Balancing equity and policyholder protection: Assessing insurer's interests in green lending under cap-and-trade regulations.

This paper presents a contingent claim model designed to assess an insurer's equity within the framework of carbon trading regulations imposed on borrowing firms while also considering the integration of green lending. The development of this model is particularly relevant for regions with established carbon trading markets, with a specific focus on the post-period following the 2015 Paris Agreement concerning climate change. We focus on shareholders and policyholders to optimize equity and ensure maximum protection. Strict caps in cap-and-trade harm interest margins, reducing guaranteed rates for equity maximization and compromising policyholder protection. Government intervention through sustainable production carbon trading hinders win-win outcomes. Green subsidies can improve insurer margins, but achieving win-win solutions remains challenging. A collective approach is needed to share sustainable production and finance benefits among diverse economic sectors.

Decoupled learning for brain image registration.

Image registration is one of the important parts in medical image processing and intelligent analysis. The accuracy of image registration will greatly affect the subsequent image processing and analysis. This paper focuses on the problem of brain image registration based on deep learning, and proposes the unsupervised deep learning methods based on model decoupling and regularization learning. Specifically, we first decompose the highly ill-conditioned inverse problem of brain image registration into two simpler sub-problems, to reduce the model complexity. Further, two light neural networks are constructed to approximate the solution of the two sub-problems and the training strategy of alternating iteration is used to solve the problem. The performance of algorithms utilizing model decoupling is evaluated through experiments conducted on brain MRI images from the LPBA40 dataset. The obtained experimental results demonstrate the superiority of the proposed algorithm over conventional learning methods in the context of brain image registration tasks.

Regiodivergent and stereoselective hydroxyazidation of alkenes by biocatalytic cascades.

Asymmetric functionalization of alkenes allows the direct synthesis of a wide range of chiral compounds. Vicinal hydroxyazidation of alkenes provides a desirable path to 1,2-azidoalcohols; however, existing methods are limited by the control of stereoselectivity and regioselectivity. Herein, we describe a dual-enzyme cascade strategy for regiodivergent and stereoselective hydroxyazidation of alkenes, affording various enantiomerically pure 1,2-azidoalcohols. The biocatalytic cascade process is designed by combining styrene monooxygenase-catalyzed asymmetric epoxidation of alkenes and halohydrin dehalogenase-catalyzed regioselective ring opening of epoxides with azide. Additionally, a one-pot chemo-enzymatic route to chiral ß-hydroxytriazoles from alkenes is developed via combining the biocatalytic cascades and Cu-catalyzed azide-alkyne cycloaddition.

One-pot synthesis of benzo[c]carbazoles by photochemical annulation of 2-chloroindole-3-carbaldehydes.

A novel and efficient procedure for the synthesis of benzo[c]carbazoles has been achieved in moderate to high yields by the one-pot photochemical annulations of 2-chloroindole-3-carbaldehydes by styrenes via photodechlorination-initiated coupling of 2-chloroindole-3-carbaldehydes with styrenes, electrocyclic reactions and deformylative aromatization in the presence of pyridine.