Dayananda Sagar University

The integration of Artificial Intelligence (AI) into forensic accounting has significantly enhanced the detection of fraud, financial manipulation, and anomalies in corporate reporting. However, the increasing reliance on AI systems raises critical concerns related to governance, ethics, and regulatory compliance. This chapter examines the concept of responsible AI within the domain of forensic accounting, focusing on transparency, fairness, accountability, and data integrity. It proposes a governance framework that aligns AI deployment with ethical auditing practices and evolving regulatory standards. By analysing global developments and practical challenges, the chapter highlights the risks of algorithmic bias, opacity, and misuse of automated systems. It offers strategic recommendations for ensuring that AI-driven forensic practices remain reliable, auditable, and aligned with sustainability and ESG objectives.

Assessment of skeletal bone age is an essential method and extensively applied across medical and scientific disciplines to estimate the developmental progression of an individual’s bones. Unlike chronological age, the skeleton bone age provides a more accurate outcome that measures the individual’s biological and physiological maturity. Based on this, a bone age assessment is essential for assessing the overall growth, especially in specialized applications, such as orthodontics. However, the process of determining bone age is complex and time-consuming. It usually requires the examination of hand bone X-rays and comparison with established reference standards, a task that demands expert analysis. This manual procedure is susceptible to inconsistencies, both between different evaluators (inter-observer differences) and within the same evaluator over time (intra-observer differences), resulting in variable outcomes. Therefore, a fully automated, reliable, and accurate model is developed for assessing skeletal bone age. At first, the relevant input images are collected from standard source. These images are given to the developed Multi-Convolutional with Multi-Attention based Residual Densenet with Optimal Tuning Model for performing the skeletal bone age assessment. Here, each convolution carries each attention type, including spatial attention, region attention and sparse attention mechanism. Additionally, the parameters of the MC-MARDNet are optimized by utilizing the Modified Random Attribute-based Flower Fertilization Optimization (MRA-FFO). This model supports in monitoring the children’s physical development and also identify the growth hormone deficiency. The performance estimation of the developed model shows resultant outcomes when compared with traditional methodologies.

Environmental, social and governance reporting creates opportunities for accountability and misleading environmental communication. This conceptual chapter examines how natural language processing and machine learning can support forensic scrutiny of sustainability disclosures. A critical literature review distinguishes textual classification, claim verification and professional findings. The proposed seven-layer framework connects document acquisition, structured extraction, linguistic analysis, numerical consistency testing, risk prioritisation, forensic validation and reporting. A worked application to Microsoft's public sustainability disclosure illustrates how reporting boundaries and relative measures affect interpretation. Current reporting and assurance developments, including ISSA 5000, inform the practical context. The framework offers an auditable decision process; its detection performance and assurance benefits require empirical validation.

Nonlinear wave equations exhibit rich interplay among dispersion, nonlinearity, coupling, dissipation, and external forcing, producing diverse coherent and complex wave structures. Although exact travelling-wave solutions provide analytical benchmarks, their construction alone does not reveal underlying phase-space, bifurcations, stability, or responses to perturbations. This review presents a reduction-based methodological framework connecting nonlinear partial differential equations(PDEs) to reduced dynamical systems, invariant phase-space structures, exact waveform reconstruction, stability analysis, and verification in PDEs. Its applicability and limitations are examined across Schrödinger-type, coupled, nonparaxial, dissipative, magnetic, shallow-water, and fractional wave equations. Particular emphasis is placed on correspondence between analytical waveforms and invariant orbits. Equilibria, periodic trajectories, homoclinic orbits, and heteroclinic connections geometrically represent constant, periodic, localized, and front-like structures, respectively. This review distinguishes existence and stability of invariant structures from onset of chaos, emphasizing complementary diagnostics rather than reliance on phase-portraits or finite-time indicators alone. Major methodological gaps include incomplete parameter-space characterization, weak correspondence between reduced models and full-PDE dynamics, ambiguities in generalized and fractional formulations, limited robustness analysis, inadequate numerical reproducibility, and insufficient links to experimental observables. The framework prioritizes physical admissibility, stability, robustness, and predictive relevance over generation of additional formal solutions. It supports reliable nonlinear wave prediction, stability assessment, control, and system design in fluid, optical, plasma, and other nonlinear physical systems.

This review focused on developing the nanomedicine through inclusive discussions about synthesis, physicochemical properties, in vitro and in vivo biomedical applications of different nanoparticles (NPs). Liposomal NPS encapsulates the hydrophobic drugs in a lipid bilayer allowing for targeted delivery to specific tissues while reducing toxicity. The polymeric NPs are made from biodegradable material-controlled release and deliver drugs including biomacromolecules, resulting in therapeutic applications. Gold nanoparticles (AuNPs) exhibit distinctive optical and electrical properties, where they are used for targeted therapy and imaging in drug delivery systems (DDS). The current advancements focus on controlling the size, shape, and surface chemistry to develop smart NPs. Nanotechnology could provide numerous benefits, such as treating long-lasting human diseases by site-specific drug efficacy and target-oriented delivery of exact nanomedicines. Different synthesized NPs affect the lifelong activities of tumor cells, breast cancer, and several other related diseases due to their outstanding capability to control chemotherapy and immunotherapy, respectively. Drug nanomaterials are designed to deliver the target tissues resulting in declining toxicity and increasing patients’ compliance with a lower dosing index. The modern novelty of NPs lies in their ability to exhibit applications, specifically sustainable green synthesis, stimuli-responsiveness, multifunctional agents in biomedicine and environmental remediation, respectively. The integration of NPs based systems into medicine highlights their potential to develop drug delivery by improving efficacy and safety profiles in future perspectives.