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Manpower is the most valuable asset in the construction industry. Based on an examination of literature, selected key data sources, and views from 29 key informants, this paper addresses the important labour resource context related to the construction industry in the case of Hong Kong. These include the trends of the critical indicators of the labour market in construction and the implications of the changing markets and technology on the future pattern of skill requirements, and the government policies on construction personnel. The findings are of immense importance to anyone involved in the construction industry, particularly training organizations and policy makers in their mission to maintain a skilled, competitive and adequate workforce able to meet the future demands of the industry. The changing labour market trends and skill requirements pose challenges for construction personnel in terms of upgrading their skills. Further research is recommended to construct robust models predicting the occupational trends in labour resources for effective manpower planning and to establish a labour market information system which could lead to capturing periodic labour market signals with a view to assisting the process of policy making on various human resource development aspects of construction workforce in Hong Kong.

Efficient manpower planning has been recognized as a critical aspect for the development of an economy. In 2001, the Works Bureau of the Hong Kong SAR Government (predecessor of Environment, Transport and Works Bureau) commissioned an HKPolyU consultancy team to develop a computer‐based model to estimate the demand for different categories of construction personnel. This article presents the concept and features of the manpower demand‐forecasting model developed for the construction industry of Hong Kong. The forecasting model is formulated on the basis of the labour multiplier approach by deriving the relationship between the number of workers required and the project expenditure in the given project duration. Multipliers for 61 project types were derived for 38 labour trades using completed project data. The labour demand by occupation for each project can then be estimated by multiplying the corresponding multipliers and the estimated project expenditure. Several unique features of the model have been developed, including “normalization” and “contract cost adjustment factor”. Normalizing the labour multipliers can facilitate the prediction of occupational labour requirements at different stages of a construction project. The adjustment factor is introduced to eliminate the discrepancy between the original estimates and final contract values so as to enhance the estimation accuracy. The model can also be used to predict the number of jobs created for a given level of investment. The government can apply this model to check and compare which project types will generate most jobs before committing public money. This model could be easily adopted and adapted by foreign construction authorities while planning manpower.

This study investigates the impact of insider-ownership of publicly traded firms on their performance, cost of debt (COD) and cost of equity. We use a sample of 104 non-finance listed companies of Pakistan for the period from 2006 to 2016. Our study is conducted in Pakistan as a developing country in which insider-ownership is dominant, and a weak external corporate governance mechanism increases the payoffs from insider-ownership. We use feasible generalized least square (FGLS) regression methods to examine these hypotheses. Based on agency theory, we find that insider-ownership enhances firm performance. Furthermore, our results show that insider-ownership reduced the COD and equity. Higher ownership decreases the opportunistic behavior of insiders. It also reduces the creditor’s perception of the likelihood of default on loan payments and reduces agency issues among shareholders. The insider will invest in positive NPV projects which will help maximize shareholders’ wealth and minimize the COD. Similarly, the relationship between insider-ownership and cost of equity is significant but negative. Supporting the convergence of interest increase in ownership helps in aligning the goals of managers and stakeholders whereby the insider will focus on value creation by minimizing equity cost.

This paper aims to propose the new incremental and parallel training algorithm of proximal support vector machines (Inc-Par-PSVM) tailored on the edge device (i.e. the Jetson Nano) to handle the large-scale ImageNet challenging problem.

The Inc-Par-PSVM trains in the incremental and parallel manner ensemble binary PSVM classifiers used for the One-Versus-All multiclass strategy on the Jetson Nano. The binary PSVM model is the average in bagged binary PSVM models built in undersampling training data block.

The empirical test results on the ImageNet data set show that the Inc-Par-PSVM algorithm with the Jetson Nano (Quad-core ARM A57 @ 1.43 GHz, 128-core NVIDIA Maxwell architecture-based graphics processing unit, 4 GB RAM) is faster and more accurate than the state-of-the-art linear SVM algorithm run on a PC [Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM].

The new incremental and parallel PSVM algorithm tailored on the Jetson Nano is able to efficiently handle the large-scale ImageNet challenge with 1.2 million images and 1,000 classes.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

Tracheobronchial stents are commonly used in airway management as a form of palliation for obstruction. This form of therapy immediately relieves the patients from life‐threatening conditions and significantly improves their quality of life. In the cases of complex tracheobronchial obstruction, customised airway stents are required for effective palliation. In this work, an Airway Stent Customisation Protocol (ASCP) is introduced.

It describes two variant routes that use a combination of rapid prototyping (RP) and rapid tooling (RT) techniques to fabricate customised airway stents in short lead times. The ASCP allows the stents to be tailored in terms of geometry, and distending strength. A brief comparison between the ASCP and other RP/RT manufacturing routes is also carried out.

The ability to customise airway stents in short lead times is important as it allows surgeons to swiftly treat life‐threatening conditions arising from tracheobronchial obstructions. It is shown that the ASCP is capable of providing relief to patients quickly. The application of RP and RT in the ASCP has not only allowed shorter response time to patients, but has also allowed the stents to be produced at a relatively low cost.

Tracheobronchial stents are commonly used in patients facing advanced stages of cancer. Focuses on a time and cost‐effective solution that is provided to improve their quality of life.

This paper examines the inter‐relationship between profitability, cost of capital and capital structure among property developers and contractors in Hong Kong. Whilst major indigenous local developers are among the largest and the most profitable in the world, their contractor counterparts are generally small and nowhere near as profitable. An analysis of financial data suggests that gearing is generally higher among contractors than developers. However, it does not mean that contractors borrow more than developers. Indeed they do not need to borrow as much as developers even if they have the assets to pledge as collateral. Contractors do not have to pay for high land costs, and they obtain project finance from developers through interim payments in lump sum contracts that are widely adopted in the industry. Their high gearing reflects more their low equity base than high level of debts. Their costs of equities are about double the developers’, probably due to their usually low or negative profit margins. This conclusion is substantiated by further regression analysis of the data. The findings indicate that capital gearing is positively related with asset but negatively with profit margins. This article concludes with a discussion on implications of such profitability divide between the two sectors on the unequal relationship between developers and contractors, and on their competitiveness.

The purpose of this paper is to study the user experiences of the futures wheel method to investigate its suitability to advance futures thinking in the real estate field.

The user experiences of the futures wheel method are investigated through questionnaire answers of 114 master’s level students and real estate experts taking part in future wheel workshops.

The futures wheel method could enhance future-oriented thinking and decision-making in the real estate field. The respondents see futures thinking as an important skill and recognize several advantages concerning the method.

The futures wheel method bears great potential to be used in the real estate sector and it could be a fruitful addition to the curriculums at different education levels in real estate studies.

Futures thinking is essential when aiming for sustainable decisions in the real estate field which again would benefit the whole surrounding society.

This paper is the first published paper concentrating on the user experiences of the future wheel method in the real estate sector. The benefits and the disadvantages of the method are investigated but also the attitudes indicating the potential of the method to be successfully adopted in the field are analyzed.

The purpose of this paper is to examine the problem of rate allocation (RA) in computer networks in cases when some parameters are unknown or their values are imprecise.

The application of uncertain variables for the RA problems in computer networks in the presence of uncertainty is proposed.

Decision‐making problem formulations for RA in computer networks with unknown parameters of the utility functions and bandwidths based on the network utility maximization concept are given. Solution algorithms for all these problems are proposed.

It is assumed that an expert can describe possible values of unknown network parameters in the form of a certainty distribution. Then, the formalism of uncertain variables is applied and the knowledge of an expert is modelled with certainty distributions.

The RA algorithms obtained can be useful for designing and planning computer networks.

The new approach to the RA problem in computer networks in the presence of uncertainty, in cases when the probabilistic approach cannot be applied, is proposed and discussed.