Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.

This review provides an overview of the importance of nanoparticles in various fields of science, their classification, synthesis, reinforcements, and applications in numerous areas of interest. Normally nanoparticles are particles having a size of 100 nm or less that would be included in the larger category of nanoparticles. Generally, these materials are either 0-D, 1-D, 2-D, or 3-D. They are classified into groups based on their composition like being organic and inorganic, shapes, and sizes. These nanomaterials are synthesized with the help of top-down bottom and bottom-up methods. In case of plant-based synthesis i.e., the synthesis using plant extracts is non-toxic, making plants the best choice for producing nanoparticles. Several physicochemical characterization techniques are available such as ultraviolet spectrophotometry, Fourier transform infrared spectroscopy, the atomic force microscopy, the scanning electron microscopy, the vibrating specimen magnetometer, the superconducting complex optical device, the energy dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy to investigate the nanomaterials. In the meanwhile, there are some challenges associated with the use of nanoparticles, which need to be addressed for the sustainable environment.

New hybrid magnetic materials based on HDPE filled with Со and Ni nanoparticles have been prepared via the metal vapor synthesis. Properties of the metal-polymer composites have been elucidated as a function of MVS parameters and metal nature. The Faraday method has been applied to characterize the magnetic properties of the systems. The microstructure of the samples has been studied with a number of X-ray and synchrotron techniques, including XRD, EXAFS and SAXS. Core-level and valence band spectra were measured by XPS. The peak at binding energy of 282.8 eV characteristic of C-Ni bond was recorded in the C 1s spectrum. It was shown that properties of nanocomposite materials with similar compositions are determined both by the synthesis conditions and post-synthesis factors.

In this work, the structural transformations of a suboxide vacuum-deposited film of SiO_1.3 composition annealed in an inert atmosphere in a wide temperature range of 100 °C–1100 °C were characterized by the reflection-transmission spectroscopy technique. The experimental spectroscopic data were used to obtain the spectra of the absorption coefficient α(hν) in the absorption edge region of the film. Based on their processing, the dependences of Urbach energy E_U and optical (Tauc) bandgap E_o on the annealing temperature were obtained. An assessment of the electronic band gap (mobility gap) E_g was also carried out. Analysis of these dependences allowed us to trace dynamics of thermally stimulated disproportionation of the suboxide film and the features of the formation of nanocomposites consisting of amorphous and/or crystalline silicon nanoparticles in an oxide matrix.

Nickel Oxide (NiO) nanoparticles (NPs), doped with manganese (Mn) and cobalt (Co) at concentrations up to 8%, were synthesized using the composite hydroxide method (CHM). X-ray diffraction (XRD) analysis confirmed the formation of a cubic NiO structure, with no additional peaks detected, indicating successful doping. The average crystallite size was determined to range from 15 to 17.8 nm, depending on the dopant concentration. Scanning electron microscopy (SEM) images revealed mostly spherical, agglomerated particles, likely due to magnetic interactions. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the incorporation of Mn and Co into the NiO lattice, consistent with the XRD results. The dielectric properties exhibited a high dielectric constant at low frequencies, which can be attributed to ion jump orientation and space charge effects. The imaginary part of the dielectric constant decreased with increasing frequency, as it became harder for electrons to align with the alternating field at higher frequencies. Both the real and imaginary dielectric constants showed behavior consistent with Koop’s theory, increasing at low frequencies and decreasing at higher frequencies. Dielectric loss was primarily attributed to dipole flipping and charge migration. AC conductivity increased with frequency, and exhibited higher conductivity at high frequencies due to small polaron hopping. These co-doped NPs show potential for applications in solid oxide fuel cells.

The native peoples of the State of Mexico, especially the Mazahua community, present a high degree of marginality and food vulnerability, causing their inhabitants to be classified within the poor and extremely poor population. The objective of the research is to propose a food vulnerability index for the Mazahua community of the State of Mexico through the induction-deduction method, contrasting the existing literature with a semi-structured exploratory interview to identify the main factors that affect the native peoples. The study population was selected taking into account the number of inhabitants and poverty levels. The sources of information, in addition to documentary sources, were key informants and visits to Mazahua families that facilitated information about the different variables: natural, economic, social, cultural component, degree of adaptability and resilience for the creation and better understanding of the food vulnerability index in the communities under study.