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Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.

A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.

The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.

This study has positive implications for enriching the method system of multivariable grey prediction model.

An experiential retailing strategy is considered cardiotonic for consumers and brands. When such a stimulus is used, what cognition and behaviors are generated is an issue worthy of study. Therefore, the purpose of this study is to explore the factors and mechanisms that affect consumer response and relationship quality through empirical research.

Based on theoretical deduction, this paper proposes a conceptual model that includes four antecedents: experiential scene atmosphere (ESA), highlight design, interaction approach and value fit. These affect consumer–brand relationship quality (CRQ) through consumer identification (CI), brand identity (BI) and experiential immersion degree. In two rounds of predictive tests, 624 and 481 valid data were collected, respectively, and the feasibility of the scale was verified scrupulously. Furthermore, 427 participants reported the participants' tendencies in a formal empirical study.

The results show that the direct effects of antecedents, mediators and dependent variables are significant. Although the mediating effect of BI in experiential highlighting design on CRQ is not supported, other consumer response variables have a full or partial mediating effect.

This study not only functions as an innovation of research perspective enriching the theoretical framework of the influence mechanism of experiential retailing, but also strengthens the discussion on the role of value fit, especially emotional value fit, in experiential retailing.

The objective of this study is to explore the impact of a government-supported initiative for operational security, specifically the establishment of the national security emergency industry demonstration base, on the profitability of local publicly traded companies. Additionally, the study investigates the significance of firms' blockchain strategies and technologies within this framework.

Using the differences-in-differences (DID) approach, this study evaluates the impact of China's national security emergency industry demonstration bases (2015–2022) on the profitability of local firms. Data from the China Research Data Service (CNRDS) platform and investor Q&As informed our analysis of firms' blockchain strategy and technology, underpinned by detailed data collection and a robust DID model.

Emergency industry demonstration bases have notably boosted enterprise profitability in both return on assets (ROA) and return on equity (ROE). Companies adopting blockchain strategies and operational technology see a clear rise in profitability over non-blockchain peers. Additionally, the technical operation of blockchain presents a more pronounced advantage than at the strategic level.

We introduced a new perspective, emphasizing the enhancement of corporate operational safety and financial performance through the pathway of emergency industry policies, driven by the collaboration between government and businesses. Furthermore, we delved into the potential application value of blockchain strategies and technologies in enhancing operational security and the emergency industry.

This study investigated the effect of individual perceptions of innovation-oriented human resource system (IHRS) on individual innovative work behavior (IWB) and how this effect is realized.

The authors conducted an online questionnaire survey at three time points with 481 employees in three Chinese organizations. Structural equation modeling was used to test the hypothesized relationships among the variables.

Perceived IHRS was found to positively influence IWB, and this effect was sequentially mediated by individual perceptions of innovative culture and intrinsic motivation.

In order to elicit IWB, HR systems should be constructed around the strategic objective of innovation. Moreover, there should be a match between IHRS and innovative culture to trigger intrinsic motivation and ultimately IWB.

This study examines the effect of perceptions of IHRS on individuals' IWBs; Moreover, it integrates organizational culture and individual motivation and finds a chain mediating role of individual perceptions of innovative culture and intrinsic motivation in the relationship between IHRS and IWB.

Tantalum is a kind of metal material with moderate hardness, high ductility, small thermal expansion coefficient, excellent corrosion resistance and outstanding biocompatibility. The purpose of this study is that its tribological performance could be tested and analyzed so as to use it in different fields.

The friction resistance of a-Ta under dry friction conditions was tested at different roads. The relationships between load and friction coefficient, wear rate and two-dimensional shape of wear scars were studied.

The stable Ta₂O₅ film with lubrication effect was generated in the process of friction. And, the larger the test load, the more Ta₂O₅ would be generated.

This work lays a theoretical foundation for tantalum as an excellent wear-resistant material.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0047/

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.