Abstract

Purpose – The impact of digital transformation on banks’ systemic risk merits thorough investigation.

Design/methodology/approach – This study examines the influence of digital transformation on banks’ systemic risk based on the fixed effect model with quarterly unbalanced panel data on 36 listed commercial banks in China from 2011 to 2020.

Findings – Results show that digital transformation has a negative impact on banks’ systemic risk by reducing both bank-specific tail risk and systemic linkage to extreme market shocks. Heterogeneity analysis suggests that digital transformation can significantly reduce systemic risk in national commercial banks relative to regional commercial banks, mediated through lowered management costs. Finally, this study finds an asymmetric relationship between digital transformation and banks’ systemic risk. Particularly, a desirable level of digital transformation can reduce systemic risk, while excessive digital transformation may exacerbate it.

Originality/value – These findings provide valuable guidance for promoting digital transformation for banks and mitigating systemic risk from digitalization.

Abstract

Purpose – We introduce a novel method for comparing the prices and the delta and vega risks for European options by considering Liu’s stock model of uncertainty and the stochastic Black and Scholes model. We aim to reveal the differences between the prices and risks under both approaches.

Design/methodology/approach – We develop an uncertainty approach to estimate the Greek letters delta and vega risks and establish two comparison criteria based on order relations and matrix norm metrics. The method is tested through a numerical experiment, incorporating a wide range of experimental and market parameters for strike price and maturity options and expert views for asset volatilities and preference levels.

Findings – We find four key facts: prices and risks of European call options differ significantly between the two approaches; uncertain young out-of-the-money call options are costly and riskier than the stochastic counterparts; the expert preference level is more important than the volatility for uncertain call options’ premiums and risks; and these, in turn, are sensitive for young options and across all strike prices.

Practical implications – We design a static delta hedging strategy for call options under uncertainty and find that although it is more expensive, it may offer better hedging than the stochastic counterpart. Thus, market hedgers may benefit more from the uncertainty framework rather than the stochastic one.

Originality/value – Our findings are summarized in a set of facts that could be considered to develop innovative foundations to support future research in artificial intelligence for financial risk management using the uncertainty theory.

Thermoelectric technology that utilizes thermodynamic effects to convert thermal energy into electrical energy has greatly expanded wearable health monitoring, personalized detecting, and communicating applications. Encouragingly, thermoelectric technology assisted by artificial intelligence exerts great development potential in wearable electronic devices that rely on the self-sustainable operation of human body heat. Ionic thermoelectric (i-TE) devices that possess high Seebeck coefficients and a constant and stable electrical output are expected to achieve an effective conversation of thermal energy harvesting. Herein, we developed an i-TE paster for thermal chargeable energy storage, temperature-triggered material recognition, contact/non-contact temperature detection, and photo thermoelectric conversion applications. An all-solid-state organic ionic gel electrolyte (PVDF-HFP-PEO gel) with onion epidermal cells-like structure was sandwiched between two electrodes, which take full advantage of a synergy between the Soret effect and the polymer thermal expansion effect, thus achieving the enhanced ZT value up to 900% compared with the PEO-free electrolyte. The i-TE device delivers a Seebeck coefficient of 28 mV K⁻¹, a maximum energy conversion efficiency of 1.3% in performance, and ultra-thin and skin-attachable properties in wearability, which demonstrate the great potential and application prospect of the i-TE paster in self-sustainable wearable electronics.

Professor Chuan He's research group at Southern University of Science and Technology reported an example of asymmetric Si–H/O–H coupling between racemic monohydrosilanes and alcohols in the same catalytic system, simultaneously achieving enantiomeric construction of the silicon chiral center and precise control of the Z/E configuration of the alkene. Through mechanistic studies combined with DFT calculations, the stereopolymerization of silicon chirality and the cis-trans isomerization process of the alkene were elucidated in detail. This reaction exhibits excellent yields and good to excellent enantiomeric selectivity, providing a new scheme for the efficient synthesis of four stereoisomers [( R,Z), (R,E), (S,Z), (S,E)]. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

Graphs are widely used to represent complex relationships in everyday applications such as social networks, bioinformatics, and recommendation systems, where they model how people or things (nodes) are connected through interactions (edges). Subgraph matching—the task of finding a smaller pattern, or query subgraph, within a larger graph—is crucial for detecting fraud, recognizing patterns, and performing semantic searches. However, current research on streaming subgraph, a similar task where timing is important, matching faces major challenges in scalability and latency, including difficulties in handling large graphs, low cache efficiency, limited query result reuse, and slow indexing performance. To address these issues, Liuyi Chen et al. presented a new framework that leverages a subgraph index based on graph embeddings, enabling effective caching and reuse of query results while demonstrating robustness and consistency across varying batch sizes and datasets. Their work was published in Intelligent Computing, a Science Partner Journal, under the title “Accelerating Streaming Subgraph Matching via Vector Databases”.

A research paper by scientists from Beihang University proposed a machine learning (ML)-driven cerebral blood flow (CBF) prediction model, featuring multimodal imaging data integration and an interactive web application to address the challenge of quantitative CBF assessment during long-duration spaceflight.

A research paper by scientists at The University of New South Wales presented a new hydraulic-driven dual soft robotic system featuring a 3 DOF-soft cutting arm (SCA) and a 3-jaw teleoperated soft grasper system (TSGS).