Shocks and Stability: Understanding Trade Resilience to Natural Disasters

International trade networks are increasingly vulnerable to disruptions from natural disasters, yet the resilience of global trade flows remains insufficiently understood. This study examines how different types of natural disasters—including earthquakes, droughts, extreme temperatures, floods, and storms—affect bilateral trade, and investigates whether structural factors such as export diversification and sectoral positioning shape trade resilience. Our analysis proceeds in two stages. In the first stage, we employ a gravity model estimated using Poisson Pseudo Maximum Likelihood (PPML) on bilateral trade data from Comtrade and disaster data from EM-DAT to quantify the impact of disasters on export volumes. We find that upstream industries experience the most severe trade contractions—particularly due to extreme temperatures and storms—while downstream industries are relatively less affected. In the second stage, we adopt a moment-based three-step method to assess trade resilience by estimating the conditional probability of trade stability following a shock. Our results indicate that export diversification enhances resilience, but its effectiveness varies by economic context: high-income countries benefit more from complexity-driven trade adjustments, whereas low-income economies are more adversely affected by disruptions. Additionally, we find that disasters in trading partner countries generate strong spillover effects in vulnerable economies

Comparative analysis of the secretomes of Schizophyllum commune and other wood-decay basidiomycetes during solid-state fermentation reveals its unique lignocellulose-degrading enzyme system

Additional file 3: Table S2. Identified proteins in the secretomes of four fungi during SSF on Jerusalem artichoke stalk

Bilevel Scheduled Sampling for Dialogue Generation

Exposure bias poses a common challenge in numerous natural language processing tasks, particularly in the dialog generation. In response to this issue, researchers have devised various techniques, among which scheduled sampling has proven to be an effective method for mitigating exposure bias. However, the existing state-of-the-art scheduled sampling methods solely consider the current sampling words' quality for threshold truncation sampling, which overlooks the importance of sentence-level information and the method of threshold truncation warrants further discussion. In this paper, we propose a bilevel scheduled sampling model that takes the sentence-level information into account and incorporates it with word-level quality. To enhance sampling diversity and improve the model's adaptability, we propose a smooth function that maps the combined result of sentence-level and word-level information to an appropriate range, and employ probabilistic sampling based on the mapped values instead of threshold truncation. Experiments conducted on the DailyDialog and PersonaChat datasets demonstrate the effectiveness of our proposed methods, which significantly alleviate the exposure bias problem and outperform state-of-the-art scheduled sampling methods.Comment: 13 pages, 4 figures, Natural Language Processing and Chinese Computing(NLPCC 2023) accepte

Big Data Platform Architecture Under The Background of Financial Technology

With the rise of the concept of financial technology, financial and technology gradually in-depth integration, scientific and technological means to become financial product innovation, improve financial efficiency and reduce financial transaction costs an important driving force. In this context, the new technology platform is from the business philosophy, business model, technical means, sales, internal management, and other dimensions to re-shape the financial industry. In this paper, the existing big data platform architecture technology innovation, adding space-time data elements, combined with the insurance industry for practical analysis, put forward a meaningful product circle and customer circle.Comment: 4 pages, 3 figures, 2018 International Conference on Big Data Engineering and Technolog

The Voluntary Adoption of an Audit Committee and Earnings Quality: Evidence from China

This study investigates the causes and consequences of firms’ voluntarily adoption of the audit committees, using a sample of China’s listed firms from 2001 to 2008 when no regulations or listing rules existed for audit committees. We develop and test two hypotheses. The ‘‘demand’’ hypothesis holds that firms with greater agency costs are more likely to have an audit committee. In contract , the ‘‘opportunistic behavior’’ hypothesis predicts that the bargaining power of the CEO relative to the rest of the board of directors will determine the level of composition of the board and the extent of board monitoring. In this study, we empirically investigate the validity of these two hypotheses and further compare the quality of accounting numbers produced by China’s listed firms with and without an audit committee, in order to shed light on the determinants and effectiveness of audit committee in emerging markets

The Contribution and Prospect of 5G Technology to China's Economic Development

Since the birth of 5G, it has attracted much attention from all countries in the world. The development of 5G industry is particularly important for domestic economic development. 4G changes life, 5G changes society. 5G will not only accelerate the speed of people surfing the Internet, but also bring revolutionary changes to all aspects of social life, making people's lives, work and entertainment more convenient and diverse. The economic impact of the development of the 5G industry on China cannot be underestimated. Nowadays, information and communication technology has increasingly become a new driving force for economic development. 5G technology has already become a key technology pursuit for countries to compete for the status of world power, and it has also become an indispensable part of contemporary economic and social development. We should give full play to the government's guiding role, and work with network giants to build a new platform for cooperation, promote coordinated industrial development, achieve win-win results, and promote economic and social prosperity and development

A Literature Review of the "Burning Money" Behavior of Internet Products

This article mainly studies the current status of the operation of the “burning money model” of Internet products, expounds and analyzes the disadvantages of this operating model, studies the practical effects brought by the “burning money model”, and explores the development direction of future Internet products. Domestic scholars have done a lot of research on the development of Internet products in the future and have achieved certain results. This article aims to discuss the development model of China’s Internet products, summarize the current “burning money” behavior in the operation process of Internet products, and combine the development situation of China’s Internet in the new era to make suggestions for the future development of Internet products

Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries

With the increasing popularity of machine learning (ML), many open-source software (OSS) contributors are attracted to developing and adopting ML approaches. Comprehensive understanding of ML contributors is crucial for successful ML OSS development and maintenance. Without such knowledge, there is a risk of inefficient resource allocation and hindered collaboration in ML OSS projects. Existing research focuses on understanding the difficulties and challenges perceived by ML contributors by user surveys. There is a lack of understanding of ML contributors based on their activities tracked from software repositories. In this paper, we aim to understand ML contributors by identifying contributor profiles in ML libraries. We further study contributors' OSS engagement from three aspects: workload composition, work preferences, and technical importance. By investigating 7,640 contributors from 6 popular ML libraries (TensorFlow, PyTorch, Keras, MXNet, Theano, and ONNX), we identify four contributor profiles: Core-Afterhour, Core-Workhour, Peripheral-Afterhour, and Peripheral-Workhour. We find that: 1) project experience, authored files, collaborations, and geographical location are significant features of all profiles; 2) contributors in Core profiles exhibit significantly different OSS engagement compared to Peripheral profiles; 3) contributors' work preferences and workload compositions significantly impact project popularity; 4) long-term contributors evolve towards making fewer, constant, balanced and less technical contributions