University of Hafr Al-Batin

Integrated Reporting (IR) aims to present a holistic view of value creation by combining financial and non-financial information, including sustainability and ESG indicators. However, during the COVID-19 pandemic, companies in Saudi Arabia faced challenges related to delays in disclosure, limited transparency in non-financial information, and difficulties in providing timely and integrated reports due to operational disruptions and remote reporting systems. This study examines the impact of digital transformation on integrated reporting quality, the influence of pre- vs. post-COVID conditions, and the moderating effect of COVID-19 on the relationship between digital transformation and IR quality. Using a quantitative longitudinal approach, panel data from firms listed on the Saudi Stock Exchange (Tadawul) were analyzed using Stata over the periods before and after the pandemic (2018–2019 vs. 2020–2021/2022). The results reveal that digital transformation significantly improves IR quality, IR quality differs significantly between the pre- and post-COVID periods, and COVID-19 strengthens the relationship between digital transformation and IR quality. These findings highlight the importance of advanced digital technologies to enhance transparency, speed, and reliability in corporate reporting.

The integration of Artificial Intelligence (AI) into Internet of Things (IoT) medical devices has revolutionized arrhythmia monitoring. However, the high computational and power demands of traditional Deep Learning (DL) models pose significant challenges for long-term, battery-operated smart electronics. Spiking Neural Networks (SNNs), inspired by the biological efficiency of the human brain, offer a promising solution. This paper reviews the intersection of SNNs, low-power IoT hardware, and biomedical signal processing. I examine the transition from frame-based to event-driven processing, and discuss the hardware–software co-design necessary for next-generation cardiac wearables.

The present study integrated supramolecular analysis with a range of in silico approaches, including crystal structure determination, target prediction, ADMET (absorption, distribution, metabolism, and excretion) profiling, network pharmacology, and molecular docking to investigate the anticancer potential of compound 3 (tetrabromothia-bridged diquinoline). In the solid state, Compound 3 was found to incorporate p-xylene, yielding a stable inclusion complex [(3)2. (p-xylene)], the structure of which was elucidated and interpreted through the lenses of crystal engineering and supramolecular chemistry. Moreover, compound 3 exhibited favourable drug-likeness and strong predicted binding affinity for EGFR and ERBB2 (HER2), suggesting that modulation of EGFR tyrosine kinase inhibitor resistance, along with ERBB signalling pathways, may underpin its potential anticancer activity.

Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by recurrent itching, predominantly affecting children but also impacting adults. Geraniol, a monoterpene alcohol found in various aromatic plant-based essential oils, possesses a pleasant rose-like scent. This study aimed to investigate the therapeutic potential of geraniol in a mouse model of atopic dermatitis by elucidating its anti-inflammatory and immunomodulatory properties. Mice were subjected to 2% 2,4-Dinitrochlorobenzene (DNCB) to induce AD, and treated with both oral and topical administrations of prednisolone and geraniol from day 7 to day 19. Macroscopic assessments of ear and dorsal skin, as well as ear thickness, were conducted on days 0, 7, and 19. Total leukocyte count (TLC) and differential leukocyte count (DLC) were measured in blood samples using an automatic hematology analyzer. Ear tissues were analyzed for mRNA expression levels of IL-4 and IL-13 via reverse transcription quantitative polymerase chain reaction (RT-qPCR), and molecular docking studies were performed to evaluate the binding affinity of geraniol to these cytokines. Histopathological examination using hematoxylin and eosin staining was conducted on ear and dorsal skin tissues to assess eosinophil and mast cell infiltration, as well as epidermal thickness. The results demonstrated that both oral and topical geraniol significantly alleviated AD-like symptoms. Geraniol treatment led to a reduction in DLC and TLC levels in the blood, as well as downregulation of IL-4 and IL-13 expression in ear tissue. In silico studies revealed that geraniol exhibited moderate binding affinities of --4.5 kcal/mol with IL-4 and --4.9 kcal/mol with IL-13. Histopathological analysis indicated a reduction in epidermal thickness and infiltration of mast cells and eosinophils in geraniol-treated mice. In conclusion, geraniol effectively alleviated atopic dermatitis in mice by reducing clinical scores, inflammatory cell infiltration, epidermal thickening, and regional downregulation of IL-4 and IL-13 mRNA expression. The in silico docking studies support the hypothesis of a potential Th2-modulatory effect of geraniol.

The current study investigates the factors affecting students' behavioral intentions to utilize "artificial intelligence" (AI) applications in learning by extending the "unified theory of acceptance and use of technology" (UTAUT) model with the inclusion of cognitive flexibility. Data were collected from 315 undergraduate students and analyzed employing a two-step approach involving "structural equation modeling" (SEM) with AMOS. The measurement model was first run to assess and confirm the reliability and validity of the variables, followed by the structural model to test the hypothesized relationships. The results revealed that "performance expectancy (PE), effort expectancy (EE), and social influence" (SI) exerted significant positive effects on students' behavioral intentions to use AI applications, while "facilitating conditions" (FC) exhibited no significant impact. Additionally, "cognitive flexibility" (CF) did not directly influence behavioral intention but exerted a significant indirect effect through its influence on PE, EE, and SI. These findings underscore the significance of students' cognitive adaptability in shaping their perceptions of technological usefulness and ease of use and highlight the value of integrating psychological variables into technology acceptance models in educational contexts. Implications for educational technology implementation and future research directions are discussed.