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Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

This paper aims to develop a quality evaluation model for artificial intelligence (AI)-based products/services that is applicable to startups utilizing AI technology. Although AI-based service has risen dramatically and replaced many service offerings, in reality, startups are rarely to develop and evaluate AI services. The features of AI service are fundamentally different from the properties of existing services and have a great influence on the customer's service selection.

This paper reviews startups' development process, existing quality evaluation models and characteristics of services utilizing AI technology, and develops a quality evaluation model for AI-based services. A detailed analysis of a survey (application of the model) on customer satisfaction for AI speakers is provided.

This paper provides seven key features and 24 evaluation items for evaluating AI-based services.

This paper contributes to the growing need for methodologies that reflect the new era of AI-based products/services in quality evaluation research.

The purpose of this paper is to attempt to analyze the situation and trend of China's energy consumption structure and predict it.

Starting from the situation of China's energy consumption structure, a quadratic programming model is created to analyze the trend of it. A homogeneous Markov chain is chosen to predict China's energy consumption structure with the data collected from China's Statistical Yearbook. Finally, the implication of the prediction is explained.

The results are convincing: the substitution of different energies are found, China will not enter the oil era, natural gas and non‐fossil energy will rapidly develop.

The results of this article can provide an important basis for the government to make a non‐fossil energy development plan and energy policies.

The paper succeeds in revealing and predicting China's energy consumption structure by quadratic programming and homogeneous Markov chain.

The paper attempts to design an efficient algorithm for bearing track correlation of multi‐sensor on the same platform using grey incidence analysis which is on the basis of the line segment Hausdorff distance.

Starting from the line segment, Hausdorff distance that has been extended to calculate the distance between line segment sets by many scholars has been used for face recognition achieving good results. The degree of grey incidence is defined based on the above distance and properties which include normality, symmetry and closeness, are proved. Furthermore, a grey incidence matrix is built. With only the azimuth information detected by bearing sensors track correlation is difficult to judge, however grey incidence analysis can quickly and accurately determine whether two tracks are from the same target, and so an algorithm is designed to solve this dilemma. In the last part of the paper simulation experiment is conducted.

The results are convincing: not only the algorithm proposed in the paper can solve the problem of track correlation of bearing‐only sensors, but also the algorithm can judge the correlation degree of both tracks even in the case of intensive targets.

The method exposed in the paper can be used to judge correlation degree of tracks detected by different sensors even for less information, and also be used to determine the similarity of two waveforms in the field of engineering.

The paper succeeds in introducing the line segment Hausdorff distance into grey incidence analysis and on the basis of that an algorithm is designed to solve the problem of track correlation.

The purpose of this paper is to analyze and summarize the development of science and technology (S & T) policies in China from a government-driven perspective in chronological order. To develop knowledge-based economy, China enacts a range of S & T policies since “Reform and Open Policy” started in 1978. Furthermore, it investigates the overall effects of these S & T policies on university-industry linkages (UILs).

This paper conducts an analysis framework of S & T policies in historical sequence to explain how government drives UILs to stimulate technological progress and economic growth in China.

More than a site for high-quality workforce education and knowledge spread, universities as an important part of national innovation are required to participate in economic activities. Considering that most Chinese universities are national, S & T policies with particular regard to university technology transfer would be more important and essential. This research finds that S & T policies enacted by government have made critical contributions to UILs in economic transition period, such as improving academic faculty, enhancing university–industry collaborations and supporting university spin-off formation. The experiences of China suggest that government should enact more effective S & T policies in the knowledge-based economy era.

First, universities need to educate high-level human resources that are important for economic growth and social development. Second, universities need to engage in R & D activities and enhance their collaboration with industries, such as consulting services, research contracts with industry, patent licensing and other general knowledge commercial mechanisms. Third, universities also can directly transfer commercial knowledge to start up new businesses by itself or in partnership with industrial sectors. Without doubt, a series of S & T policies or programs enacted by China’s government to drive entrepreneurship continuously played critical role in the UILs over the past 26 years.

This paper is a pioneering work on how S & T policies enacted by government drive UILs to stimulate technological progress in transitional China.

The purpose of this paper is to describe a novel design method to construct lattice structure computational models composed of a set of unit cells including simple cubic, body-centered cubic, face-centered cubic, diamond cubic and octet cubic unit cell.

In this paper, the authors introduce a new implicit design algorithm based on the computation of volumetric distance field (VDF). All the geometric components including lattice core structure and outer skin are represented with VDFs in a given design domain. This enables computationally efficient design of a computational model for an arbitrarily complex lattice structure. In addition, the authors propose a hybrid method based on the VDF and parametric solid models to construct a conformal lattice structure, which is oriented in accordance with the geometric form of the exterior surface. This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

Experimental results are shown for a variety of geometries to validate the proposed design method along with illustrative several lattice structure prototypes built by additive manufacturing techniques.

This method enables the authors to design highly complex lattice structure, computational models, in a consistent design framework irrespective of the complexity in geometric representations without sacrificing accuracy and efficiency.

The purpose of this paper is to investigate the effect of empowering leadership on employees’ psychological well-being (PWB) and job engagement. This study also examines the mediating role of psychological capital (PsyCap) in these relationships.

The study draws on survey data (n=285) from employees in eight large-sized firms in South Korea. Confirmatory factor analysis was conducted to examine the convergent validity of the construct measures. Structural equation modeling was used to estimate the fit of the hypothesized model to the data.

The authors found that empowering leadership influenced job engagement both directly and indirectly through PsyCap. Employees’ PsyCap fully mediated the relationship between empowering leadership and employees’ PWB, while partially mediating the relationship between empowering leadership and job engagement.

This is the first empirical study to examine the effects of empowering leadership behaviors of leaders on both PWB and PsyCap, which are positive psychological resources of employees. The study has also provided empirical evidence for the importance of PsyCap, which plays a positive role in the ability of employees to manage their overall feelings in achievement situations and in employees’ engagement in the workplace.

This paper aims to enhance the responsiveness of enterprises with regard to delivery schedules in a supply chain. Based on the observations in industry, it seems that companies often re‐negotiate their due‐dates. These phenomena have begun to appear only recently, as a result of the advancements of information technology and flexibility in the supply chain. However, these due‐date re‐negotiation processes are haphazard and ad hoc in nature, and a formal process is needed to respond to market fluctuations more quickly. In this paper, a re‐negotiable order processing method is developed that can change the already‐assigned due‐dates or allow partial shipments to increase the flexibility of firm response to a rush order from a prior customer (PC).

A due‐date re‐negotiation process is defined and a formal system for handling customer orders is proposed. The new data and business process integration model are proposed for the due‐date re‐negotiation process based on RosettaNet's Partner Interface Processes. Web services and ubiquitous computing technologies are used in the proposed system architecture to allow for responsive sales order management.

The paper shows that assigned due‐dates can also be re‐negotiated by enhancing the connectivity and visibility of the supply chain with co‐operative customers. In the early stage of the product life cycle or high‐regional demand variation environments, it is better to re‐negotiate due‐dates than to meet scheduled due‐dates.

In some instances, meeting the due‐date requested by a customer relates to the success or failure of a particular sales order. The proposed re‐negotiation method could help the sales offices of the MF to respond to PC requests quickly and flexibly. Rush orders from PC might be acceptable based on the slack gained through the re‐negotiation of co‐operative customers' assigned orders.

There has been a considerable volume of studies on the due‐date assignment, order releasing, and scheduling problems. However, these previous studies considered due‐dates as exogenous parameters or fixed endogenous variables. In this paper, the assigned due‐dates for pre‐contracted co‐operative customers are considered as re‐negotiable variables. On the other hand, most studies on ubiquitous computing technologies, especially those relate to radio frequency identification (RFID), have focused mostly on asset management and processing time reduction. This paper suggests other benefits of increased visibility and traceability of RFID technology in enhancing responsiveness and flexibility in the sales order management system.

This study aims to examine the associations of collaboration measured as social network characteristics with perceived job demands, job control and social support in primary healthcare professionals.

A cross-sectional study design incorporating social network analysis was utilized. Wellbeing surveys with a network questionnaire were sent to care personnel (physicians, nurses and allied health workers) of Finnish primary healthcare in December 2022 (n = 96). Correlation coefficients and multivariate linear regression modeling were used to analyze the associations.

Higher level of collaboration (measured as number of connections in the network) was associated with lower perceived job demands and higher job control. Care professionals’ frequency of collaboration and proportion of connections with the same occupation (homophily) were borderline associated with social support, indicating further research needs. Larger professional networks, perhaps enabling better teamwork and sharing of workload or information, may directly or indirectly protect from job strain.

Work-related collaboration in primary care should be encouraged and large support networks need to be promoted further. Individuals, especially allied health workers, working in multiple locations or as a sole member of their occupation group in the work community need to be provided with adequate social support.

Social network analysis has been proposed as a tool to investigate care integration and collaboration, but direct analyses of network measures and validated wellbeing instruments have remained absent. This study illuminated the role of collaboration structures in work-related wellbeing of care professionals by showcasing the potential of social network analysis.