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Article
Green & Sustainable Science & Technology
Qing Guo et al.
Summary: China plays a crucial role in global PV product trade, and the RCEP agreement offers more opportunities for China. This study utilizes an improved trade gravity model with BP neural networks to estimate trade potentials and concludes that: (1) BP neural networks outperform traditional estimation methods, with multiple network combination enhancing robustness and accuracy. (2) China's trade potential with RCEP countries in PV products is relatively mature, but there is still room for further expansion in trade with Japan and other countries. (3) Trade potentials for different regions within RCEP have shown historical variations, with a decline in Oceania and an increase in East and Southeast Asia in recent years.
Article
Mariska Aucamp et al.
International Trade Journal
(2023)
Article
Computer Science, Theory & Methods
Anna Breit et al.
Summary: This study provides an in-depth understanding of the characteristics and trends of Semantic Web Machine Learning (SWeML) systems through a systematic study and analysis of nearly 500 papers. It also introduces a classification system for SWeML systems as ontology.
ACM COMPUTING SURVEYS
(2023)
Article
Mathematics, Interdisciplinary Applications
Hai-Chuan Xu et al.
Summary: The energy commodity is crucial for economic development and national security. Understanding the international energy trade network and avoiding systemic risk is important for countries. However, granular trade network data is often lacking, requiring network reconstruction from partial data. This paper compares seven network reconstruction methods applied to 16 types of energy trade data, finding that Cimi is the most successful method for reproducing network structures and bilateral weights, aiding in the assessment of systemic risks.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Thermodynamics
Aso Mafakheri et al.
Summary: Petrochemicals are valuable products made from oil, and this research aims to use computational methods to help stakeholders identify future markets and suppliers. Unlike previous studies, which focused on connection establishment in unipartite networks, this research improves and extends link prediction for petrochemicals by identifying weak trade connections and using bipartite graphs to model country-product relations.
Article
Computer Science, Information Systems
Giuseppina Di Paolo et al.
Summary: Due to the rapid growth of knowledge graphs (KG) as representational learning methods, question-answering approaches using KG have gained attention. This paper proposes a question-answering approach that translates natural language queries into graph triples and uses knowledge graph embedding (KGE) models to retrieve answers. The system outperforms existing literature and provides a fast knowledge extraction system and answer prediction model. A use case example demonstrates the generated KG in a graphical interface.
Article
Green & Sustainable Science & Technology
Luoming Hu et al.
Summary: This study utilizes the bilateral trade data from the United Nations International Trade Statistics Database to identify statistical imbalances in trade records. Statistical imbalance refers to the inequality between the import or export trade value of a commodity category and the total value of its subcategories. The findings reveal that statistical imbalance is widespread and exhibits clustering patterns, particularly in specific commodity categories. Thus, it is recommended that researchers pre-screen the data for statistical imbalance to ensure the validity of their research results.
Article
Engineering, Environmental
Zhihan Jiang et al.
Summary: UN Comtrade, a widely used data source for physical trade analysis, faces the issue of outliers that limit its application. To address this issue, researchers developed a framework using kernel density estimation and statistical models to detect and handle outliers, as well as a deviation index to assess their impacts, resulting in improved data quality.
RESOURCES CONSERVATION AND RECYCLING
(2022)
Article
Engineering, Environmental
Zhihe Zhang et al.
Summary: This paper introduces a novel approach for improving data accuracy in trade, where missing physical values are estimated using statistical methods, resulting in varying effects on countries and commodities during data processing.
RESOURCES CONSERVATION AND RECYCLING
(2022)
Article
Economics
Adriana AnaMaria Davidescu et al.
Summary: The trade performance of Romania is significantly influenced by the demand of its major trade partners in the EU, imports from China and the rest of the world, as well as factors such as government effectiveness, corruption control, and cultural values. The capacity of Romanian exports to recover to pre-COVID-19 levels is assessed through simulation forecasting scenarios based on economic recovery shapes and shock transmission types.
ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA
(2022)
Article
Computer Science, Information Systems
Andrea Rossi et al.
Summary: Knowledge Graphs (KGs) are widely used in industrial and academic settings, with research efforts focused on large-scale information extraction. Link Prediction (LP) techniques address the issue of incompleteness in KGs by identifying missing facts. LP methods based on KG embeddings have shown promising performance, but there is insufficient attention on design choices and the standard practice of reporting accuracy may lead to overlooking majority of KG content.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Economics
Kaoru Nabeshima et al.
Summary: This paper introduces the ACRI to measure additional regulatory requirements exporters face in foreign markets. It finds a significant negative impact of regulatory burdens on bilateral trade, with effects varying across sectors and depending on the development levels of the trading countries.
JOURNAL OF THE JAPANESE AND INTERNATIONAL ECONOMIES
(2021)
Article
Economics
Halit Yanikkaya et al.
Summary: This paper explores the relationship between countries' participation in Global Value Chains (GVCs) and their positions in the International Trade Network (ITN), finding that forward participation is negatively correlated with clustering and positively correlated with trade links, while backward participation is positively correlated with clustering and negatively correlated with trade links. The results provide a clear understanding of the nexus between GVCs and ITN.
APPLIED ECONOMICS LETTERS
(2021)
Review
Genetics & Heredity
Bohyun Lee et al.
FRONTIERS IN GENETICS
(2020)
Article
Business, Finance
Theresa M. Greaney et al.
Article
Computer Science, Artificial Intelligence
Petar Ristoski et al.
