Abstract

Purpose – This paper originally proposed the fuzzy option pricing method for green bonds. Based on the requirements of arbitrage equilibrium, this paper draws on Merton's corporate bond option pricing model.

Design/methodology/approach – Describing the asset value behavior of green bond issuing enterprises through diffusion-jump processes to reflect the uncertainty brought by carbon emission reduction policies and technologies, using approximation methods to get the analytical pricing formula and then, using a fuzzification technique of Choquet expectation under  λ-additive fuzzy measures after considering fuzzy factors, the paper provides fuzzy intervals for the parity coupon rates of green bonds with different subjective levels for investors.

Findings – The paper proposes and argues the classical and fuzzy option pricing methods in turn for both corporate ordinary bonds and green bonds, considering carbon risk or climate risk. It implements the scenario analysis varying with industry emission standards and discusses the sensitiveness of the related key parameters of the option.

Practical implications – The fuzzy option pricing for the green bonds provides the scope of the variable equilibrium values, operational theoretical supports and some policy implications of carbon reduction and promoting green funding.

Originality/value – The logic of introducing the fuzziness of the option pricing for the green bonds lies with considering the existence of fuzzy information about the project supported by the green bond and the subjectivity of investors and it also responds to changes in technological uncertainty and policy uncertainty in the process of “carbon peaking and carbon neutrality.”

Laboratory medicine is an essential part of the diagnostic process, supporting clinical decisions, guiding and addressing therapy. The recent COVID-19 pandemic illustrated well the key role of laboratory medicine in the diagnosis, management and prognosis of SARS-CoV-2 infected patients. Technological advances improved the laboratory diagnosis and patients’ management and others appear very promising as clustered regularly interspaced short palindromic repeats (CRISPR) or artificial intelligence (AI). This review describes the current diagnostic assays routinely used in laboratory as well as the novel technologies not in routine yet but that represent future directions and will probably dominate the laboratory in the next years. Serology is important for detecting antibodies and/or antigens of the infectious pathogens or for epidemiological purposes, while real-time PCR with its high sensitivity and specificity has a key role in pathogen detection in different biological matrices and in monitoring the therapy. Nanochip-based technologies make possible delivering a laboratory report at the patient’s bed or in settings where a laboratory-based hospital is not available. Next generation sequencing (NGS) is a massively high throughput parallel sequencing technology that allows the simultaneous sequence of billions of DNA fragments in a short time frame. This technology can be used to detect drug-associated mutations, minority species within an infected patient or for pathogen identification. CRISPR-based technology is a fast and accurate diagnostic method that can be applied to different human diseases including infectious diseases. Artificial intelligence is increasingly used in laboratory medicine. In clinical microbiology, it is used to build up diagnosis analyzing genomic information or mass spectra from isolated bacteria, for predicting antibiotic sensitivity or for processing in a short time a large number of images with meaningful results. Thus, the laboratory is becoming increasingly automated and interwoven with sophisticated software or algorithms that will increase the sensitivity and specificity of diagnoses, besides reducing time to results.

Mass spectrometry (MS) is an analytical technique for molecular identification and characterization, with applications spanning various scientific disciplines. Despite its significance, MS faces challenges in widespread adoption due to cost constraints, instrument portability issues, and complex sample handling requirements. In recent years, 3D printing has emerged as a technology across industries due to its cost-effectiveness, customization capabilities, and rapid prototyping features. This review explores the integration of 3D printing with MS technology to overcome existing limitations and enhance biomedical analysis capabilities. We first categorize mainstream 3D printing methods and assess their potential in MS applications. We also discuss their roles in different MS categories such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GCMS), ambient ionization mass spectrometry (AIMS), and matrix-assisted laser desorption/ionization MS (MALDI MS) in biomedical research. Additionally, we highlight the current challenges and future research directions for advancing 3D printing-assisted mass spectrometry, emphasizing its role in enabling portable, cost-effective, and customized MS solutions for biomedical analysis.

This article introduces a new methodology for predicting the service life of concrete structures in marine environments, published in Engineering. Researchers from Southeast University have developed a multiscale modeling approach combined with extensive data analysis to efficiently assess the durability of marine concrete structures. By leveraging numerical simulations and real-world datasets, the study offers a more efficient alternative to traditional long-term exposure testing, providing valuable insights for the design and maintenance of concrete structures in marine conditions.

This article explores the potential for Africa to develop its soybean industry and contribute to global trade. It highlights how recent advancements in breeding and technology, along with China–Africa STI partnerships, could help Africa overcome historical challenges in soybean cultivation and tap into the growing demand from China. The piece also examines the socio-economic benefits and environmental considerations of expanding soybean production on the continent.

In the realm of infrastructure maintenance, a novel approach to detecting concrete bridge damage has emerged. Researchers have developed an automated detection method using an enhanced version of the YOLO (You Only Look Once) algorithm, augmented with Vision Transformers. This advancement aims to improve the accuracy and efficiency of identifying structural defects in bridges, particularly those captured by drones under challenging real-world conditions. The study, published in Engineering, highlights the potential of this technology to enhance bridge inspection processes.