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This paper aims to examine the dynamics between Hong Kong’s domestic sporting needs and its regional aspirations as a “hub” for sport and culture, which have created challenges and contradictions for the optimal provision of relevant infrastructure. These have become particularly evident during the COVID-19 pandemic, when local restrictions have undermined Hong Kong’s appeal as an event destination and hindered access and utilization of venues. In recent years, policies in this area have mainly focused on the development of a new sports park on the former airport runway in Kai Tak, which has acquired additional significance in the city’s quest for post-pandemic economic recovery. Simultaneously, any noncommercial land use in Hong Kong, one of the most densely populated cities in the world, faces intense scrutiny over a perceived scarcity of space.

By drawing upon concepts from urban studies and policy studies, the paper explores a presumed preference for commodified sporting landscapes and provides an interdisciplinary approach to enhance the author’s understanding of sport policy and infrastructure. This is achieved through direct comparisons of two case studies, and by building on and expanding a multidimensional evaluative framework of sustainability that can avoid economic reductionism.

This paper finds that Hong Kong’s sport policy framework exhibits unbalanced consideration when it comes to the planning and development of relevant infrastructure.

By acknowledging the interrelatedness and similarity between sports and culture, the paper may further test the adaptability of cultural policy concepts for the analysis of Hong Kong’s sports policy. As such, it aims to bring the usually separated study of cultural and sport policy within a comparable framework.

Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors.

This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness.

Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers.

This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

The purpose of this paper is to study the pipe-type electromagnetic induction heating device under power frequency condition.

To reduce eddy current loss and improve heating efficiency, the structure of a pipe-type power-frequency electromagnetic heating device was optimized. Based on the maximum load flow formula, a parallel excitation winding structure is designed, and the distribution of electromagnetic field under four different powers is analyzed by simulation. Four heating modes were proposed according to the structure of diversion ring, inner wall and outer wall. Two heating modes with better heating effect were obtained by comprehensively considering the factors such as magnetic field distribution, thermal power and energy consumption.

The double-wall structure of the pipe-type electromagnetic heating device can make the heat source distribution more uniform, and the use of power-frequency power supply can increase security, the installation of diversion ring can make the heating more sufficient and the heating efficiency of the two heating methods selected according to the structural performance is more than 90%.

In view of the medium or high frequency of pipe-type electromagnetic heating device, it is necessary to configure high power electronic frequency conversion drive system, and eddy current can only be produced on the tube wall, resulting in uneven distribution of heat sources. A pipe-type power-frequency electromagnetic heating device with double-wall structure was proposed.

This paper identifies and maps themes and critiques emerging from feminist-informed and gender-focused mainstream entrepreneurship and social entrepreneurship (SE) literature. We aim to develop a robust, theoretically informed gender-aware framework for future SE research.

A five-stage literature review process was undertaken to identify the common themes, key knowledge gaps and areas of contention highlighted by feminist critiques of mainstream entrepreneurship and what we refer to here as the “critical SE literature”. We draw on a widely used framework of epistemological approaches to discuss the emergent themes, specifically utilising three dominant epistemologies previously found to map onto entrepreneurship policy, those being: “gender as variable”, feminist standpoint and feminist poststructuralist positions.

We find feminist perspectives on SE particularly distinctive for their high reliance on relational and embedded accounts of the economy, more attentive to collectivist themes like “belongingness” in the performance of gender and identity, and maintaining a challenge to neoliberal and postfeminist norms. Anchoring each perspective in their respective epistemologies, we demonstrate their lineage in critiques of mainstream entrepreneurship and critical (non-feminist) SE. However, divergences from these lineages lead us to identify diversely contextualised and value-adding feminist accounts of SE that add richness and nuance to mainstream entrepreneurship theorising.

The relative “youth” of the gender-focused and feminist-informed SE literature, alongside the theoretical relevance of the feminist critique of mainstream entrepreneurship and critical social entrepreneurship literature, lead us to draw on the strengths of a more interpretivist paradigm in our research. This amplifies our ability to support the revelation of conceptual connections and relationships between emergent themes, theories, epistemologies and approaches drawn from multidisciplinary sources and to thereby enhance the reach, and associated value, of our research findings.

This review responds to calls for a gender-aware framework for social entrepreneurship research by (1) developing a set of original emergent themes, (2) tracing their intellectual lineage in associated research fields and (3) using them to deepen the connection between feminist epistemologies and entrepreneurship concepts.

A popular production model for high-tech manufacturers is that they move most production lines abroad to produce formal products for sale and just keep a few production lines in headquarters to manufacture sample products for new product development. Under such a production model, the paper aims to develop a selection model of International Air Express (IAE) for high-tech manufacturers in airfreight of sample products using the fuzzy best-worst method (BWM).

In this paper, an assessment model based on the fuzzy BWM approach is proposed for high-tech manufacturers in selecting airfreight carriers for the shipping of sample products. Further, one high-tech electronic manufacturer in Taiwan was empirically investigated to validate the assessment model.

The result indicates that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for the transportation of sample products. Based on the findings, some practical management implications were discussed.

Some literature limitations should be addressed. Initially, the adoption of the fuzzy BWM assumes independence among criteria. Nonetheless, this assumption is not yet to confirm in this study. Accordingly, this limitation leaves room for improvement in future studies. Further, in this paper, five experienced experts from the Radiant Opto-Electronics Corporation (ROEC) case were empirically surveyed. To ensure the validity of the surveying, this paper adopted an interviewing survey instead of a traditional mailed survey. However, more representative samples are still necessary to confirm the empirical results in future research.

Firstly, the proposed research model provides a systematic framework to the decision-making process, which assists high-tech manufacturers in identifying the most suitable IAEs based on multiple criteria. It has been illustrated that high-tech companies deliver their sample products requiring timely and secure means of transport. In practice, manufacturers can assess various IAEs considering some main factors, such as Operational Flexibility (OF), Partner Relationship (PR), Transportation Capability (TC) and Management, using fuzzy BWM. This process ensures the selection of IAEs aligning with their logistical needs and business priorities, ultimately enhancing operational efficiency and customer satisfaction. Secondly, empirical results from the ROEC case indicate that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for transportation of sample products. In other words, ROEC should consider establishing long-term contracts with preferred IAEs (i.e. FedEx) to secure favorable rates and service commitments. On top of that, results not only provide practical information for manufacturers in selecting IAEs but also for IAE partners to improve their service policies.

The results not only provide practical information for high-tech manufacturers in selecting airfreight carriers but also for the airfreight carriers to improve their service quality.

Ever since China’s implementation of the open-door policy in 1978, there has been a remarkable transformation in the nation’s economic landscape. Undesirably, amidst the rapid urban development, the importance of prioritising and nurturing rural development in China has not received unwavering attention. Nevertheless, the Chinese government has embarked on many ventures to bridge the disparities existing amidst urban and rural areas, revitalise the rural economy, and enhance overall productivity. This paper enunciates the role of the Chinese government in prospering rural areas by implementing policies that align with the Sustainable Developmental Goals (SDGs)- 1, 2 and 12.

This study employed a comprehensive methodology encompassing both primary and secondary research techniques to procure valuable insights and reviewed various Chinese government policies pertaining to rural revitalisation.

The study results demonstrate that throughout the policy implementation, China has contributed to the livelihoods of the rural communities and achieved SDG-1 (ending poverty) by 2030, ten years ahead of Agenda (2030). The country has also substantially improved its rural agricultural system by integrating modern science and technology and aiming to achieve SDG-2 (ensure food security) with the alignment of SDG-12 (sustainable production and consumption). The findings of this research indicate that despite some limitations in China’s rural revitalisation strategy, overall progress is seen in many aspects, particularly in achieving SDG-1, 2, and 12.

The Chinese government has made significant efforts to promote ecological, social, and economic development in rural areas through various national initiatives such as the “New Countryside” and “Rural Revitalisation” strategies. These initiatives have successfully alleviated poverty, increased food production, and ensured sustainable production and consumption. The discoveries presented within this article possess immense value, as they provide profound insights for policymakers, rural planners, and researchers who are fervently searching for viable solutions to tackle the intricate interplay between rural development and sustainability. Therefore, this study has the potential to greatly benefit policymakers from various nations, as they can adopt China’s rural revitalisation model as a means to successfully achieve SDGs 1, 2, and 12.

This study found that despite numerous initiatives to improve rural landscapes, China’s rural revitalisation approach still poses concerns as local governments are likely to focus on increasing income capacity rather than concentrating on establishing environmental governance.

Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud.

This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments.

The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision.

Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.