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This paper examines the role of export intermediaries in the internationalization of small and medium sized companies in Finland. The empirical study focuses on small Finnish food companies that export to German and Chinese markets.

The research method of this study is qualitative. Data is collected through semi-structured interviews with six respondents presenting exporting companies and export consultants.

The paper provides an empirical contribution to the food internationalization debate. First, the paper discusses the definitions of export intermediaries and their role in export based on the literature. Second, by examining how Finnish food companies experience the role of export intermediaries, the paper contributes to the current discussion on internationalization modes. The empirical results highlight that export companies need transaction-creating services from intermediaries especially when entering physically and culturally distant markets like China.

Limitations of the research generally relate to the use of a small case sample.

The paper holds a number of relevant insights for food companies seeking to enter to German and Chinese markets. Identifying the export services needed by small food companies might help export intermediaries and public policy agencies to better focus their supporting initiatives.

The findings add to the current body of knowledge on the key influence on internationalization modes within the food sector.

Helicopter fuselage vibration prediction is important to keep a safety and comfortable flight process. The helicopter vibration mechanism model is difficult to meet of demand for accurate vibration prediction. Thus, the purpose of this paper is to develop an intelligent algorithm for accurate helicopter fuselage vibration analysis.

In this research, a novel weighted variational mode decomposition (VMD)- extreme gradient boosting (xgboost) helicopter fuselage vibration prediction model is proposed. The vibration data is decomposed and reconstructed by the signal clustering results. The vibration response is predicted by xgboost algorithm based on the reconstructed data. The information transfer order between the controllable flight data and flight attitude are analyzed.

The mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed weighted VMD-xgboost model are decreased by 6.8%, 31.5% and 32.8% compared with xgboost model. The established weighted VMD-xgboost model has the highest prediction accuracy with the lowest mean MAPE, RMSE and MAE of 4.54%, 0.0162, and 0.0131, respectively. The attitude of horizontal tail and cycle pitch are the key factors to vibration.

A novel weighted VMD-xgboost intelligent prediction methods is proposed. The prediction effect of xgboost model is highly improved by using the signal-weighted reconstruction technique. In addition, the data set used is collected from actual helicopter flight process.

The purpose of this paper is to serve as a comprehensive review of short-term study abroad (STSA) outcomes to help guide future STSA and study abroad (SA) scholars and practitioners in the further development of the field.

This paper is the first comprehensive and systematic review of all outcomes of STSA programs within the SA body of research based on 156 papers.

The study provides the first comprehensive classification of all previously studied STSA outcomes (85) into six categories: cross-cultural outcomes, STSA pedagogy outcomes, personal and professional outcomes; language outcomes; teacher and faculty outcomes; and other outcomes. Distinct sub-categories are identified that provide insights on the current landscape of STSA and related research.

This study makes a significant contribution to the theory and practice of SA, and among the key contributions are a systematic understanding of the scale and scope of STSA outcomes; insights on the most efficient design of future STSA programs; and an expanded understanding of the role and importance of STSA programs in international education. Furthermore, a comprehensive STSA outcomes map develops an extensive research agenda.

While the COVID-19 pandemic currently limits the opportunities for STSA, given its previous popularity, the authors envisage a strong return in the coming years of this form of affordable and valuable global learning. STSA programs have become an important component of higher education and which require considerable resources from participants and educational institutions alike. Therefore, further research is needed to understand the impacts of STSA programs and to further improve program design. Such research will serve to better inform both academic understanding of the phenomenon and educational practice.

The study provides the first comprehensive classification of all studied STSA outcomes.

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.