The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

In the vital context of education, the application of artificial intelligence (AI) to assessments necessitates a nuanced examination of the boundaries between ethically permissible and impermissible practices. In this chapter, the authors applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped key ethical principles to different assessment phases in a triadic ontological framework. The chapter looks to provide researchers and practitioners the insights into the ethical challenges that exist across an end-to-end assessment pipeline.

Purpose Blood-brain barrier disruption (BBBD) is of interest for treating neurodegenerative diseases and tumors by enhancing drug delivery. Focused ultrasound (FUS) is a powerful method to alleviate BBB challenges; however, the detection of BBB opening by non-invasive methods remains limited. The purpose of this work is to demonstrate that 3D transcranial color Doppler (3DCD) and photoacoustic imaging (PAI) combined with custom-made nanoparticle (NP)-mediated FUS delivery can detect BBBD in mice. Procedures We use MRI and stereotactic ultrasound-mediated BBBD to create and confirm four openings in the left hemisphere and inject intravenously indocyanine green (ICG) and three sizes (40 nm, 100 nm, and 240 nm in diameter) of fluorophore-labeled NPs. We use PAI and fluorescent imaging (FI) to assess the spatial distribution of ICG/NPs in tissues. Results A reversible 41 ± 12 % (n = 8) decrease in diameter of the left posterior cerebral artery (PCA) relative to the right after FUS treatment is found using CD images. The spectral unmixing of photoacoustic images of the in vivo (2 h post FUS), perfused, and ex vivo brain reveals a consistent distribution pattern of ICG and NPs at *FUS locations. Ex vivo spectrally unmixed photoacoustic images show that the opening width is, on average, 1.18 ± 0.12 mm and spread laterally 0.49 ± 0.05 mm which correlated well with the BBB opening locations on MR images. In vivo PAI confirms a deposit of NPs in tissues for hours and potentially days, is less sensitive to NPs of lower absorbance at a depth greater than 3 mm and too noisy with NPs above an absorbance of 85.4. FI correlates well with ex vivo PAI to a depth of 3 mm in tissues for small NPs and 4.74 mm for large NPs. Conclusions 3DCD can monitor BBBD over time by detecting reversible anatomical changes in the PCA. In vivo 3DPAI at 15 MHz combined with circulating ICG and/or NPs with suitable properties can assess BBB opening 2 h post FUS.

The rise of AI in educational assessments has significantly enhanced efficiency and accuracy. However, it also introduces critical ethical challenges, including bias in grading, data privacy risks, and accountability gaps. These issues can undermine trust in AI-driven assessments and compromise educational fairness, making a structured ethical framework essential. To address these challenges, this study empirically validates an existing triadic ethical framework for AI-assisted educational assessments, originally proposed by Lim, Gottipati and Cheong (In: Keengwe (ed) Creative AI tools and ethical implications in teaching and learning, IGI Global, 2023), grounded in student perceptions. The framework encompasses three ethical domains—physical, cognitive, and informational—which intersect with five key assessment pipeline stages: system design, data stewardship, assessment construction, administration, and grading. By structuring AI-driven assessments within this ethical framework, the study systematically maps key concerns, including fairness, accountability, privacy, and academic integrity. To validate the proposed framework, Structural Equation Modeling (SEM) was employed to examine its relevance and alignment with learners' ethical concerns. Specifically, the study aims to (1) evaluate how well the triadic framework aligns with learners' perceptions of ethical issues using SEM analysis, and (2) examine relationships among the assessment pipeline stages, ethical considerations, pedagogical outcomes, and learner experiences. Findings reveal robust connections between AI-assisted assessment stages, ethical concerns, and learners' perspectives. By bridging theoretical validation with practical insights, this study emphasizes actionable strategies to support the development of AI-driven assessment systems that balance technological efficiency, pedagogical effectiveness, and ethical responsibility.

This study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities, to improve the liquidity provision of real-world AMMs. The proposed architecture augments Uniswap V3, a cryptocurrency AMM protocol, by using a novel market equilibrium pricing to reduce divergence and slippage losses. Furthermore, the proposed architecture involves a predictive AMM capability, for which a deep hybrid long short-term memory (LSTM) and Q-learning reinforcement learning framework is used. It seeks to improve market efficiency through obtaining more accurate forecasts of liquidity concentration ranges, where liquidity starts moving to expected concentration ranges prior to asset price movement; thus, liquidity utilization is improved. The augmented protocol framework is expected to have practical real-world implications through (1) reducing divergence loss for liquidity providers; (2) reducing slippage for crypto-asset traders; and (3) improving capital efficiency for liquidity provision for the AMM protocol. The proposed architecture is empirically benchmarked against the well-established Uniswap V3 AMM architecture. The preliminary findings indicate that the novel AMM framework offers enhanced capital efficiency, reduced divergence loss, and diminished slippage, which could potentially address several of the challenges inherent to AMMs.