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The purpose of this paper is to reveal a symbol – “however” that authors are very interested in, but few research studies pay attention to the existing literature. The authors aim to further insight its function.

In this research, the authors selected 3,329 valid comments on articles published in the British Medical Journal (BMJ) from 2015 to 2020 as the research objects. The authors showed the length distribution of reviewers' comments. In what follows, the authors analyzed the general distribution of words in comments and reviewer comments’ position to understand reviewers' comments qualitatively in word dimension. Specially, the authors analyzed functions of “however” and “but”, words that authors are most concerned with. In addition, the authors also discussed some factors, which may be related to “however,” that reflect reviewers' praise through regression analysis.

The authors found that there are marked differences in the length of reviewers' comments under different review rounds. By mapping the reviewers' comments to different sections, the authors found that reviewers are deeply concerned to methods section. Adjectives and adverbs in comments written in different sections of the manuscripts also have different characteristics. The authors tried to interpret the turning function of “however” in scientific communication. Its frequency of use is related to reviewers' identities, specifically academic status. More precisely, junior researchers use “however” in praise more frequently than senior researchers do.

The linguistic feature and function of “however” and “but” in the reviewers' comments of the rejected manuscripts may be different from accepted papers and also worth exploring. Regrettably, the authors cannot obtain the peer review comments of rejected manuscripts. This point may limit the conclusion of the investigation of this article.

Overall, the survey results revealed some language features of reviewers' comments, which could provide a basis of future endeavors for many reviewers in open peer review (OPR) field. Specially, the authors also put forward an interesting symbol to examine the review comments, “however”, for the first time.

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.

This paper aims to refine the mechanisms affecting the two-way technology spillover and carbon transfer interactions between supply chain enterprises, and to guide their reduction of carbon emissions.

This study formulates a supplier-led Stackelberg game model to explore the effects of the interactions between two-way technology spillover effects and carbon transfers in decentralized and centralized decision-making scenarios. The optimized Shapley value is introduced to coordinate across the supply chain and determine the overall profits lost in the decentralized scenario.

Emission reductions by the low-carbon manufacturer are negatively correlated with the carbon transfers. Vertical technology spillovers promote carbon reduction, whereas horizontal technology spillovers inhibit it. The vertical technology spillovers amplify the negative effects of the carbon transfers, whereas the horizontal technology spillovers alleviate these negative effects. When the vertical technology spillover effect is strong or the horizontal technology spillover effect is weak in the centralized scenario, the carbon reduction is negatively correlated with the carbon transfers. Conversely, when the vertical technology spillover effect is weak or the horizontal technology spillover effect is strong, the enterprise’s carbon reduction is positively correlated with the carbon transfers. An optimized Shapley value can coordinate the supply chain.

This study examines the effects of carbon transfers on enterprises from a micro-perspective and distinguishes between vertical and horizontal technology spillovers to explore how carbon transfers and different types of technology spillovers affect enterprises’ decisions to reduce carbon emissions.

This paper aims to present the design of a micro-electro-mechanical system (MEMS)-based three-dimensional combined vector hydrophone tailored for unmanned underwater vehicles (UUVs). The proposed design addresses the left-right ambiguity inherent in conventional MEMS hydrophones and enhances acoustic sensing capabilities to support improved UUV performance in underwater environments.

A novel MEMS-based three-dimensional vector hydrophone (M3DH) is proposed, integrating a highly sensitive MEMS chip with a piezoelectric ceramic-based scalar channel. A theoretical model of the hydrophone’s packaging was developed, and its acoustic performance was analyzed through COMSOL Multiphysics 6.2 simulations. Experimental validation of the hydrophone’s sensitivity and directional characteristics was conducted in a standing wave tank.

The MEMS-based three-dimensional combined hydrophone (M3DH) achieved a triaxial vector channel sensitivity of −175.6 dB at 800 Hz (re 1 V/µPa) and a scalar channel sensitivity of −186.3 dB (0 dB = 1 V/µPa). In addition, at 500 Hz, the vector channel exhibited a distinct “8”-shaped directivity pattern, whereas the scalar channel maintained a circular omnidirectional response. The hydrophone demonstrated excellent acoustic performance in three-dimensional space, effectively providing comprehensive acoustic information for small underwater platforms.

This research addresses the left-right ambiguity issue in MEMS hydrophones by presenting an MEMS-based three-dimensional combined hydrophone designed for integration into UUVs, offering an innovative solution to enhance underwater acoustic sensing capabilities in small platforms.

Currently, various detection technologies for unmanned underwater vehicles are highly susceptible to environmental impacts. Wake detection technologies have gradually gained attention and development. However, the clarity of detection results remains a challenge. This paper aims to present the design of a MEMS three-dimensional vector wake sensor. Compared to similar sensors, the MEMS three-dimensional vector wake sensor offers improved propeller wake measurement capabilities.

A MEMS three-dimensional vector wake sensor inspired by the fish lateral line system is designed. This paper discusses the working principle of the sensor. Finite element simulation is used to determine the optimal dimensions of the sensor’s sensitive chip and packaging structure. In addition, the wake environment is simulated for performance testing.

Flow velocity calibration test results confirm that the MEMS three-dimensional vector wake sensor exhibits high sensitivity, achieving 1727.6 mV/(m/s). Vector capability tests show that the data consistency in the same direction reaches 91.8%. The sensor demonstrates strong vector detection capability.

The MEMS three-dimensional vector wake sensor plays a critical role in the formation control of unmanned underwater vehicle fleets and target detection.

This study focuses on applications for unmanned underwater vehicles. It enhances the detection capabilities of unmanned underwater vehicles. This is of significant importance for future deep-sea target detection.

This study aims to analyze the changes in the ecosystem service value (ESV) in response to land use and contribute significantly to ecological construction and sustainable development.

The present study was conducted in the upper Zhanghe River region based on the moderate-resolution imaging spectroradiometer Land Cover Type product MCD12Q1 by using geographic information system (GIS) methods to process and re-classify the land-use data and using the Chinese ESV equivalent weight factors to investigate changes in land use and resulting changes in ESV between 2001 and 2013.

The results showed significant fluctuations in ESV between 2001 and 2013: there was a decline in ESV from 2001 to 2004, followed by a gradual rise after 2004, and the overall ESV exceeded 2001 levels by the end of 2013. However, the pattern of ESV change differed across geographic locations, and each administrative region contributed differently to the overall trend. The analysis confirmed that the land-use change was closely related to the change in its ESV, and the coefficients of sensitivity of ESV for all types of land use were less than one, indicating that the coefficient value of ESV lacked elasticity.

Therefore, to promote sustainable development in the upper Zhanghe River region, ESV should be taken into consideration when planning land use, especially for land types with high ESV, such as water bodies and forestlands.

The results can provide scientific support for the sustainable development of the ecological, economic and societal aspects of the upper Zhanghe River region. In addition, county-level administrative divisions were set as the basic research unit for the analysis and discussion of ESV changes in each unit within the research period and its impact on the overall ESV of the entire area to lay down a foundation for the analysis of the ESV spatial dynamic distribution in the entire research site.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

This study quantitatively examines the relationship between economic fluctuations and government budget size in the context of China’s fiscal decentralization, drawing inspiration from theoretical predictions of the Keynesian view and empirical studies on other economies.

The panel comprises 31 provinces or equivalents in mainland China, spanning from 1994 to 2019. Diverse estimation strategies including two-way fixed effect regression, the generalized method of moments (GMM) and threshold regressions are, utilized.

The results suggest that under the “tax-assignment system”, neither the central government’s fiscal transfers nor the provincial budgetary revenues or expenditures help reduce economic volatility. Surprisingly, some regression outcomes suggest that government size measures destabilize business cycles.

While the study does not provide supportive evidence for the stabilizing effect of public budgets in Chinese provinces, it promotes a rethinking of the government’s intricate role in macroeconomic stabilization in the context of China’s fiscal decentralization.

The purpose of this paper is to summarize the advances in grey system theory research and various application achievements in science and engineering. At the same time, it commemorates the 40th anniversary of the birth of grey system theory and the 10th anniversary of Grey Systems–Theory and Application.

Firstly, the innovations of theoretical research in grey system theory were summarized and some of the widely recognized new results are briefly described. By searching and combing the research results of grey system theory in China national knowledge infrastructure (CNKI) database and Web of Science by Institute for Scientific Information (ISI), this paper shows the rapid development trend of grey system theory in the past 40 years, and the successful applications of grey system theory in the fields of social sciences, natural sciences and engineering technologies.

More than 227 thousands literature were found by input 10 phrases such as grey system, grey number and sequence operator etc. in CNKI database. After entering the new century, the number of grey system papers included in CNKI database is increasing rapidly. Since 2008, more than 10 thousands papers have been included per year and more than 15 thousands papers have been included per year since 2014. Grey system method and model are widely used in physics, chemistry, biology and other fields of natural science, as well as transportation, electric power, machinery and other fields of engineering technology, and a large number of valuable results have been achieved.

It can be seen that the grey system theory plays an important role in promoting China’s scientific and technological progress, innovation and development and high-level talent training from tens of thousands of literatures marked with important national science and technology projects and a large number of grey system literatures published by China’s double first-class universities and double first-class discipline construction universities.

Both innovations of theoretical research and practical application play important role in the growth of new theory. The innovations of theoretical research provide methods and tools for practical application, which is conducive to improve application efficiency and broaden application fields. A large number of practical applications needs have become the source of theoretical innovation and the solid background for the birth of theoretical innovation achievements.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.