to develop essential online teaching and learning pedagogy in the future.

Authors:
Mo, D. Y., Wang, Y., Ho, D. C. K., & Leung, K. H.

Abstract:
Service parts management has the potential to generate high profits for companies that deliver superior service parts services in the after-sale market. However, a big challenge in managing service parts operations is to meet the high expectations of service levels and to reduce excess inventories caused by fluctuating demand and a complex service parts logistics network structure. By expanding the conventional inventory management that passively focuses on the forward and lateral flows of service parts deployment, we propose a crucial but overlooked practice of inventory redeployment as an integral part of the operations that allow the proactive management of lateral and reverse flows of service parts. We formulate the service parts inventory problem with the application of an excess inventory redeployment strategy in a multi-echelon service network as a multi-period integer programming model. This optimisation model is evaluated using a case study of an international company’s service parts operations and demonstrates a higher cost-saving potential. Our novel, integrated approach confers the advantage of redeploying excess inventories in a closed-loop service parts logistics network with a higher cost-saving potential that could not have been achieved in a conventional approach.

Authors:
Tang V., Lam H.Y., Wu C.H. and Ho G.T.S.

Abstract:
Due to the increasing ageing population, how can caregivers effectively provide long-term care services to meet the older adults’ needs with finite resources is emerging. In addressing this issue, nursing homes are striving to adopt smart health with the internet of things and artificial intelligence to improve the efficiency and sustainability of healthcare. This study proposed a two-echelon responsive health analytic model (EHAM) to deliver appropriate healthcare services in nursing homes under the Internet of Medical Things environment. A novel care plan revision index is developed using a dual fuzzy logic approach for multidimensional health assessments, followed by care plan modification using case-based reasoning. The findings reveal that EHAM can generate patient-centred long-term care solutions of high quality to maximise the satisfaction of nursing home residents and their families. Ultimately, sustainable healthcare services can be within the communities.

Authors:
Tang Y.M., Ho G.T.S., Lau Y.Y. and Tsui S.Y.

Abstract:
In the context of the global economic slowdown, demand forecasting, and inventory and production management have long been important topics to the industries. With the support of smart warehouses, big data analytics, and optimization algorithms, enterprises can achieve economies of scale, and balance supply and demand. Smart warehouse and manufacturing management is considered the culmination of recently advanced technologies. It is important to enhance the scalability and extendibility of the industry. Despite many researchers having developed frameworks for smart warehouse and manufacturing management for various fields, most of these models are mainly focused on the logistics of the product and are not generalized to tackle the specific manufacturing problem facing in the cyclical industry. Indeed, the cyclical industry has a key problem: the big risk which high sensitivity poses to the business cycle and economic recession, which is difficult to foresee. Despite many inventory optimization approaches being proposed to optimize the inventory level in the warehouse and facilitate production management, the demand forecasting technique is seldom focused on the cyclic industry. On the other hand, management approaches are usually based on the complex logistics process instead of integrating the inventory level of the stock, which is very crucial to composing smart warehouses and manufacturing. This research study proposed a digital twin framework by integrating the smart warehouse and manufacturing with the roulette genetic algorithm for demand forecasting in the cyclical industry. We also demonstrate how this algorithm is practically implemented for forecasting the demand, sustaining manufacturing optimization, and achieving inventory optimization. We adopted a small-scale textile company case study to demonstrate the proposed digital framework in the warehouse and demonstrate the results of demand forecasting and inventory optimization. Various scenarios were conducted to simulate the results for the digital twin. The proposed digital twin framework and results help manufacturers and logistics companies to improve inventory management. This study has important theoretical and practical significance for the management of the cyclical industry.

Authors:
Tsang, Y. P., Wu, C. H., Lin, K. Y., Tse, Y. K., Ho, G. T. S., & Lee, C. K. M.

Abstract:
New product development to enhance companies’ competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers, manufacturing capability, and market trend, are considered in the FFE phase to avoid common flaws in product design. Because of the high degree of uncertainties in the FFE, multidimensional and high-volume data are added from time to time at the beginning of the formal product development process. To address the above concerns, deploying big data analytics to establish industrial intelligence is an active but still under-researched area. In this paper, an intelligent product design framework is proposed to incorporate fuzzy association rule mining (FARM) and a genetic algorithm (GA) into a recursive association-rule-based fuzzy inference system to bridge the gap between customer attributes and design parameters. Considering the current incidence of epidemics, such as the COVID-19 pandemic, communication of information in the FFE stage may be hindered. Through this study, a recursive learning scheme is established, therefore, to strengthen market performance, design performance, and sustainability on product design. It is found that the industrial big data analytics in the FFE process achieve greater flexibility and self-improvement mechanism on the evolution of product design.

Authors:
Dong, N., Zhai, M. D., Chang, J. F., & Wu, C. H.

Abstract:
As important immune cells in the human body, white blood cells play a very significant role in the auxiliary diagnosis of many major diseases. Clinically, changes in the number and morphology of white blood cells and their subtypes are the prediction index for important, serious diseases, such as anaemia, malaria, infections, and tumours. The application of image recognition technology and cloud computing to assist in medical diagnosis is a hot topic in current research, which we believe have great potential to further improve real-time detection and improve medical diagnosis. This paper proposes a novel automatic classification framework for the recognition of five subtypes of white blood cells, in the hope of contributing to disease prediction. First, we present an adaptive threshold segmentation method to deal with blood smear images with nonuniform colour and uneven illumination. The method is designed based on colour space information and threshold segmentation. After successfully separating the white blood cell from the blood smear image, a large number of features, including geometrical, colour, and texture features are extracted. However, redundant features can affect the classification speed and efficiency, and in view of that, a feature selection algorithm based on classification and regression trees (CART) is designed to successfully remove irrelevant and redundant features from the initial features. The selected prominent features are fed into a particle swarm optimisation support vector machine (PSO-SVM) classifier to recognise the types of white blood cells. Finally, to evaluate the performance of the proposed white blood cell classification methodology, we build a white blood cell data set containing 500 blood smear images for experiments. The proposed methodology achieves 99.76% classification accuracy, which well demonstrates its effectiveness.

Authors:
Lam, H. Y., Tsang, Y. P., Wu, C. H., & Tang, V.

Abstract:
For companies to gain competitive advantage, an effective customer relationship management (CRM) approach is necessary. Based on customer purchase behaviour and ordering patterns, companies can be classified into different categories in terms of providing customised sales and promotions for customers. However, companies that lack an effective CRM strategy can only offer the same sales and marketing strategies to all customers. Furthermore, the traditional approach to managing customers is control via a centralised method, in which the information regarding customer segmentation is not shared among the customer network. Consequently, valuable customers may be neglected, resulting in the loss of customer loyalty and sales orders, and the weakening of trust in the customer–company relationship. This paper designs an integrated data analytic model (IDAM) in a peer-to-peer cloud, integrating RFM-based k-means clustering algorithm, analytical hierarchy processing and fuzzy logic to divide customers into different segments and hence formulate a customised sales strategy. A pilot study of IDAM is conducted in a trading company specialised in providing advanced manufacturing technology to demonstrate how IDAM can be applied to formulate an effective sales strategy to attract customers. Overall, this study explores the effective deployment of CRM into the peer-to-peer cloud so as to facilitate sales strategy formulation and trust between customers and companies in the network.

Authors:
Long, W., Wu, C. H., Tsang, Y. P., & Chen, Q.

Abstract:
Pallet pooling is regarded as a sustainable and cost-effective measure for the industry, but challenging to advocate due to weak data and pallet authentication.In order to establish trust between end-users and pallet pooling services, we propose an end-to-end, bidirectional authentication system for transmitted data and pallets based on blockchain and Internet-of-things (IoT) technologies. In addition, secure data authentication fosters the pallet authenticity in the whole supply chain network, which is achieved by considering the tag, location, and object-specific features. To evaluate the object-specific features, the scale invariant feature transform (SIFT) approach is adopted to match key-points and descriptors between two pallet images. According to the case study, it is found that the proposed system provides a low bandwidth blocking rate and a high probability of restoring complete data payloads. Consequently, positive influences on end-user satisfaction, quality of service, operational errors, and pallet traceability are achieved through the deployment of the proposed system.

Authors:
Tsang, Y. P., Wu, C. H., Ip, W. H., & Shiau, W. L.

Abstract:
Purpose
Due to the rapid growth of blockchain technology in recent years, the fusion of blockchain and the Internet of Things (BIoT) has drawn considerable attention from researchers and industrial practitioners and is regarded as a future trend in technological development. Although several authors have conducted literature reviews on the topic, none have examined the development of the knowledge structure of BIoT, resulting in scattered research and development (R&D) efforts.

Design/methodology/approach
This study investigates the intellectual core of BIoT through a co-citation proximity analysis–based systematic review (CPASR) of the correlations between 44 highly influential articles out of 473 relevant research studies. Subsequently, we apply a series of statistical analyses, including exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), k-means clustering (KMC) and multidimensional scaling (MDS) to establish the intellectual core.

Findings
Our findings indicate that there are nine categories in the intellectual core of BIoT: (1) data privacy and security for BIoT systems, (2) models and applications of BIoT, (3) system security theories for BIoT, (4) frameworks for BIoT deployment, (5) the fusion of BIoT with emerging methods and technologies, (6) applied security strategies for using blockchain with the IoT, (7) the design and development of industrial BIoT, (8) establishing trust through BIoT and (9) the BIoT ecosystem.

Originality/value
We use the CPASR method to examine the intellectual core of BIoT, which is an under-researched and topical area. The paper also provides a structural framework for investigating BIoT research that may be applicable to other knowledge domains.

Authors:
Wang, T., Zuo, H., Wu, C. H., & Hu, B.

Abstract:
The estimation of the difference between the new competitive advantages of China’s export and the world’s trading powers have been the key measurement problems in China-related studies. In this work, a comprehensive evaluation index system for new export competitive advantages is developed, a soft-sensing model for China’s new export competitive advantages based on the fuzzy entropy weight analytic hierarchy process is established, and the soft-sensing values of key indexes are derived. The obtained evaluation values of the main measurement index are used as the input variable of the fuzzy least squares support vector machine, and a soft-sensing model of the key index parameters of the new export competitive advantages of China based on the combined soft-sensing model of the fuzzy least squares support vector machine is established. The soft-sensing results of the new export competitive advantage index of China show that the soft measurement model developed herein is of high precision compared with other models, and the technical and brand competitiveness indicators of export products have more significant contributions to the new competitive advantages of China’s export, while the service competitiveness indicator of export products has the least contribution to new competitive advantages of China’s export.

Authors:
Tsang, Y. P., Choy, K .L., Wu, C.H., Ho, G.T.S.     

Abstract:
Effective deployment of the emerging environmental sensor network in environmental mapping has become essential in numerous industrial applications. The essential factors for deployment include cost, coverage, connectivity, airflow of heating, ventilation, and air conditioning, system lifetime, and fault tolerance. In this letter, a three-stage deployment scheme is proposed to formulate the above-mentioned considerations, and the fuzzy temperature window is established to adjust sensor activation times over various ambient temperatures. To optimize the deployment effectively, a multi-response Taguchi-guided k-means clustering is proposed to embed in the genetic algorithm, where an improved set of the initial population is formulated and system parameters are optimized. Therefore, the computational time for repeated deployment is shortened, while the solution convergence can be improved.

Authors:
Ho, G.T.S. , Tsang, Y.P., Wu, C.H., Wong, W.H., Choy, K.L.            

Abstract:
In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.

Authors:
Mo, D. Y., Ng, S. C. H., Tai, David.

Abstract:
This study demonstrates how NetApp, a data storage system provider, used Six Sigma to solve the service parts inventory problem in its multiechelon logistics network, which its inventory management system was unable to fix. The nonstationary demand for service parts created a blind spot for the system, thus hampering NetApp’s contractual commitment to customers of an almost 100% fill rate (FR) for replacing service parts. Constant customer complaints because of FRs that were less than 100% caused NetApp to improve the performance of its service parts replenishment and order fulfillment processes. By following the Six Sigma approach and using the associated qualitative and quantitative tools, the company worked systemically to identify the major causes of insufficient stock and systematically corrected the problem. NetApp formulated a cost-effective inventory solution for its inventory planning system, which resulted in a 10% decrease in the ratio of inventory to revenue and an FR increase from 99.1% to 99.6%. The standard deviation of the replenishment lead time also declined from 4.97 to 1.87 days, implying that the variation of the replenishment lead time was greatly reduced. The Six Sigma process, therefore, provided new insights and a new approach to enable NetApp to manage its inventory planning process.

Authors:
Lo, W. H., Lam, B. S. Y., Cheung , M. F.

Abstract:
This article examines the news framing of the 2017 Hong Kong Chief Executive election using a big data analysis approach. Analyses of intermedia framing of over 370,000 articles and comments are conducted including news published in over 30 Chinese press media, four prominent Chinese online press media, and posts published on three candidates’ Facebook pages within the election period. The study contributes to the literature by examining the rarely discussed role of intermedia news framing, especially the relationship between legacy print media, online alternative news media, and audience comments on candidates’ social network sites. The data analysis provides evidence that audiences’ comments on candidates’ Facebook pages influenced legacy news coverage and online alternative news coverage. However, this study suggests that legacy news media and comments on Facebook do not necessarily have a reciprocal relationship. The implication of the findings and limitations are discussed.

Authors:
Lam, B. S. Y., Choy , S. K.

Abstract:
Different versions of principal component analysis (PCA) have been widely used to extract important information for image recognition and image clustering problems. However, owing to the presence of outliers, this remains challenging. This paper proposes a new PCA methodology based on a novel discovery that the widely used -PCA is equivalent to a two-groups -means clustering model. The projection vector of the -PCA is the vector difference between the two cluster centers estimated by the clustering model. In theory, this vector difference provides inter-cluster information, which is beneficial for distinguishing data objects from different classes. However, the performance of -PCA is not comparable with the state-of-the-art methods. This is because the -PCA can be sensitive to outliers, as the equivalent clustering model is not robust to outliers. To overcome this limitation, we introduce a trimming function to the clustering model and propose a trimmed-clustering based -PCA (TC-PCA). With this trimming set formulation, the TC-PCA is not sensitive to outliers. Besides, we mathematically prove the convergence of the proposed algorithm. Experimental results on image classification and clustering indicate that our proposed method outperforms the current state-of-the-art methods.

Authors:
Xu, L., Tang, M. L., Chen, Z.

Abstract:
In longitudinal data analysis, it is crucial to understand the dynamic of the covariance matrix of repeated measurements and correctly model it in order to achieve efficient estimators of the mean regression parameters. It is well known that any incorrect covariance matrices can result in inefficient estimators of the mean regression parameters. In this article, we propose an empirical likelihood based method which combines the advantages of different dynamic covariance modeling approaches. The effectiveness of the proposed approach is demonstrated by an anesthesiology dataset and some simulation studies.