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.

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).

Blue energy—renewable power derived from the kinetic and potential forces of water—has become a key pathway for building sustainable and low-carbon energy systems. This review synthesizes global progress in hydropower, ocean energy, and hybrid offshore technologies, highlighting rapidly advancing wave-energy devices and triboelectric nanogenerators (TENGs) for blue energy. While hydropower is technologically mature, ocean-based energy systems are expanding due to vast resource potential and accelerating technological innovation. The review identifies promising designs for efficient wave-energy harvesting alongside remaining engineering challenges related to durability, cost, and large-scale deployment. Together, these insights provide a roadmap for advancing water-derived renewable energy to support global decarbonization.