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This article conducts a systematic mapping and inductive analysis of existing work related to hydrogen energy policy in the Web of Science Core Collection from 1996 to 7 July 2023. First, based on bibliometrics, the research reveals the publication volume trends, influential contributors (countries, authors, organizations and journals). Second, bibliometric and content analysis were applied to vividly demonstrate the evolution of the research topic, discuss leading research topics, and provide valuable insights into issues that deserving appropriate attention in the future. The unequivocal role of policies in supporting the development of hydrogen energy is undeniable. In the future, the focal point of hydrogen energy policies should be concentrated on stimulating the creation of demand for green hydrogen, necessitating the effective evaluation of policy outcomes and ensuring the safety of hydrogen energy. Furthermore, the application scope of hydrogen energy extends beyond the transportation sector, holding potential for expansion into other high carbon‐emitting domains. With the strengthening of international collaboration in hydrogen energy, considerations of energy justice and fairness are poised to become pivotal factors in cooperation, exerting a profound influence on the attainment of long‐term development and environmental sustainability. These critical research directions will shape the future landscape of hydrogen energy policy and serve as an essential resource for policymakers, researchers, and stakeholders in this domain. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the Journal of Marine Science & Engineering titled "Advanced Research on Sustainable Maritime Transportation." The issue contains 15 research papers that cover various aspects of sustainable maritime transportation, including green shipping technologies, maritime safety, and sustainable port operations. The research papers explore topics such as the challenges of transitioning to low-carbon fuels for maritime diesel engines, fault-tolerant control strategies for electric ships, the impact of emission control areas on ship orders, and the effectiveness of machine learning methods in predicting a ship's carbon intensity index. Other papers focus on maritime security and sustainable port operations, discussing unmanned surface vessels, maritime safety management, and energy-saving solutions for waste heat recovery on ships. The special issue emphasizes the importance of technological innovation and policy adjustment in achieving sustainability goals in the maritime transportation industry. The research presented in these papers provides scientific guidance and practical directions for the industry's sustainable development. [Extracted from the article]

Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment., Author(s): Ji Guo [1,2]; Yujia Lou [1]; Wanyi Wang [1]; Xianhua Wu (corresponding author) [1,2] 1. Introduction As the world's most consumed light petroleum product, gasoline is one of the [...]

How to evaluate the carbon emission efficiency of multimodal transport is an important issue of public concern, and this article attempts to solve it with a network data envelopment analysis (DEA) model. DEA is a method to evaluate the efficiency of homogeneous decision-making units (DMUs). First, this article studies the efficiency decomposition and efficiency aggregation of the general network structure for DEA model. In efficiency decomposition, the relationship between system efficiency and division efficiency is discussed; whereas in efficiency aggregation, the division tendency brought about by the definition of weights is analyzed. Then, a reasonable and single compromise solution to division efficiency scores is investigated while the system efficiency remains optimal. Finally, a two-stage network DEA model of rail-water intermodal transport is established with carbon dioxide (CO2) emissions as an undesirable output. Based on this model, the rail-water intermodal transport efficiencies of 14 ports in China in 2015 are evaluated by the methods of efficiency decomposition, efficiency aggregation, and non-cooperation. The results show that Rizhao Port, Tangshan Port, Nanjing Port, and Zhuhai Port have set an example to other ports. Qinhuangdao Port, Ningbo-Zhoushan Port, Guangzhou Port, and Beiliang Port need to improve the efficiency of railway transportation. Beibu Gulf port, Zhanjiang Port, Dalian Port, Lianyungang Port, Yantai Port, and Yichang Port should optimize their intermodal system. In addition, Yantai Port and Yichang Port urgently need to improve the port efficiency in low-carbon operation. The network DEA model constructed in this article can be further applied to the efficiency evaluation of multi-link supply chains, and the empirical results can provide a reference for the efficiency evaluation of ports in China. [ABSTRACT FROM AUTHOR]

Abstract Background Trastuzumab is currently approved for the clinical treatment of breast and gastric cancer patients with HER-2 positive tumors, but not yet for the treatment of esophageal carcinoma patients, whose tumors typically show 5 ~ 35% HER-2 gene amplification and 0 ~ 56% HER-2 protein expression. This study aimed to investigate the therapeutic efficacy of Trastuzumab in patient-derived esophageal squamous cell carcinoma xenograft (PDECX) mouse models. Methods PDECX models were established by implanting patient esophageal squamous cell carcinoma (ESCC) tissues into immunodeficient (SCID/nude) mice. HER-2 gene copy number (GCN) and protein expression were determined in xenograft tissues and corresponding patient EC samples by FISH and IHC analysis. Trastuzumab anti-tumor efficacy was evaluated within these PDECX models (n = 8 animals/group). Furthermore, hotspot mutations of EGFR, K-ras, B-raf and PIK3CA genes were screened for in the PDECX models and their corresponding patient’s ESCC tissues. Similarity between the PDECX models and their corresponding patient’s ESCC tissue was confirmed by histology, morphology, HER-2 GCN and mutation. Results None of the PDECX models (or their corresponding patient’s ESCC tissues) harbored HER-2 gene amplification. IHC staining showed HER-2 positivity (IHC 2+) in 2 PDECX models and negativity in 3 PDECX models. Significant tumor regression was observed in the Trastuzumab-treated EC044 HER-2 positive model (IHC 2+). A second HER-2 positive (IHC 2+) model, EC039, harbored a known PIK3CA mutation and showed strong activation of the AKT signaling pathway and was insensitive to Trastuzumab treatment, but could be resensitised using a combination of Trastuzumab and AKT inhibitor AZD5363. In summary, we established 5 PDECX mouse models and demonstrated tumor regression in response to Trastuzumab treatment in a HER-2 IHC 2+ model, but resistance in a HER-2 IHC 2+/PIK3CA mutated model. Conclusions This study demonstrates Trastuzumab-induced tumor regressions in HER-2 positive tumors, and highlights PIK3CA mutation as a potential resistance mechanism to Trastuzumab treatment in pre-clinical patient-derived EC xenograft models.

As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co‐opetition matrices of Chinese provinces, then builds the co‐opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co‐opetition DEA cross‐efficiency model that integrates haze's feature of cross‐border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision‐making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co‐opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co‐opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results. [ABSTRACT FROM AUTHOR]