Innopolis University

Brain delivery of lipophilic antioxidants is limited by poor solubility and restricted transport across the blood-brain barrier (BBB). To address this challenge, we developed a plant-derived glucosylceramide (GlcCer)-based w/o/w nanoemulsion designed to exploit glucose transporter (GLUT)-mediated uptake for brain targeting. Unlike synthetic glucose conjugates, GlcCer provides intrinsic amphiphilicity and biocompatibility, enabling self-assembly into stable, surfactant-free nanoemulsions incorporating astaxanthin, curcumin, and sesame oil through a cost-effective and scalable process. The resulting GlcCer-functionalized nanoemulsions demonstrated enhanced physicochemical stability and low cytotoxicity in SH-SY5Y and hCMEC/D3 cells. Quantitatively, the sesame oil-based targeted formulation achieved a 1.6-fold increase in neuronal uptake, stronger ROS suppression, and a 1.4-fold improvement in transendothelial transport compared with nontargeted controls. These findings identify GlcCer-based w/o/w nanoemulsions as a promising and scalable platform for brain-targeted antioxidant delivery.

A technology for operational complex forecasting (postprocessing)1 of squall wind gusts at the points of weather station location is described. The technology was implemented at the Hydrometeorological Research Center of Russia. It uses the results of forecasts of several forecast models and the complex forecast of average wind. The tests have shown an increase in forecast accuracy as compared to original models.

The article presents a comparative analysis of English and German phraseological units representing negative communicative personality traits: talkativeness, deceitfulness, and malice. The study reveals universal and culture-specific features in the linguistic expression of these concepts. It is established that both languages actively use somatisms, animalistic metaphors, and images of filth to convey negative assessment.

Understanding source code is a cognitively demanding task, yet existing complexity metrics primarily rely on structural features and overlook actual human effort. This paper introduces a time-based approach to measuring code comprehension using large language models (LLMs). We investigate whether LLMs can estimate the number of seconds a junior developer would need to understand Java source files. Using a dataset of 371 files from the Apache Hive project, we evaluated five LLMs under a uniform prompt design. Human comprehension times, collected through a controlled study involving junior developers, were used as ground truth. Among the tested models, Gemini 2.0 Flash most closely approximated human estimates, while others exhibited systematic bias or output instability. Our findings show that while LLMs can produce interpretable, numeric predictions of comprehension time, achieving consistent accuracy requires prompt refinement and potential model fine-tuning. This work contributes a behavioral perspective to software complexity analysis, offering time-based estimations as an alternative to static metrics.

Large language models for source code (Code LLMs) demonstrate great performance on high-resource programming languages (HRPLs) but struggle with low-resource ones (LRPLs). Previous studies have improved LLM performance on LRPLs by continued training or tokenizer adaptation. However, they require costly data and can cause catastrophic forgetting. This paper proposes to address the poor performance of LLMs on LRPLs using uncertainty estimation (UE). UE methods have advanced LLM performance on natural language tasks, but are underexplored in source code settings. The research may provide three contributions: (1) a new code generation benchmark evaluating not only functional correctness but also readability, efficiency, and idiomatic style across Python, Java, Racket, and Elixir; (2) a new benchmark for evaluating uncertainty estimation when generating code; and (3) methods to improve LRPL code generation by leveraging UE. The methods utilizing UE include filtering synthetic training data by low uncertainty, an UE-driven curriculum learning strategy, uncertainty-aware decoding, and using uncertainty as an RL reward in alignment. The research may provide a comprehensive evaluation of uncertainty in code models, demonstrate that UE can improve LRPL generation, and open-source release of benchmarks and models as outcomes.

We consider the situation where a transmitter communicates with a legitimate receiver and at the same time aims to send a covert message to a covert receiver while ensuring that the covert message is non-detectable at a third receiver, referred to as the warden. The considered system belongs to the class of non-orthogonal multiple access (NOMA) systems. For the considered NOMA system, we show that it is possible to achieve complete covertness, i.e., the error detection probability at the warden to be one, using artificial noise generated at the transmitter. Depending on the power of the artificial noise, there is a trade-off between the data rate achieved at the legitimate receiver and the covert data rate achieved at the covert receiver, while ensuring complete covertness.

Reliable, real-time detection of sperm-whale clicks is essential yet difficult in noisy ocean audio streams. We present the first browser-based pipeline that combines self-supervised embeddings with a BiLSTM to label clicks at millisecond resolution. The system attains 99% F1 on the Watkins benchmark, reduces false alarms by 60% against the best published baseline, and analyses one-second segments in ~40 ms on a consumer GPU. An interactive UI overlays multi-algorithm detections on waveform and spectrogram views with drag-zoom and live streaming. Code, pretrained weights and the public demo are released to advance bioacoustic event detection.