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The purpose of this study is to delineate the factors influencing the use of financial derivatives by non-financial firms in managing their exchange rate exposure. In total, 219 non-financial firms are surveyed in regard to their financial hedging decision.

This study is conducted via a survey and the questionnaires were sent to the treasurers and financial controller of the firms. Descriptive analysis is employed to assess the profiles of the respondents. Then, factor analysis is carried out to determine the factors influencing the use of financial derivatives in Malaysia.

The results indicate that the hedging decision of non-financial firms is influenced by their assertive level toward the market and regulators and also how flexible they are for derivative instruments. The intellectual capability that firms acquire to perform hedging strategies is also vital in influencing them to make hedging decision.

The insights of this survey would assist and prepare firms to hedge their exchange rate risk by employing financial derivatives. Knowing the influences of firms' adoption of currency derivatives would allow policy makers to formulate their policies in boosting the liquidity of Malaysian derivative market.

This study presents findings on the factors influencing the execution of financial hedging by non-financial firms in Malaysia. Survey data are used to seek for the feedback from the market players in order to provide empirical evidence on the corporate use of financial hedging.

The purpose of this paper is to construct a new algorithm of program procedure for medical grey relational method based on SAS software.

Based on the SAS environment, the authors construct a new algorithm of program procedure through the following methods: the construction data set, confirmation of the comparison sequence and reference sequence, the original data transformation, calculation of the grey relational coefficient of reference sequence and comparison sequence and calculating the correlation.

The results show that the novel algorithm of program procedure for medical grey relational method based on SAS software satisfies the properties properly. It also fully confirmed the biggest advantage of the grey relational analysis is that its requirements are not too high for the amount of data, and it does not need to follow the typical distribution.

The paper succeeds in constructing a novel algorithm of program procedures for medical grey relational method and providing a valuable tool for solving similar problems.

The purpose of this paper is to develop a statistical-based model to forecast future domestic water demand in the context of climate change, population growth and technological development in Yellow River.

The model is developed through the analysis of the effects of climate variables and population on domestic water use in eight sub-basins of the Yellow River. The model is then used to forecast water demand under different environment change scenarios.

The model projected an increase in domestic water demand in the Yellow River basin in the range of 67.85 × 10⁸ to 62.20 × 10⁸ m³ in year 2020 and between 73.32 × 10⁸ and 89.27 × 10⁸ m³ in year 2030. The general circulation model Beijing Normal University-Earth System Model (BNU-ESM) predicted the highest increase in water demand in both 2020 and 2030, while Centre National de Recherches Meteorologiques Climate Model v.5 (CNRM-CM5) and Model for Interdisciplinary Research on Climate- Earth System (MIROC-ESM) projected the lowest increase in demand in 2020 and 2030, respectively. The fastest growth in water demand is found in the region where water demand is already very high, which may cause serious water shortage and conflicts among water users.

The simple regression-based domestic water demand model proposed in the study can be used for rapid evaluation of possible changes in domestic water demand due to environmental changes to aid in adaptation and mitigation planning.

The purpose of this paper is to propose a design of online-merge-offline (OMO) classroom for open education with design principles related to practical issues of teachers’ teaching, students’ learning and schools’ management.

Three stages were covered: drafted an OMO classroom framework, built a sample classroom and explored end-users’ experience. First, authors searched for and reviewed previous studies and related cases to draft an OMO framework. Second, a classroom, consisted of wireless devices, cloud-based services, Internet of Things terminals, ergonomics furniture, and comprehensive data management and analysis services, was built in Shanghai Open University. Third, invited 11 students’, 18 teachers’ and 9 school managers’ perspectives were collected and analysed by surveys and interviews.

All student participants responded positively in terms of learning experience in the classroom. They not only engaged in classroom activities such, but also accessed needed learning materials and interacted with teachers and peers anytime anywhere via mobile devices. Similarly, most teachers (90 per cent) made positive responses because of flexibility of teaching strategies and learning activities and expressed willingness to use the classroom in the future (94.4 per cent). In addition, more than 78 per cent of managers positively commented on the design of classroom, interaction effects and effective management. Visualised data allowed them to timely monitor status of facilities, comprehensively understand users’ behaviour and issues, make necessary decision with scientific evidence.

The framework and classroom not only provide teachers, students, school managers and researcher with a better understanding of innovative open education, but also indicate the key role of objective-oriented and data-driven issues for further work.

To meet needs of teachers, students, managers and researchers in today’s open education, an OMO classroom was built in Shanghai Open University based on the proposed Objective-Oriented Pedagogy-Space-Technology (OPST) framework. The framework provides readers (especially teachers and administrators of open-education institutes, staff of information centres and ed-tech researchers) with a better understanding of innovative instruction and effective management, and the originally designed classroom can be a practical and illuminating example.

This paper aims to investigate the conformable fractal approaches of unsteady natural convection in a partial layer porous H-shaped cavity suspended by nano-encapsulated phase change material (NEPCM) by the incompressible smoothed particle hydrodynamics method.

The partial hot sources with variable height L_Hot are in the H-cavity’s sides and center. The performed numerical simulations are obtained at the variations of the following parameters: source of hot length L_Hot = (0.4–1.6), conformable fractal parameter α (0.97–1), fusion temperature θ_f (0.05–0.9), thermal radiation parameter Rd (0–7), Rayleigh number Ra (10³–10⁶), Darcy parameter Da (10⁻² to 10⁻⁵) and Hartmann number Ha (0–80).

The main outcomes showed the implication of hot source length L_Hot, Rayleigh number and fusion temperature in controlling the contours of a heat capacity within H-shaped cavity. The presence of a porous layer in the right zone of H-shaped cavity prevents the nanofluid flow within this area at lower Darcy parameter. An increment in the thermal radiation parameter declines the heat transfer and changes the heat capacity contours within H-shaped cavity. The velocity field is strongly enhanced by an augmentation on Rayleigh number. Increasing the Hartmann number shrinks the velocity field within H-shaped cavity.

The novelty of this work is solving the conformable fractal approaches of unsteady natural convection in a partial layer porous H-shaped cavity suspended by NEPCM.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.