Search results

This study aims to develop a four-dimensional (4D) textile composite that self-forms upon thermal stimulation while eliminating thermomechanical programming steps by using fused deposition modeling (FDM) 3D printing technology, and tries to refine the product development path for this composite.

Polylactic acid (PLA) printing filaments were deposited on prestretched Lycra-knitted fabric using desktop-level FDM 3D printing technology to construct a three-layer structure of thermally responsive 4D textiles. Subsequently, the effects of different PLA thicknesses and Lycra knit fabric relative elongation on the permanent shape of thermally responsive 4D textiles were studied. Finally, a simulation program was written, and a case in this study demonstrates the usage of thermally responsive 4D textiles and the simulation program to design a wrist support product.

The constructed three-layer structure of PLA and Lycra knitted fabric can self-form under thermal stimulation. The material can also achieve reversible transformation between a permanent shape and multiple temporary shapes. Thinner PLA deposition and higher relative elongation of the Lycra-knitted fabric result in the greater curvature of the permanent shape of the thermally responsive 4D textile. The simulation program accurately predicted the permanent form of multiple basic shapes.

The proposed method enables 4D textiles to directly self-form upon thermal, which helps to improve the manufacturing efficiency of 4D textiles. The thermal responsiveness of the composite also contributes to building an intelligent human–material–environment interaction system.

This study aims to examine the human resources (HR) knowledge and cross-cultural and interpersonal skills needed by HR managers/directors to administer the integrated resort and hotel (IR&H) organizational talent.

This paper uses a qualitative method in the form of extensive interviews.

The labor-intensive nature of the IR&Hs have forced these organizations to be innovative in terms of retaining their top talents and to look overseas for foreign workers to ensure smooth operations.

The research is limited to two Asian geographies and the high regulatory nature of the industry correspondingly limits the number of HR executives in operation within IR&Hs.

This paper provides insights for current and future Asian-based IR&Hs policy planners and HR managers.

The paper provides an understanding of national and organizational skills shortages and their impact upon work and workers.

No previous comparative research in relation to Singapore and Macau has been conducted.

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

In both the United States and the United Kingdom, a series of surveys of the mental health of children and, in particular, adolescents have suggested that there appear to be significant increases in measured levels of anxiety and depression among more recently‐born populations. Here, 16 studies are selected of children in North America, which adds to the body of evidence suggesting that rates of depression among adolescent girls do appear to be rising (p=0.024) to rates of above one in seven suffering in the most recent of surveys, as opposed to almost seven times fewer being depressed among their mothers' generation. The results for boys also show a rise, but not yet significant at the p<0.05 level (p=0.108). These studies are taken from a wider worldwide set, which, in aggregate, do not show a sustained rise. In the worldwide set of studies, most of the more recent surveys have been taken in more equitable affluent countries away from North America or the United Kingdom. By inference, this review suggests that it is the particularly competitive and divisive social environments of North America and the United Kingdom that may well have led to levels of anxiety rising for children in countries in these regions more significantly than elsewhere in affluent countries. Geography appears to matter to children's mental health. The review begins and ends by raising concerns over the possible effects of the current economic crash given this social context, and the political desire to return to economic business as usual.

This paper aims to present the case for a new integrated resort business model. It does so by integrating the notion of a smart tourism ecosystem into the concept of dynamic capabilities so as to guide integrated resort theory and practice in an evolving landscape.

The theoretical model is derived from synthesising the gaps surrounding literature that contextualises business models and the relatively scarce body of knowledge related to integrated resort business models.

Integrated resorts are characterised by strategies using a follower mentality. Destinations seeking to create new integrated resorts largely emulate existing product or service points of differentiation, though such attempts are often quickly eroded by competition. By integrating the smart tourism ecosystem to the theoretical framework, this conceptual paper elucidates how an information-rich environment can help to better realise sustainable competitive advantages.

This conceptual paper will require empirical data to validate the theoretical model. Implications stemming from the research will hasten greater social networks that need to be incorporated to foster timely and necessary circulation of information to attain optimal outcomes generated by the dynamic capability effect.

This paper re-conceptualises the business model for integrated resorts. By integrating the notion of smart tourism ecosystems to business model literature, the paper illuminates how integrated resorts can be better positioned in adapting to the changing operating environments.

In this paper we present a corporate information system for untrained users to search gigabytes of unformatted data using quasi‐natural language and relevance feedback queries. The data can reside on distributed servers anywhere on a wide area network, giving the users access to personal, corporate, and published information from a single interface. Effective queries can be turned into profiles, allowing the system to automatically alert the user when new data are available. The system was tested by twenty executive users located in six cities. Our primary goal in building the system was to determine if the technology and infrastructure existed to make end‐user searching of unstructured information profitable. We found that effective search and user interface technologies for end‐users are available, but network technologies are still a limiting cost factor. As a result of the experiment, we are continuing the development of the system. This article will describe the overall system architecture, the implemented subset, and the lessons learned.

Water heating is one of the major energy‐consuming operations in the lodging sector. The purpose of the current study is to estimate the energy consumed and emission associated with hot water usage, to predict the energy cost required under different hot water systems in hotels; and to create a model for the decision‐making criteria in selecting hot water systems.

A total of 24 hotels, which use heat pumps as their main water heating systems, were investigated. A tailor‐made model for estimating the energy requirement of the water heating system was employed. Comparative studies on the energy consumption and energy costs of various types of water heating systems, including heat pumps, diesel boilers, gas boilers, and electric boilers, were conducted. Moreover, an analytic hierarchy process was used to analyze hoteliers' and lenders' selection criteria for water heating facilities.

The energy output for water heating by heat pumps was estimated at 15 GW in the lodging sector. The use of heat pumps can achieve substantial energy savings and reduction of air pollutants when compared with the energy requirements under conventional boilers. The latter accounts for 13 percent of the hoteliers' total decision weight on choosing water‐heating systems. Whereas the air pollutants generated by gas‐fired boilers are remarkably lower than those emitted in the power plants due to the use of heat pumps. Both bankers and hoteliers consider seriously the energy saving potential of hot water supply by trucks.

Due to the small number of decision‐makers in hotels participating in the analytic hierarchy process, the result can only provide an indication of the overall picture of the selection criteria adopted by hoteliers.

The analysis provides hotel owners and managers with an objective and scientific investigation of the emission prediction and energy cost estimation based on the use of different hot water systems. Hotel operators and owners can use the analytical results as reference for making green purchasing decisions.

The current study, which is based on the operational experiences of existing hotels, is a collaborative work between hospitality industry practitioners and educators. It is also the first of its kind to indicate the emission impact of various types of hotel water heating systems and the perspectives of hoteliers and bankers on these systems.

The purpose of this study is to examine how the lodging market and the state economy affected by Hurricane Sandy have recovered from the damages sustained. Specifically, this study examines and predicts the influence of revenue management key performance indicators (KPIs) on recovery and lodging revenue in the affected states and the states’ economies. These KPIs include average daily rate (ADR), occupancy and revenue per available room (RevPAR).

Secondary financial data were collected for the states most damaged by Hurricane Sandy. Subsequently, pooled Ordinary Least Square (OLS) regression was conducted combining time and non-time dependent variables based on the states and radius from the landfall.

The results indicate that although the lodging market and the state economies have recovered since the onslaught of Hurricane Sandy, certain KPIs still need to improve.

Managerial implications are suggested in terms of dynamic pricing, market-based recovery, the KPIs, federal aid and facility management.

Despite its importance, research on the effects of climate change in the hospitality context has not actively progressed after Hurricane Katrina. Time and non-time dependent variables are combined in this analysis to gain a richer understanding of the impacts and recovery of KPIs on the revenue in the lodging market and the revenue on states’ economies. Additional analysis based on the radius from the landfall of the hurricane was performed to examine the impact and recovery based on geographical proximity.