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This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a challenging open problem. In recent years, agent-based crowdsourcing approaches focusing on recommendations or incentives have emerged to dynamically match workers with diverse characteristics to tasks to achieve high collective productivity. However, existing approaches are mostly designed based on expert knowledge grounded in well-established theoretical frameworks. They often fail to leverage on user-generated data to capture the complex interaction of crowdsourcing participants’ behaviours. This paper aims to address this challenge.

The paper proposes a policy network plus reputation network (PNRN) approach which combines supervised learning and reinforcement learning to imitate human task allocation strategies which beat artificial intelligence strategies in this large-scale empirical study. The proposed approach incorporates a policy network for the selection of task allocation strategies and a reputation network for calculating the trends of worker reputation fluctuations. Then, by iteratively applying the policy network and reputation network, a multi-round allocation strategy is proposed.

PNRN has been trained and evaluated using a large-scale real human task allocation strategy data set derived from the Agile Manager game with close to 500,000 decision records from 1,144 players in over 9,000 game sessions. Extensive experiments demonstrate the validity and efficiency of computational complex crowdsourcing task allocation strategy learned from human participants.

The paper can give a better task allocation strategy in the crowdsourcing systems.

Since the declaration of reform and market opening from China in 1990, China has drawn much attention from the world thanks to its rapid economic growth and its emergence as the world's major consumer market and the center of the global economy. Moreover, it established the new trade order, making East Asia the center of the new trade trend as it becomes a manufactural and sales stronghold of multinational companies. The Chinese distribution market is expected to show a high growth rate by 2010 and it draws attention as a new business sector which can bring huge profits. However, advancement of the Chinese distribution industry now faces systemic problems and research on such problems is insufficient. Therefore, in this study we will conduct SWOT analysis based on previous studies on the Chinese distribution industry and use it as a ground to propose strategic solutions for development.

The e-freight program launched by the International Air Transport Association (IATA) has gradually become a standard specification for international air freight operations. This study examined critical factors affecting air freight forwarders’ decision to adopt the IATA e-freight using a technology-organization-environment model with air freight forwarders in Taiwan as the base. Our findings show that ‘information technology (IT) competence’, ‘trading partner pressure’, ‘government policy’ and ‘competitive pressure’ all have significant positive effects on air freight forwarders’ decision to adopt the e-freight and the top three factors among these are ‘government funding’, ‘government’s active promotion’ and ‘government’s requirement of electronic air waybill (e-AWB)’. Finally, this study proposes strategies that can encourage air freight forwarders to decide on e-freight adoption for the information of relevant oK regyawniozradtison International Air Transport Association (IATA); IATA e-freight; Technology organization environment model; Air freight forwarder

The purpose of this paper is to examine the impacts of farmland renting-in on planted grain acreage.

A survey data of five counties were analyzed with the two-stage ordinary least squares model.

Households renting-in land trended to plant more maize, and the more land was rented by a household the more maize was planted, while wheat acreage showed non-response to farmland renting-in.

Overall, the analysis suggests that policy makers should be prepared for different changing trends of grain crop acreage across the nation as farmland transfer continues. Future research should pay attention to the effect of farmland transfer on agricultural productivity and rural household income growth.

As the Chinese Government is promoting larger-scale and more mechanized farms as a way of protecting grain security, it is important to understand whether farmland renting-in will reduce planted grain acreage. This study provides empirical evidence showing the answer to that question may differ across different regions and depend on the particular grain crop in question.

This study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.

Chinese wagons' existing maintenance strategy had left a certain safety margin for the characteristics of widely running range, unstable service environment and submission to transportation organization requirements. To reduce maintenance costs, China railway (CR) has attempted to extend the maintenance interval since 2020. The maintenance cycle of C80 series heavy haul wagons is extended by three months (no stable routing) or 50,000 km (regular routing). However, in the meantime, the alarming rate of the running state, a key index to reflect the severe degree of hunting stability, by the train performance detection system (TPDS) for the C80 series heavy haul wagons has increased significantly.

The present paper addresses a big data statistical way to evaluate the risk of allowing the C80 series heavy haul wagons to remain in operation longer than stipulated by the maintenance interval initial set. Through the maintenance and wayside-detector data, which is divided into three stages, the extension period (three months), the current maintenance period and the previous maintenance period, this method reveals the alarming rate of hunting was correlated with maintenance interval. The maintainability of wagons will be achieved by utilizing wagon performance degradation modeling with the state of the wheelset and the often-contact side bearing. This paper also proposes a statistical model to return to the average safety level of the previous maintenance period's baseline through correct alarming thresholds for unplanned corrective maintenance.

The paper proposes an approach to reduce safety risk due to maintenance interval extension by effective maintenance program. The results are expected to help the railway company make the optimal solution to balance safety and the economy.

This study aims to investigate whether firms purchase directors' and officers' liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high.

This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU.

The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms' purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality.

The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases.

The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms' decisions to purchase D&O insurance.

This paper aims to enhance the understanding of the role of Chinese outward foreign direct investment (OFDI) policies for cross-border merger and acquisition (M&A) by distinguishing between coercive and noncoercive OFDI policies.

The dependent variable is the count of completed M&A transactions, measured monthly. Due to the nature of the study’s data, the author performs a zero-inflated negative binomial (ZINB) regression.

Separating between coercive and noncoercive policies, the author finds that the latter type shows a stronger supportive effect on the count of M&A deals. Considering firm ownership, the study’s results reveal that announcements of coercive policies have a weaker effect on cross-border M&A for state-owned enterprises (SOEs) than that for private-owned enterprises (POEs). For local SOEs (LSOEs) and central SOEs (CSOEs), this difference becomes even larger with noncoercive policy announcements. The influence on M&A of both policy types gets partially replaced with increasing internationalization experience.

Combining institutional theory with policy change theory, the author argues that international business (IB) research on policy change needs to consider the integration of theoretical policy-level approaches to catch the effects of policy change on firm internationalization appropriately. The findings of the study support this argument by highlighting that the policy effect differs by policy type.

Forestry carbon sink (FCS) is not only an important measure to deal with the current global climate change but also an effective way to build an ecological civilization. As an important form of implementation of FCS, the afforestation and reforestation projects under the clean development mechanism (CDM A/R) have important functions such as ecological protection and economic growth. This paper aims to evaluate the short-term and long-term impact of CDM on the county economy and its impact mechanism.

This paper first uses propensity score matching to match the county (treatment group). Second, this paper uses difference in difference to estimate the net effect of CDM A/R project on county economic development to reduce estimation error. Finally, the impact mechanism of implementing CDM A/R project on county economic development was tested.

The CDM A/R project has significantly promoted the development of real gross domestic product (GDP) and per capita real GDP in the region. Because of the long project cycle, this promotion is not immediate in the short term and has an obvious hysteresis effect. The longer the implementation time, the greater the promotion of the local economy will develop. The results are robust after the robustness test that uses the single-difference method. The CDM A/R project has promoted local economic growth by optimizing the local industrial structure, increasing the regional capital stock and raising the regional government’s fiscal revenue and expenditure.

This paper provides a critical overview of the relationship between clean development mechanism and local economic development.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.