A Graph Learning Model of Network Resources for Early Stage Startup Success Prediction

With high profitability accompanied by high risks, startups are driving industry evolution and innovation. However, due to high information asymmetry, startup success prediction remains challenging. From the perspective of network resources, we propose a variant heterogeneous graph attention network (ResourceNet) to model how a focal startup can access and leverage network resources from inter-organizational networks connected by investment, co-portfolio, and VC syndication relationships, for future success. We follow the design science paradigm to develop the node and link-aware attention mechanisms in graph network representations that jointly explore the impact of different mechanisms explaining the value of network resources, i.e., reach, richness, and receptivity. This project provides contributions to the startup success prediction studies by demonstrating the value of network resources in a topological interorganizational network, and also important managerial implications for startup companies (to seek network resources for future success) and VC (to pick the winners)

Graph Learning of Multifaceted Motivations for Online Engagement Prediction in Counter-party Social Networks

Social media has emerged as an essential venue to invigorate online political engagement. However, political engagement is multifaceted and impacted by both individuals\u27 self-motivation and social influence from peers and remains challenging to model in a counter-party network. Therefore, we propose a counter-party graph representation learning model to study individuals\u27 intrinsic and extrinsic motivations for online political engagement. Firstly, we capture users\u27 intrinsic political interests providing self-motivation from a user-topic network. Then, we encode how users cast influence on others from the inner-/counter-party through a user-user network. With the learned embedding of intrinsic and extrinsic motivations, we model the interactions between these two facets and utilize the dependency by deep sequential model decoding. Finally, extensive experiments using Twitter data related to the 2020 U.S. presidential election and the 2019 HK protests validate the model\u27s predictive power. This study has implications for online political engagement, political participation, and political polarization

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies

Fractal Dimension Analysis of Pores in Coal Reservoir and Their Impact on Petrophysical Properties: A Case Study in the Province of Guizhou, SW China

Coal is a complex, porous medium with pore structures of various sizes. Therefore, it is difficult to accurately describe the characteristics of pore structure by using the traditional geometry method. The results from the present investigation suggest that the porous media system of the coal reservoir has obvious fractal characteristics at different scales. To study the complexity of the pores in the coal reservoir, 27 coal samples from Guizhou, SW China were studied. The fractal dimensions of coal pores were calculated, and the fractal dimension of a pore in a coal reservoir can be classified into two types: percolation and diffusion. The comprehensive fractal dimension can be obtained using the weighted summation method and the pore volume fraction of different fractal segments as the weight. The percolation fractal dimensions (Dp) of coal samples are between 2.88 and 3.12, the diffusion fractal dimensions (Dd) are between 3.57 and 3.84, and the comprehensive fractal dimensions (Dt) are between 3.05 and 3.63. The Dd values of all coal samples are all larger than the Dp values, which indicates that the random distribution and complexity of diffusion pores in coal are stronger than those of the percolation pores. The percolation fractal dimension decreases as the maturity degree increases, whereas the diffusion and comprehensive fractal dimensions increase. The diffusion pore volume fraction and total pore volume are all highly correlated with the comprehensive and diffusion fractal dimensions, respectively. The correlation between the comprehensive fractal dimension, diffusion pore volume fraction, and coal reservoir porosity is negative exponential, whereas the correlation between the total pore volume and coal reservoir porosity is positive linear. In comparison with the percolation and diffusion fractal dimensions, the comprehensive fractal dimension is better suited for characterizing the permeability of coal reservoirs. The fractal analysis of this paper is beneficial for understanding the relationship between the fractal characteristics of coal pores and properties

Controlling Factors of Organic Matter Enrichment in Marine–Continental Transitional Shale: A Case Study of the Upper Permian Longtan Formation, Northern Guizhou, China

The marine–continental transitional shale of the Upper Permian Longtan Formation in northern Guizhou is an important source rock in the upper Yangtze region of China, and it holds significant potential for the exploration of shale gas. To investigate the correlation between sedimentary conditions and the accumulation of organic matters in marine–continental transitional shale, this paper performed an extensive analysis using organic geochemical testing, organic petrology examination, a cross-section polisher–scanning electron microscope (CP-SEM), and geochemical analysis. The Jinsha and Dafang drilling cores were selected as the research subjects. The results showed that the TOC of the Longtan Formation in the study area was relatively high, and the TOC content of the tidal flat–lagoon environment (average of 8.37%) was significantly higher than that of the delta samples (average of 2.77%). The high content of Al2O3 (average of 17.41% in DC-1, average of 16.53% in JC-1) indicated strong terrigenous detrital input. The proxies indicated that the Longtan Formation shale in northern Guizhou was deposited in a climate that was both warm and humid, with oxic–dysoxic sedimentary water characterized by high biological productivity and a rapid sedimentation rate. The organic-rich shales during the marine and continental transitional phases were affected by various factors, including the paleo-climate, water redox properties, paleo-productivity, sedimentation rate, and other variables, which directly or indirectly impacted the availability, burial, and preservation of organic matter

Photo-Chlorination of Linear Alkanes with 2-Position Selectivity Using a Metal-Organic Layer Catalyst

Controlling regioselectivity in activating C−H bonds in linear alkanes is challenging, as their multiple secondary C−H bonds have quite similar dissociation energies with no functional groups to differentiate between the bonds. Amidyl radicals generated from N‒halogen amides were reported to activate C−H bonds with an interesting 2-position selectivity. Here, with a possibility to access the amidyl radical photocatalytically, we coupled ligand-to-metal charge transfer (LMCT)-based radical generation and amide functional group on a tailor-designed metal-organic layer (MOL) material. We achieved efficient photo-chlorination of linear alkanes with 2-position selectivity. For example, with n-hexane as the substrate, 2-chloro-n-hexane was obtained with 85% selectivity and a turnover number of 2200 in 8 hours, together with a high apparent quantum yield of ~7% at room temperature. Transient absorption spectroscopy reveals that a FeIV species is involved in the initial photo-driven process that possibly oxidizes the amide center to an amidyl radical

Manipulation of metavalent bonding to stabilize metastable phase: A strategy for enhancing zT in GeSe

Abstract Exploration of metastable phases holds profound implications for functional materials. Herein, we engineer the metastable phase to enhance the thermoelectric performance of germanium selenide (GeSe) through tailoring the chemical bonding mechanism. Initially, AgInTe2 alloying fosters a transition from stable orthorhombic to metastable rhombohedral phase in GeSe by substantially promoting p‐state electron bonding to form metavalent bonding (MVB). Besides, extra Pb is employed to prevent a transition into a stable hexagonal phase at elevated temperatures by moderately enhancing the degree of MVB. The stabilization of the metastable rhombohedral phase generates an optimized bandgap, sharpened valence band edge, and stimulative band convergence compared to stable phases. This leads to decent carrier concentration, improved carrier mobility, and enhanced density‐of‐state effective mass, culminating in a superior power factor. Moreover, lattice thermal conductivity is suppressed by pronounced lattice anharmonicity, low sound velocity, and strong phonon scattering induced by multiple defects. Consequently, a maximum zT of 1.0 at 773 K is achieved in (Ge0.98Pb0.02Se)0.875(AgInTe2)0.125, resulting in a maximum energy conversion efficiency of 4.90% under the temperature difference of 500 K. This work underscores the significance of regulating MVB to stabilize metastable phases in chalcogenides

The Exploration of Fetal Growth Restriction Based on Metabolomics: A Systematic Review

Fetal growth restriction (FGR) is a common complication of pregnancy and a significant cause of neonatal morbidity and mortality. The adverse effects of FGR can last throughout the entire lifespan and increase the risks of various diseases in adulthood. However, the etiology and pathogenesis of FGR remain unclear. This study comprehensively reviewed metabolomics studies related with FGR in pregnancy to identify potential metabolic biomarkers and pathways. Relevant articles were searched through two online databases (PubMed and Web of Science) from January 2000 to July 2022. The reported metabolites were systematically compared. Pathway analysis was conducted through the online MetaboAnalyst 5.0 software. For humans, a total of 10 neonatal and 14 maternal studies were included in this review. Several amino acids, such as alanine, valine, and isoleucine, were high frequency metabolites in both neonatal and maternal studies. Meanwhile, several pathways were suggested to be involved in the development of FGR, such as arginine biosynthesis, arginine, and proline metabolism, glyoxylate and dicarboxylate metabolism, and alanine, aspartate, and glutamate metabolism. In addition, we also included 8 animal model studies, in which three frequently reported metabolites (glutamine, phenylalanine, and proline) were also present in human studies. In general, this study summarized several metabolites and metabolic pathways which may help us to better understand the underlying metabolic mechanisms of FGR