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The impact of globalization and worldwide competition has become excruciatingly noticeable in China. The purpose of this study is to examine China’s transformation of state‐owned enterprises (SOEs) to competitive, capable organizations by identifying the dominant challenges and forces for change to State‐Owned Enterprises in China (SOE), the nature of SOE responses to those forces, and the degree of SOE success in making the necessary transformations to compete in a global business environment.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

Emerging markets face the developmental circumstances of a weak foundation in both manufacturing and services. Although servitization is viewed as an opportunity to realise industry transformation and upgrading, ways for emerging market firms to implement a high-level servitization strategy is still understudied. This study examines combinations of causal conditions for emerging market firms to implement a high level of servitization.

Anchored in the strategy tripod model, this study examines the configurations for implementing a high-level servitization strategy by using fuzzy-set qualitative comparative analysis (fsQCA) and data from the China Stock Market and Accounting Research Database (CSMAR).

The findings identify three kinds of configurations for achieving a high-level servitization strategy: market-cultivational servitization, competition-driven servitization and government-related servitization. Furthermore, the mechanisms for implementing a high-level servitization strategy differ within the regional marketization level and state-owned equity. Specifically, the improvement of the regional marketization level helps manufacturing firms realise a high level of servitization by strengthening service capacity, and state-owned equity helps firms gain distinctive legitimacy to integrate suppliers and providers into the servitization context.

The proposed multilevel perspective frameworks enable manufacturing firms in emerging markets to achieve a high level of servitization strategy.

This paper explores the impact of institutional environment, industry conditions and firm-level microfoundations on servitization, therefore providing a reference framework for emerging market firms interested in implementing high-level servitization strategies.

The purpose of this paper is to verify the performance and function of the scale-up prototypes by predicting the material and energy consumption on the basis of dimension-reduced prototypes. Additive manufacturing (AM) costs determine carbon emissions in total life cycle, among which material and energy consumption are major components. Predicting material and energy consumption is fundamental to reducing costs.

This paper presents a material and energy co-optimization method for AM via multiple layers prediction (MLP). Material and energy consumption are predicted to reduce the AM costs. In particular, scalable, complex curved surface component is used to improve forecasting efficiency. Subsequently, the back pressure distribution is obtained by scale-up specimens, which can lay the foundation for the ergonomic conceptual design.

Taking evolutionary ergonomic product as an example, the relative gravity direction of backrest is calculated. The material and energy consumption are predicted with low deviation. Physical experiments were carried out to validate information. Digital and physical tests have revealed that material and energy co-optimization improves manufacturing efficiency.

The innovatively proposed MLP method predicts material and energy consumption of scale-up prototypes to reduce the costs. It is propitious to improve the carbon emission efficiency in life cycle of AM. The originality may be widely adopted alongside increasing environmental awareness.

Compared with cusp height and area deviation ratio, volume error (VE) caused by the layer height could represent the stair-case effect more comprehensively. The proposed relative volume error (RVE)-based adaptive slicing method takes VE rather than cusp height as slicing criteria, which can improve part surface quality for functionalized additive manufacturing.

This paper proposes a volumetric adaptive slicing method of manifold mesh for rapid prototyping based on RVE. The pre-height sequences of manifold mesh are first preset to reduce the SE by dividing the whole layer sequence into several parts. A breadth-first search-based algorithm has been developed to generate a solid voxelization to get VE. A new parameter RVE is proposed to evaluate the VE caused by the sequence of the layer positions. The RVE slicing is conducted by iteratively adjusting the layer height sequences under different constraint conditions.

Three manifold models are used to verify the proposed method. Compared with uniform slicing with 0.2 mm layer height, cusp height-based method and area deviation-based method, the standard deviations of RVE of all three models are improved under the proposed method. The surface roughness measured by the confocal laser scanning microscope proves that the proposed RVE method can greatly improve part surface quality by minimizing RVE.

This paper proposes an RVE-based method to balance the surface quality and print time. RVE could be calculated by voxelized parts with required accuracy at a very fast speed by parallel.

This study examines specific dimensions of the performance‐only measurement of service quality (SERVPERF) as determinants of consumer satisfaction and subsequent behavioral intentions associated with banking services in mainland China. Empirical support for the predictive ability of context‐dependent service quality dimensions is presented. Our results extend and enhance the validity of the performance‐only approach to service quality through the focus on the multidimensional facets of the SERVPERF scale, a direct link between context‐dependent dimensions of service quality and consumer satisfaction, and its application in an international setting. Strategic issues in managing service quality with retail banks in the Chinese market are identified and discussed.

The layer section of laser additive manufacturing (AM) can be rasterized. Subsequently, the rasterized layer section can be converted into sparse matrix. However, large storage space is occupied due to the high manufacturing resolution. In order to reduce the storage space, the purpose of this research is to propose a lossless compression method to compress the sparse matrix.

A lossless compression method for additive manufacturing is proposed. According to manifold and irregularity feature of the object of laser AM, a lossless compression method called continuous rows compressed storage (CRCS) based on continuous rows is innovatively proposed. In particular, the better direction strategy of compression method is selected based on the side-projected area per layer.

Take human teeth as an example, compared with compressed sparse row (CSR), the CRCS has advantage up to 98.88% in storage space. Compared with block compressed sparse row (BCSR), the CRCS has advantage up to 60.04% in storage space.

The proposed CRCS could be employed to compress the sparse matrixes of rasterized layer sections of laser AM. Compared with common lossless compression method of sparse matrix, the compression ratio of CRCS is greater. CRCS is propitious to reduce the storage space usage, thereby improving transmission efficiency.