A nationwide, multicenter trial in China compares tegoprazan-based (TACB) and esomeprazole-based (EACB) triple therapy plus bismuth for Helicobacter pylori. TACB has 93.5% eradication in the full analysis set, superior to EACB’s 86.4%, with comparable safety and high compliance.

A new 3D printing method developed by researchers at CUHK and USC eliminates the need for STL files in laser additive manufacturing.

By directly converting implicit geometries into optimized laser scanning toolpaths, the team achieved record-high printing resolution, faster computation, and stronger, smoother microscale metallic lattices.

This STL-free hybrid toolpath strategy reduces memory by 90%, shrinks wall thickness to 65 μm, and boosts strength by up to 66%. Applications range from aerospace brackets to copper heat exchangers.

Abstract

Purpose – Climate change has emerged as one of the new sources of financial risk, but it is still not recognized as a significant influencing factor in existing studies, especially in China. This study aims to investigate how climate policy changes in China affect intersectoral systemic risk from a mixed frequency model perspective.

Design/methodology/approach – We include asymmetric tail long memory for the dependence, which has not been covered by other risk-related literature, in the study of China’s sector risk contribution by proposing the TVM-MIDAS Copula model-based MES approach. Besides, we construct the GARCH-MIDAS-CPU model to investigate the impact of CPU on the contribution of systemic risk in the sector.

Findings – The results show that the real estate sector has the greatest tail dependence on the market, the raw materials sector has the longest memory of upper tail dependence, and the consumer sector has a weaker link to the market. For CPU, when the market falls moderately, CPU amplifies the volatility of the systematic risk contribution of the energy, materials, industrials, and real estate sectors and reduces the volatility of the risk contribution of the consumer, healthcare, and financial sectors. When the market plummets, the CPU amplifies the intensity of the volatility of systemic risk contributions from all sectors except the healthcare sector.

Originality/value – First, this paper analyzes how CPU influences systemic risk within Chinese sectors, offering confident evidence of the link between climate policy changes and sectoral risks. Second, it proposes a TVM-MIDAS copula model to capture dynamic tail dependence with tail memory advantages. Third, it utilizes a GARCH-MIDAS-CPU mixed-frequency model to examine the heterogeneous impact of CPU on systemic risk across sectors, addressing the co-frequency data downsampling issue and providing more precise insights.

Abstract

Purpose – We investigate latent higher-order dependencies in Chinese sectoral risk connectedness networks, characterize their topology and quantify resilience at both the system and sector levels, thereby offering new insights for mitigating systemic risk and preserving financial stability.

Design/methodology/approach – Employing the RHOSTS approach, we construct higher-order risk connectedness networks for Chinese stock sectors and analyze their structure with network-topology metrics. These metrics are then embedded in a coupled-map-lattice model to track the time-varying resilience of the overall network and its constituent sectors.

Findings – The sectoral network exhibits pronounced higher-order interactions, with four-sector synchronous resonance as the prevailing motif. Shock-specific core resonance clusters emerge and although system-wide resilience increases over time, marked heterogeneity across sectors persists.

Originality/value – By moving beyond traditional pairwise spillover models, our higher-order financial network reveals collective risk resonance spanning multiple sectors. The topology-based metrics we propose enable simultaneous assessment of system-level and sector-specific resilience and its evolution.

Abstract

Purpose – The primary objective of this research is to develop new algorithms in the framework of deep neural networks for the valuation of options under dynamics driven by stochastic volatility models. We aim to use the Heston model for equity options to demonstrate the accuracy of our approach.

Design/methodology/approach – Physics-informed neural networks (PINNs) are trained to minimize a loss function that includes terms from the partial differential equation residuals, initial condition and boundary conditions evaluated at selected points in the space-time domain. Speed and accuracy comparisons are carried out against single hidden-layer neural networks, called physics-informed extreme learning machines (PIELMs). American options are formulated as linear complementarity problems, and PINNs are applied in conjunction with penalty methods for the computation of the option prices.

Findings – For American options under the Heston model, PINNs yield accurate prices. Computed Greeks sensitivities are in close agreement with those reported for mesh-based methods. In contrast to mesh-based penalty methods for American options, PINNs work with smaller values of the penalty term. For the real estate index American option problem, numerical prices obtained using PINNs have comparable accuracies as those obtained by a high-order radial basis functions finite difference scheme.

Practical implications – There is a lack of reliable pricing models for pricing property derivatives. This work contributes to developing accurate neural network algorithms.

The development of flexible zinc-ion batteries (ZIBs) faces a three-way trade-off among the ionic conductivity, Zn²⁺ mobility, and the electrochemical stability of hydrogel electrolytes. To address this challenge, we designed a cationic hydrogel named PAPTMA to holistically improve the reversibility of ZIBs. The long cationic branch chains in the polymeric matrix construct express pathways for rapid Zn²⁺ transport through an ionic repulsion mechanism, achieving simultaneously high Zn²⁺ transference number (0.79) and high ionic conductivity (28.7 mS cm⁻¹). Additionally, the reactivity of water in the PAPTMA hydrogels is significantly inhibited, thus possessing a strong resistance to parasitic reactions. Mechanical characterization further reveals the superior tensile and adhesion strength of PAPTMA. Leveraging these properties, symmetric batteries employing PAPTMA hydrogel deliver exceeding 6000 h of reversible cycling at 1 mA cm⁻² and maintain stable operation for 1000 h with a discharge of depth of 71%. When applied in 4 × 4 cm² pouch cells with MnO₂ as the cathode material, the device demonstrates remarkable operational stability and mechanical robustness through 150 cycles. This work presents an eclectic strategy for designing advanced hydrogels that combine high ionic conductivity, enhanced Zn²⁺ mobility, and strong resistance to parasitic reactions, paving the way for long-lasting flexible ZIBs.