International establishment mode, ownership structure, and performance of emerging economy MNES: enriching the global value chain perspective with the institution-based view

By adopting a global value chain (GVC) perspective and institution-based view, this study examined three key issues of emerging market multinational enterprises (EM MNEs) as they conduct foreign investments: 1) determinants of the decision made concerning the mode of establishment, greenfield or acquisition; 2) determinants of the choice of ownership structure, whole ownership or share ownership; and 3) the impact of mode choices on firms’ foreign subsidiary performance. The study adopted empirical, quantitative research methods for the analysis and chose China as the emerging economy research context. Secondary data were collected from the annual reports of Chinese on-list MNEs from the years between 2007 and 2015. It was found that the value chain extension of investment activities led to different entry modes of foreign direct investment (FDI). For the establishment mode decision, firms conducting vertical value chain extension were more likely to choose acquisition over greenfield. Cultural distance between home and host country strengthened this positive impact of vertical extension. Further, firms were more likely to select acquisition over greenfield when the vertical extension was upstream (design end) as opposed to downstream (marketing end). For the choice of ownership structure, it was found that both the value chain extension of the investment and the state ownership of firms affected the choice made between whole ownership and shared ownership. On the one hand, the value chain extension of the investment showed a very weak impact on shared ownership choices in both acquisition and greenfield. It was also found that the economic freedom of the host market could alleviate the tendency of vertical extension firms to choose shared ownership in both acquisition and greenfield. Moreover, the degree of the home market sub-national marketization can alleviate the tendency for vertical extension firms to choose shared ownership in both acquisition and greenfield, while in acquisition such alleviation effects are more obvious compared to greenfield. On the other hand, a background of state ownership results in a tendency for firms to choose shared ownership over whole ownership in both acquisition and greenfield. In acquisition, this tendency is more obvious compared to in greenfield. It was also found that compared to greenfield, the degree of home market sub-national marketization could alleviate state ownership background firms in choosing share ownership compared to whole ownership. Empirical support was also found for the research model. Foreign operations whose establishment mode and ownership structure choices were selected according to prescribed theory out-performed those whose modes were selected otherwise. Unifying the previous issues raised by the choice of two modes and their impact on firms’ performances in this framework, integrated with an institution-based view, a more relevant test of the global value chain perspective is provided to explain EM MNEs investment activities in the international business arena

SSLRec: A Self-Supervised Learning Framework for Recommendation

Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-the-art performance in various recommendation scenarios (e.g., graph collaborative filtering, sequential recommendation, social recommendation, KG-enhanced recommendation), there is still a lack of unified frameworks that integrate recommendation algorithms across different domains. Such a framework could serve as the cornerstone for self-supervised recommendation algorithms, unifying the validation of existing methods and driving the design of new ones. To address this gap, we introduce SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders. The SSLRec framework features a modular architecture that allows users to easily evaluate state-of-the-art models and a complete set of data augmentation and self-supervised toolkits to help create SSL recommendation models with specific needs. Furthermore, SSLRec simplifies the process of training and evaluating different recommendation models with consistent and fair settings. Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field. Our implemented SSLRec framework is available at the source code repository https://github.com/HKUDS/SSLRec.Comment: Published as a WSDM'24 full paper (oral presentation

Measuring pollutant emissions of cattle breeding and its spatial-temporal variation in China

The rapid development of animal husbandry has resulted in serious pollution issues in the livestock and poultry breeding industry, increasing the cost of environmental management. This issue is particularly prominent in China due to its rapid economic development, significant domestic consumption, and aggressive carbon neutrality targets. This study analyses pollution emissions and spatial-temporal variation in China's cattle breeding industry. Using an emission coefficient method and panel data of 31 Chinese provinces/municipalities between 2002 and 2017, we measure the total volume of pollutant emissions from China's cattle breeding industry and five major pollutants: chemical oxygen demand, total nitrogen, total phosphorus, copper, and zinc. We also analyse the dynamic variation of the spatial distribution. The results show that both the total emissions volume and emissions of the five major pollutants have decreased to different extents, among which chemical oxygen demand has decreased the fastest. Spatial divergence is strengthened as the heavy pollution areas have moved from the southeast to the northwest of the country. This study contributes to current research by its focus on the cattle breading industry and by our improvements to the pollutant emission measurement method

A centi-pc-scale compact radio core in the nearby galaxy M60

M60, an elliptical galaxy located 16.5~Mpc away, has an active nucleus with a very low luminosity and an extremely low accretion rate. Its central supermassive black hole has a mass of $M_{\rm BH}\sim4.5\times10^{9}\, M_{\odot}$ and a Schwarzschild radii corresponding to $R_{\rm S}\sim5.4\,\mu\mathrm{as}$. To investigate the nature of its innermost radio nucleus, data from the Very Long Baseline Array (VLBA) at 4.4 and 7.6~GHz were reduced. The VLBA images reveal a compact component with total flux densities of $\sim$20~mJy at both frequencies, a size of $\leq$0.27~mas (99.7$\%$ confidence level), about 0.022~pc ($50\,R_{\rm S}$) at 7.6~GHz, and a brightness temperature of $\geq6\times10^{9}$~K. This suggests that the observed centi-parsec-scale compact core could be attributed to a nonthermal jet base or an advection-dominated accretion flow (ADAF) with nonthermal electrons. The extremely compact structure also supports the presence of an SMBH in the center. Our results indicate that M60 is a promising target for broad-band VLBI observations at millimeter wavelengths to probe ADAF scenarios and tightly constrain the potential photon ring (about 28\,$\mu$as) around its SMBH.Comment: 15 pages, 5 figures, 3 tables, accepted for publication in Astrophysical Journa

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility

Tumor Tissue-Derived Formaldehyde and Acidic Microenvironment Synergistically Induce Bone Cancer Pain

Background: There is current interest in understanding the molecular mechanisms of tumor-induced bone pain. Accumulated evidence shows that endogenous formaldehyde concentrations are elevated in the blood or urine of patients with breast, prostate or bladder cancer. These cancers are frequently associated with cancer pain especially after bone metastasis. It is well known that transient receptor potential vanilloid receptor 1 (TRPV1) participates in cancer pain. The present study aims to demonstrate that the tumor tissue-derived endogenous formaldehyde induces bone cancer pain via TRPV1 activation under tumor acidic environment. Methodology/Principal Findings: Endogenous formaldehyde concentration increased significantly in the cultured breast cancer cell lines in vitro, in the bone marrow of breast MRMT-1 bone cancer pain model in rats and in tissues from breast cancer and lung cancer patients in vivo. Low concentrations (1 similar to 5 mM) of formaldehyde induced pain responses in rat via TRPV1 and this pain response could be significantly enhanced by pH 6.0 (mimicking the acidic tumor microenvironment). Formaldehyde at low concentrations (1 mM to 100 mM) induced a concentration-dependent increase of [Ca(2+)]i in the freshly isolated rat dorsal root ganglion neurons and TRPV1-transfected CHO cells. Furthermore, electrophysiological experiments showed that low concentration formaldehyde-elicited TRPV1 currents could be significantly potentiated by low pH (6.0). TRPV1 antagonists and formaldehyde scavengers attenuated bone cancer pain responses. Conclusions/Significance: Our data suggest that cancer tissues directly secrete endogenous formaldehyde, and this formaldehyde at low concentration induces metastatic bone cancer pain through TRPV1 activation especially under tumor acidic environment.Multidisciplinary SciencesSCI(E)PubMed24ARTICLE4e10234

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Far-Reaching Impacts of African Dust- A Calipso Perspective

African dust can transport across the tropical Atlantic and reach the Amazon basin, exerting far-reaching impacts on climate in downwind regions. The transported dust influences the surface-atmosphere interactions and cloud and precipitation processes through perturbing the surface radiative budget and atmospheric radiative heating and acting as cloud condensation nuclei and ice nuclei. Dust also influences biogeochemical cycle and climate through providing nutrients vital to the productivity of ocean biomass and Amazon forests. Assessing these climate impacts relies on an accurate quantification of dust transport and deposition. Currently model simulations show extremely large diversity, which calls for a need of observational constraints. Kaufman et al. (2005) estimated from MODIS aerosol measurements that about 144 Tg of dust is deposited into the tropical Atlantic and 50 Tg of dust into the Amazon in 2001. This estimated dust import to Amazon is a factor of 3-4 higher than other observations and models. However, several studies have argued that the oversimplified characterization of dust vertical profile in the study would have introduced large uncertainty and very likely a high bias. In this study we quantify the trans-Atlantic dust transport and deposition by using 7 years (2007-2013) observations from CALIPSO lidar. CALIPSO acquires high-resolution aerosol extinction and depolarization profiles in both cloud-free and above-cloud conditions. The unique CALIPSO capability of profiling aerosols above clouds offers an unprecedented opportunity of examining uncertainties associated with the use of MODIS clear-sky data. Dust is separated from other types of aerosols using the depolarization measurements. We estimated that on the basis of 7-year average, 118142 Tg of dust is deposited into the tropical Atlantic and 3860 Tg of dust into the Amazon basin. Substantial interannual variations are observed during the period, with the maximum to minimum ratio of about 1.6 and 2.5 for the deposition to the tropical Atlantic and Amazon, respectively. The MODIS-based estimates appear to fall within the range of CALIPSO-based estimates; and the difference between MODIS and CALIPSO estimates can be largely attributed to the interannual variability, which is corroborated by long-term surface dust concentration observations in the tropical Atlantic. Considering that CALIPSO generally tends to underestimate the aerosol loading, our estimate is likely to represent a low bound for the dust transport and deposition estimate. The finding suggests that models have substantial biases and considerable effort is needed to improve model simulations of dust cycle