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This method will become a new development trend in subgrade structure design for high speed railways.

This paper summarizes the structural types and design methods of subgrade bed for high speed railways in China, Japan, France, Germany, the United States and other countries based on the study and analysis of existing literature and combined with the research results and practices of high speed railway subgrade engineering at home and abroad.

It is found that in foreign countries, the layered reinforced structure is generally adopted for the subgrade bed of high speed railways, and the unified double-layer or multi-layer structure is adopted for the surface layer of subgrade bed, while the simple structure is adopted in China; in foreign countries, different inspection parameters are adopted to evaluate the compaction state of fillers according to their respective understanding and practice, while in China, compaction coefficient, subsoil coefficient and dynamic deformation modulus are adopted for such evaluation; in foreign countries, the subgrade top deformation control method, the subgrade bottom deformation control method, the subsurface fill strength control method are mainly adopted in subgrade bed structure design of high speed railways, while in China, dynamic deformation control of subgrade surface and dynamic strain control of subgrade bed bottom layer is adopted in the design. However, the cumulative deformation of subgrade caused by train cyclic vibration load is not considered in the existing design methods.

This paper introduces a new subgrade structure design method based on whole-process dynamics analysis that meets subgrade functional requirements and is established on the basis of the existing research at home and abroad on prediction methods for cumulative deformation of subgrade soil.

This paper aims to clarify the relationship between digital transformation and labor structure from the perspectives of microenterprise business strategies and factor allocation efficiency. It attempts to address the gap in existing research by explaining the impact of digital transformation on multidimensional workforce structures and the positive effects of this structural adjustment on labor allocation efficiency. In addition, the study further explores the economic ramifications of digital transformation, clarifying the correlation between changes in labor force structure and enterprise human resource allocation, thus enhancing the employment mobility effects of digital innovation at the enterprise level.

In contrast to prior research, our approach uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures.

The paper asserts that digital transformation significantly influences labor structure changes, evidenced by increased proportions of high-skilled, non-routine and younger laborers, as well as decreased shares of low-skilled, routine and older-age workers. Furthermore, it captures internal labor structure impacts, influenced by enterprise size, ownership, industry density and regional digitization levels. Mechanism analysis indicates moderation of digital transformation effects on labor structure by innovative tasks, labor productivity and management shareholding.

The paper reveals the specific impact of corporate digital transformation on workforce structure, enriching the employment mobility effects of digital innovation at the enterprise level and providing theoretical support for the formulation and implementation of relevant policies.

First, this paper delves into the impact of digital transformation on the internal labor structure from a microlevel perspective, elucidating its mechanisms. Second, in contrast to prior research, it uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures. Lastly, the study investigates the economic ramifications of shifts in employment structures. The findings of this study furnish novel empirical evidence for the debate regarding whether digital transformation can indeed enhance labor allocation efficiency.

The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy.

An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification.

The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection.

To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose.

In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring.

The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

Binder jetting is one of the essential additive manufacturing methods because it is cost-effective, has no thermal stress problems and has a wide range of different materials. Using binder jetting technology in the industry is becoming more common recently. However, it has disadvantages compared to traditional manufacturing methods regarding speed. This study aims to increase the manufacturing speed of binder jetting.

This study used adaptive slicing to increase the manufacturing speed of binder jetting. In addition, a variable binder amount algorithm has been developed to use adaptive slicing efficiently. Quarter-spherical shaped samples were manufactured using a variable binder amount algorithm and adaptive slicing method.

Samples were sintered at 1250°C for 2 h with 10°C/min heating and cooling ramp. Scanning electron microscope analysis, surface roughness tests, and density calculations were done. According to the results obtained from the analyzes, similar surface quality is achieved by using 38% fewer layers than uniform slicing.

More work is needed to implement adaptive slicing to binder jetting. Because the software of commercial printers is very difficult to modify, an open-source printer was used. For this reason, it can be challenging to produce perfect samples. However, a good start has been made in this area.

To the best of the authors’ knowledge, the actual use of adaptive slicing in binder jetting was applied for the first time in this study. A variable binder amount algorithm has been developed to implement adaptive slicing in binder jetting.

Following resource-based view, the purpose of this paper is to investigate the effects of three intra-organizational structural elements on supply chain integration (SCI).

Based on data collected from ten countries, this study employs the structural equation modeling method to test the proposed model.

The results demonstrate that teamwork culture is positively related to three dimensions of SCI. Organizational commitment has positive effects on internal and customer integration (CI), whereas it has no significant effect on supplier integration (SI). Human goodness is only positively related to internal integration, but has no significant effect on SI or CI.

This study contributes to both structural elements literature and SCI enabler literature by operationalizing three human-related components of structural elements and empirically investigating relationships between intra-organizational structural elements and SCI.

This paper aims to analyze the publication structure of academic research on organizational creativity between 1975 and 2022.

Bibliographic data on organizational creativity are extracted from the Scopus database and then analyzed through VOSviewer and R Statistical Software.

This paper analyzes 416 publications on organizational creativity from 1975 to 2022. Accordingly, the study identifies the most productive countries, universities, authors, journals and prolific organizational creativity publications. Also, the study uses VOSviewer and R Statistical Software Bibliometrix Package to visualize the mapping based on co-citation, bibliographic coupling and co-occurrence of keywords.

The study’s main contribution is that it provides an overview of the trends and trajectories of organizational creativity, which may help researchers and practitioners comprehend the trends and future research directions.

Despite touting the value of green practices, many firms struggle to respond appropriately to the diverse environmental issues. The purpose of this paper is to investigate how the external and internal pressures interplay to influence top management championship, which, in turn, fosters the company’s green culture and the adoption of green practices. It thus helps to explain Chinese firms’ diversity with respect to the adoption of green practices.

A conceptual model is developed that summarizes the interplay of external and internal pressures, top management championship, green culture and the adoption of green practices. Data from 148 Chinese manufacturing firms were collected and a structural equation model was used for statistical analysis.

Government policy that provides incentives to adopt green practices and overseas customers’ green demand has significant positive influences on top management championship, while resources pressure has a significant negative effect. Government command and control policy, domestic customers’ green demand and organizational inertia do not impact top management championship. Furthermore, top management championship is positively correlated to both green culture and green practices, and green culture contributes to implementing green practices.

The findings help us understand which external and internal factors inspire or force top management to adopt green practices, and how they do so. Moreover, managers must also be aware of the bridging role of green culture. The findings will be valuable to policy makers in forming and enforcing “stick” or “carrot” environmental policies.

Leveraging a multi-theoretic approach, the authors’ research builds on insights from the institutional theory, natural resource-based view (NRBV) and upper echelons perspective, so as to increase the authors’ understanding on how firms adopt green practices to respond to environmental sustainability pressures. The institutional theory and the NRBV are leveraged in this study to recognize that firms perceive not only external institutional pressure for environmental management but also the internal pressure from resource constraints and capability to change. Upper echelons perspective is integrated into this study to explain the leadership role that top management serves in the management of the organization’s response to dynamic changes in the institutional environment and cultivate green culture within organization.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

The purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday basis. The authors examine whether order imbalance is more powerful to explain variations in asset prices compared to other indicators of trading activity, particularly trading volume.

Using Chicago Mercantile Exchange best bid best offer data, the impact of order imbalance is analyzed via regression analyses. The analyses are carried out for corn, wheat, soy, live cattle and lean hogs in March 2008 and March 2016.

Results confirm the positive relation between order imbalance and returns as well as between order imbalance and price volatility as suggested by market microstructure models. Order imbalance, however, does not generally outperform trading volume as an explanatory variable.

For some contracts, returns can be predicted using lagged order imbalance. This offers the opportunity to derive profitable trading strategies.

This paper is one of the first attempts to explore the relationship between order imbalance and returns, liquidity and volatility for agricultural commodity futures on an intraday basis, accounting for the increased trading volume and for the high speed at which new information enters the market in an electronic trading environment.