The nucleus, as the control center of the eukaryotic cell, plays a pivotal role in orchestrating cellular responses to environmental stresses, including salinity. Salt-tolerant plants, such as Artemisia marschalliana, possess unique adaptive mechanisms to thrive in high-salinity environments. This study investigates the distinct structural features of nuclei within the leaf mesophyll cells of Artemisia marschalliana, aiming to elucidate potential ultrastructural adaptations associated with its salt tolerance. Using transmission electron microscopy, we analyzed the chromatin organization, nucleolar morphology, and nuclear envelope integrity. Our findings reveal specific nuclear characteristics, including a well-defined nucleolus with distinct fibrillar and granular components and a relatively dispersed chromatin pattern, suggesting active transcriptional and metabolic processes. These ultrastructural observations provide insights into the cellular strategies employed by Artemisia marschalliana to maintain nuclear homeostasis and cellular function under saline conditions, contributing to a deeper understanding of plant salt tolerance mechanisms.

Therapeutic fasting attracts growing interest within neuroscience because metabolic stress modulates neurotrophic signalling. The present study evaluates intermittent fasting combined with vegan nutrient patterns for their cumulative influence on memory, attention and synaptic plasticity. A systematic examination of experimental rodents, controlled human trials and molecular investigations conducted. Brain-derived neurotrophic factor (BDNF), AMP-activated kinase and sirtuin pathways were selected as mechanistic markers. Evidence synthesis indicates that alternate-day energy restriction up-regulates hippocampal BDNF, while polyphenols and omega-3 precursors supplied by plant diets reinforce this response. Clearance of misfolded proteins, attenuation of neuro-inflammation and stabilisation of neuronal membranes surfaced as convergent mechanisms. Comparative evaluation suggests superior cognitive performance when fasting protocols coincide with plant-rich menus compared with either intervention alone. The analysis highlights translational gaps, particularly long-term human data, and formulates directions for personalised nutritional neuromodulation. Potential safety considerations related to hypoglycaemia and micronutrient sufficiency are critically appraised within cohorts. The article will be useful for clinical nutritionists, neuroscientists, geriatric practitioners and policymakers.

Air pollution is one of the most serious environmental threats to human health. In this article, using the statistical analysis method of time series, the statistical regularity of the dynamic series ytˉ\bar{y_t}ytˉ — the average amount of pollutants emitted into the atmosphere in the Republic of Uzbekistan — is studied (based on data from the State Statistics Committee of the Republic of Uzbekistan for the period 2011–2022). With a 95% confidence level, point and interval estimates of the average amount of atmospheric pollutant emissions in Uzbekistan are constructed, obvious trends are identified, and forecasts are made for the following years. Using the Durbin-Watson statistical criterion, it is established that the average amount of pollutants emitted into the atmosphere has autocorrelation dependence.

The applied methods of processing and analyzing dynamic series, after testing, can be used in the research of graduate students and scientific researchers.

The exponential growth of digital content has driven the need for more intelligent, context-aware information retrieval systems. While traditional keyword-based search engines remain foundational, they often fall short of capturing deeper semantic meaning. This article explores the evolution, methodologies, and recent developments in intelligent information retrieval systems powered by artificial intelligence. Special attention is given to the use of machine learning, natural language processing (NLP), and neural networks to improve relevance, personalization, and contextual understanding, including the application of learning-to-rank techniques. The paper contrasts the strengths and limitations of conventional search technologies with those of AI-driven models. A critical part of the study focuses on potential risks associated with AI-based search engines, including environmental concerns linked to the heavy water consumption of data centers relying on water-based cooling systems. The research concludes that a holistic approach is needed in the design and implementation of AI-powered search systems—one that integrates ethical, cognitive, and environmental considerations. This article will be of interest to professionals in media and information technology, researchers, and developers engaged in building intelligent search infrastructures.

Modern supply chain systems are experiencing unprecedented transitions driven by agility requirements, lean management principles, and the rise of intelligent, cloud-enabled data warehousing technologies. Classical operations management frameworks—such as rapid-fire fulfillment, factory physics, and Toyota Production System principles—continue to influence operational design, yet these models must now integrate with digital capabilities including IoT-enabled warehouse visibility, multi-cloud strategies, and AI-enabled analytics. The convergence of these domains creates both opportunities and theoretical tensions, especially regarding responsiveness, stability, data latency, and cross-system coordination. This article provides an exhaustive synthesis grounded exclusively in seminal and contemporary literature, establishing a unifying conceptual foundation that combines agile supply chains, lean manufacturing, performance metrics, information flow theory, and next-generation data warehousing. Using deep theoretical elaboration, this work examines the operational logic of rapid fulfillment systems, the physics of manufacturing variability, and agile responsiveness (Ferdows et al., 2004; Hopp & Spearman, 2008; Schwaber & Beedle, 2002). The discussion extends into the modern digital landscape, addressing IoT-driven inventory visibility (Chowdhury, 2025), multi-cloud challenges (Shekhar, 2021), spatio-temporal data warehousing (Gómez et al., 2009), machine-learning optimization (Ahmadi, 2023), MapReduce for big data warehousing (2018), real-time hybrid joins (Naeem et al., 2011), and advanced materialized query approaches (Chakraborty, 2021). Through this synthesis, the article develops a comprehensive framework explaining how agility, lean principles, and intelligent data architectures co-evolve to create competitive advantage (Kahn & Mentzer, 2001). The result is an integrated theoretical architecture that fills a critical gap between operations management and digital warehousing research, offering new pathways for scholars and practitioners seeking to design resilient, responsive, and data-intelligent supply chain ecosystems.

In this work, a mathematical model of the pulsating forces acting on the working chamber of a cotton‐cleaning machine was developed and analyzed. It was demonstrated that maximum productivity is achieved by installing triangular accelerators at a 60° angle, which eliminates sequential impulses and prevents unnecessary collisions.

KRAS mutations play a key role in the pathogenesis of acute myeloid leukemia (AML), occurring in 10–15% of cases and being associated with aggressive disease progression and therapeutic resistance. Despite significant advances in the treatment of KRAS-mutant solid tumors, including the approval of allele-specific G12C inhibitors, the potential of pan-KRAS inhibitors in hematologic malignancies remains insufficiently explored. This study evaluates a pan-KRAS inhibitor structurally analogous to BI-2493 in the SKM-1 cell line model (KRAS G12D+). In vitro results demonstrate reduced cell viability, induction of apoptosis (Annexin V+), and suppression of the KRAS–MEK–ERK signaling cascade. The findings are contextualized with data from Popow et al. and Revvity/Boehringer Ingelheim, enabling a comparative analysis of G12D-mutant model sensitivity across tumor types. The discussion addresses the potential for in vivo xenograft testing, combination strategies with SHP2 and BCL2 inhibitors, and the application of PROTAC degraders as alternative approaches in resistant settings. These results provide the first evidence of pan-KRAS inhibitor efficacy in an AML model, highlighting its relevance for targeted therapy in hematologic malignancies and supporting further preclinical investigation aimed at integration into personalized oncology protocols.