Unlocking innovation in the sport industry through additive manufacturing

Fast changing customer demands and rising requirements in product performance constantly challenge sports equipment manufacturers to come up with new and improved products to stay competitive. Additive Manufacturing (AM), also referred to as 3D Printing, can enhance the development of new products by providing an efficient approach of rapid prototyping. This research aims to analyse the current adoption of AM technologies in the innovation process of the sports industry i.e. level of awareness; how it is implemented; and it impact on the innovation process. Literature research shows that AM brings many possibilities to enhance the innovation process, and case studies indicated several obstacles that hinder the technology from fully unfolding. AM is still at the early stage of entering the sports equipment industry and its potential benefits have not been fully exploited yet. The findings generated from the research of real life practices show that AM provides several benefits when it comes to the innovation process, such as a faster development process, an optimised output, as well as the possibility to create new designs. However, companies are not yet able to enhance the innovation process in a way that leads to new products and new markets with AM. Limitations, including a small range of process able material and an inefficient mass production, still restrain the technology and lead to unused capability. Nevertheless, future prospects indicate the growing importance of AM in the innovation process and show that its advancement paves the way to new and innovative products

Creating a low carbon economy through green supply chain management: investigation of willingness-to-pay for green products from a consumer’s perspective

This study investigates how consumers’ willingness-to-pay (WTP) for green products affects the decisions made by the green supply chain players. Through the application of game theory and uncertainty theory, our findings show that a higher consumer WTP for green products usually leads to a higher retail price and market share of green products, which motivates retailers and manufacturers to invest more in green technology. We also find that an increased WTP for green products can spur retailers to reduce the optimal green cost-sharing rate due to the pressure of increasing costs. In addition, we find that retailers are willing to lower the cost sharing rate when the confidence level increases. Regarding the contributions made by this study, it is one of the first to explore the transmission mechanisms involved in the management of the green supply chain by linking consumers’ WTP for green products to strategic decisions made by green supply chain players under conditions of uncertainty. Furthermore, our study could help green supply chain players to optimise the cost sharing mechanisms they use to generate more revenue, due to the increase in WTP for green products, which will in turn help to facilitate a low carbon economy

An explorative study of interface support for image searching

In this paper we study interfaces for image retrieval systems. Current image retrieval interfaces are limited to providing query facilities and result presentation. The user can inspect the results and possibly provide feedback on their relevance for the current query. Our approach, in contrast, encourages the user to group and organise their search results and thus provide more fine-grained feedback for the system. It combines the search and management process, which - according to our hypothesis - helps the user to onceptualise their search tasks and to overcome the query formulation problem. An evaluation, involving young design-professionals and di®erent types of information seeking scenarios, shows that the proposed approach succeeds in encouraging the user to conceptualise their tasks and that it leads to increased user satisfaction. However, it could not be shown to increase performance. We identify the problems in the current setup, which when eliminated should lead to more effective searching overall

Carbon emissions in China's thermal electricity and heating industry: An input-output structural decomposition analysis

CO2 emissions from China accounted for 27 per cent of global emisions in 2019. More than one third of China's CO2 emissions come from the thermal electricity and heating sector. Unfortunately, this area has received limited academic attention. This research aims to find the key drivers of CO2 emissions in the thermal electricity and heating sector, as well as investigating how energy policies affect those drivers. We use data from 2007 to 2018 to decompose the drivers of CO2 emissions into four types, namely: energy structure; energy intensity; input-output structure; and the demand for electricity and heating. We find that the demand for electricity and heating is the main driver of the increase in CO2 emissions, and energy intensity has a slight effect on increasing carbon emissions. Improving the input-output structure can significantly help to reduce CO2 emissions, but optimising the energy structure only has a limited influence. This study complements the existing literature and finds that the continuous upgrading of power generation technology is less effective at reducing emissions and needs to be accompanied by the market reform of thermal power prices. Second, this study extends the research on CO2 emissions and enriches the application of the IO-SDA method. In terms of policy implications, we suggest that energy policies should be more flexible and adaptive to the varying socio-economic conditions in different cities and provinces in China. Accelerating the market-oriented reforms with regard to electricity pricing is also important if the benefits of technology upgrading and innovation are to be realised

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

General analysis of lepton polarizations in rare B -> K^* l^+ l^- decay beyond the standard model

The general analysis of lepton polarization asymmetries in rare B -> K^* l^+ l^- decay is investigated. Using the most general, model independent effective Hamiltonian, the general expressions of the longitudinal, normal and transversal polarization asymmetries for l^- and l^+ and combinations of them are presented. The dependence of lepton polarizations and their combinations on new Wilson coefficients are studied in detail. Our analysis shows that the lepton polarization asymmetries are very sensitive to the scalar and tensor type interactions, which will be very useful in looking for new physics beyond the standard model.Comment: 29 pp, 15 figure

Hybrid flow shop scheduling problems using improved fireworks algorithm for permutation

Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm’s performance, and the computational results demonstrate the improved fireworks algorithm’s superiority

An iterative agent bidding mechanism for responsive manufacturing

In today's market, the global competition has put manufacturing businesses in great pressures to respond rapidly to dynamic variations in demand patterns across products and changing product mixes. To achieve substantial responsiveness, the manufacturing activities associated with production planning and control must be integrated dynamically, efficiently and cost-effectively. This paper presents an iterative agent bidding mechanism, which performs dynamic integration of process planning and production scheduling to generate optimised process plans and schedules in response to dynamic changes in the market and production environment. The iterative bidding procedure is carried out based on currency-like metrics in which all operations (e.g. machining processes) to be performed are assigned with virtual currency values, and resource agents bid for the operations if the costs incurred for performing them are lower than the currency values. The currency values are adjusted iteratively and resource agents re-bid for the operations based on the new set of currency values until the total production cost is minimised. A simulated annealing optimisation technique is employed to optimise the currency values iteratively. The feasibility of the proposed methodology has been validated using a test case and results obtained have proven the method outperforming non-agent-based methods

Using neighborhood rough set theory to address the smart elderly care in multi-level attributes

The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption