State University of Novi Pazar

Five neutral heteroleptic mononuclear vanadium(IV) hydrazone complexes ([VOL(bpy)]), derived from 2-hydroxy-5-methylacetophenone and various acid hydrazides (furoic, thiophene, benzoic, nicotinic, and isoniazid), were synthesized and shown to exhibit improved antidiabetic efficacy in streptozotocin-induced diabetic rats, with reduced toxicity and minimal bioaccumulation compared to maltolato- and picolinato-based vanadium species. Structural identity was established by spectroscopic methods. Crystal structures were obtained for four complexes, providing insight into their solid-state chemistry. Stability studies in simulated intestinal and gastric fluids showed that the complexes largely retained their integrity under intestinal conditions, whereas decomposition occurred in the highly acidic gastric environment within several minutes. In vivo experiments revealed a structure-antihyperglycemic activity relationship. The nicotinic-containing complex showed the highest activity, reducing blood glucose levels by 67% within 7 days of treatment, while the remaining complexes improved glycemic control by more than 50%. Bioaccumulation studies demonstrated <1.1% uptake in the liver and kidneys and negligible accumulation in the brain. The presented vanadium compounds enhance antidiabetic potential by addressing key limitations, particularly bioaccumulation and toxicity, associated with vanadium agents previously evaluated in clinical trials.

The Sombor index of a graph G, introduced by Gutman in 2021, is a topological index based on vertex degrees. It is defined by SO(G) =& sum;(uv is an element of E(G))root d(u)(2) + d(v)(2), where d(u) and d(v) denote the degrees of the end-vertices of edge uv. In this paper we provide a lower bound for the Sombor index of trees with a given independence number alpha and order n, and characterize trees that achieve this bound. This study contributes to ongoing research on the extremal properties of the Sombor index and its relation to other graph parameters.

This study examines how Generation Z’s digital practices on TikTok and Instagram shape their music festival experiences, focusing on event perception, engagement, and the development of collective identity. The aim is to identify key factors connecting online and offline aspects of festival participation. The research adopts a quantitative approach based on an online survey of 248 respondents born between 1995 and 2010 from various regions of Serbia. Data were analyzed in SPSS 26.0 using Spearman correlation, quantile regression, and the Mann–Whitney test. Given the exploratory nature of the study, the findings should be interpreted accordingly. Findings show that frequent social media use has a positive but limited effect on how important these platforms are perceived for the festival experience. However, user-generated content created by attendees plays a more significant role in shaping engagement and attitudes than influencer content. Influencer credibility also influences how festivals are interpreted digitally. The interplay between online interaction and offline participation motivates content sharing and reinforces a sense of community. Overall, the study concludes that social media and digital narratives are central to Generation Z’s festival experience. Authentic, attendee-created content strongly contributes to collective identity, helping bridge digital and physical dimensions—insights valuable for festival organizers, influencers, and cultural tourism.

Noise in digital images is a common artifact introduced during acquisition, processing, or transmission, often degrading visual quality and analysis accuracy. Traditional noise types, such as Gaussian, salt-and-pepper, and Poisson noise, focus on isolated pixel-level distortions and are mathematically well defined. However, real-world phenomena, like snow or rain, create structured noise that spans multiple pixels and exhibits spatial coherence. This paper introduces a novel hybrid model, S3N-AI (structured snow simulation noise via artificial intelligence), for modeling and simulating snow effects in digital images using AI-inspired parametric control. S3N-AI combines classical image noise modeling with AI-driven probabilistic rules and structural priors that emulate natural snowfall behavior. Snowflakes are generated using adaptive, depth-aware Gaussian functions guided by learned spatial distributions, enabling intensity variation, directional coherence, and perceptual realism. A density parameter enables precise control over coverage, while affine coordinate transformations simulate wind and gravity effects. S3N-AI bridges classical and AI-enhanced noise models, offering a robust simulation framework applicable in virtual environments, vision systems, and deep learning training datasets. The MATLAB-based implementation ensures reproducibility and paves the way for future integration with neural image generation techniques.

Alzheimer’s disease (AD) is a complex neurodegenerative condition marked by a gradual decline in cognitive abilities, a reduction in acetylcholine (ACh) levels, and the accumulation of β-amyloid (Aβ) plaques. In a healthy brain, approximately 80% of ACh is broken down by acetylcholinesterase (AChE). Meanwhile, butyrylcholinesterase (BChE) serves a supportive function, gaining significance as AChE activity diminishes during the progression of Alzheimer’s disease. Modern therapeutic approaches focus on creating dual inhibitors of AChE and BChE that also aim to diminish Aβ-amyloidogenesis through interactions with the peripheral anionic site (PAS). This study combined fragment-based molecular design (CReM), AI-assisted retrosynthetic feasibility assessment, and in silico evaluation (docking, molecular dynamics, and ADMET profiling) to identify novel Donepezil derivatives as potential dual AChE/BChE inhibitors. A series of 10,000 derivatives were developed through computational methods and carefully assessed based on stringent drug-likeness, synthetic accessibility, and medicinal chemistry standards. This was succeeded by comprehensive ADMET profiling. Nine candidates were identified with predicted CNS pharmacokinetics, adequate toxicological profiles and reduced cytochrome P450 liabilities. Molecular docking yielded improved predicted binding affinities relative to Donepezil. Several derivatives, particularly D4 and D5, showed dual-site binding poses spanning both the catalytic gorge and the PAS. MD simulations indicated the stability of these poses over 100 ns. These computational results suggest that the proposed derivatives may preserve or improve upon Donepezil’s pharmacokinetic profile while offering potentially balanced AChE/BChE inhibition and anti-amyloidogenic activity, pending experimental validation.