Antibiotics have been widely used in animal husbandry to promote growth and treat infectious diseases. However, their extensive use can lead to significant consequences, including disruption of the gut microbiota in treated animals and the emergence of antibiotic-resistant bacteria. Additionally, antibiotics and their metabolites may enter the environment through animal manure, contributing to the spread of antibiotic resistance genes among bacterial populations.

This study aimed to assess the prevalence of colistin-resistant Escherichia coli in chicken meat, chicken manure, and surface samples (i.e., poultry drinkers). Samples were collected over a one-month period. Colistin-resistant bacteria were isolated using a growth medium supplemented with colistin sulfate. A total of 81 samples were analyzed, of which 90% were positive for the presence of colistin-resistant strains, including coliforms and Pseudomonas spp.

Based on morphological characteristics, 31 Escherichia coli isolates were selected for confirmation of the mcr-1 gene using qPCR technique. Subsequently, antimicrobial susceptibility of the selected bacteria was determined by the Kirby-Bauer disk diffusion method with the following antibiotics: colistin, imipenem, ertapenem, meropenem, ceftazidime, cefotaxime, cefepime, and ciprofloxacin. The minimum inhibitory concentration (MIC) for colistin was evaluated using both ETEST and broth microdilution methods.

Overall, one Escherichia coli strain was positive for the presence of of mcr-1 gene. Nonetheless, several isolates showed intermediate susceptibility or resistance to multiple antibiotics including cephalosporins and fluoroquinolones.

Lightweight classification models such as the LDDm-CNN have shown strong potential for distinguishing drones from other aerial objects such as airplanes. However, classification alone is insufficient for real-time surveillance, since it requires prior cropping or manual region-of-interest extraction and cannot localize drones directly within a scene. Although YOLO-based detectors achieve remarkable mAP, their large model sizes and high computational demands hinder deployment on edge devices. Therefore, this renders the architecture unsuitable for real-time drone surveillance in resource-constrained environments. This limitation reduces applicability in dynamic security environments where both recognition and spatial localization are critical. To overcome this gap, we extend the LDDm-CNN into a full detection framework optimized for edge devices. The proposed LDDm-YOLO uses the LDDm backbone as a compact feature extractor and integrates a lightweight, anchor-free detection head with a shallow feature pyramid for multi-scale object localization. Knowledge distillation transfers rich spatial and semantic features from a larger teacher detector, while Bayesian optimization tunes key hyperparameters such as distillation temperature, loss weighting, and feature fusion depth. Experiments on public drone detection datasets show that LDDm-Det achieves competitive mean Average Precision (mAP =0.95) while retaining a smaller model size of only a few megabytes, enabling real-time detection on edge devices and localization on resource-constrained devices.

One of the key challenges in developing fat-containing foods aligned with healthy dietary trends is optimizing the fatty acid composition. Blending traditional vegetable oils is a cost-effective and practical approach for designing products with targeted levels and ratios of polyunsaturated fatty acids (PUFAs). Our previous research identified that a blend of 52% sunflower oil and 48% rapeseed oil provides a balanced ω-6:ω-3 ratio of 9.8:1.0.

This study aimed to evaluate the physicochemical and sensory properties of this blend. Fatty acid composition was analyzed using gas–liquid chromatography, while total tocopherol and α-tocopherol contents were determined via high-performance liquid chromatography. Standard analytical methods were used to assess other physicochemical parameters. Sensory properties were evaluated using quantitative descriptive analysis based on transparency, taste, flavour, and colour. Statistical analysis was performed using parametric tests, with significance accepted at p<0.05.

The blend exhibited a favorable fatty acid profile for balanced nutrition, with high monounsaturated oleic acid (42.6±0.3%) and sufficient ω-3 linolenic acid (4.3±0.2%). It demonstrated improved hydrolytic and oxidative stability, which was confirmed by the significantly lower acid and peroxide values after 30 days of storage at (20±1)°C compared to pure sunflower oil by 5.3% and 19.7%, respectively. The accumulation rate of primary oxidation products was 1.5 times lower in the blend (p<0.05). These findings may be attributed to a higher content of antioxidant-active tocopherol isomers (β-, γ-, and δ-tocopherols) derived from rapeseed oil.

Sensory evaluation confirmed the blend's compliance with regulatory quality standards. The developed sunflower–rapeseed oil blend is a promising option for functional fat-containing products aimed at dietary improvement and disease prevention.

Inclusivity has become a central theme in the development of digital technologies. Designing with inclusivity in mind requires that systems remain accessible and valuable to users irrespective of their age, gender, or socio-economic background. However, most applications remain restricted to a single language—primarily English—thereby marginalizing large groups of non-English-speaking individuals. This work explores the integration of Computer Vision (CV) and Natural Language Processing (NLP) to enhance accessibility for visually impaired users, with a particular emphasis on multilingual support in assistive technologies. Through a review of the existing literature and user experiences, this study identifies language barriers as a major obstacle in accessing essential services. To address this, we employ a multi-stage methodology for multilingual image captioning. Image–caption pairs were extracted from the MS COCO dataset, reformatted into JSON, and translated from English to the local language (currently Hindi) to generate a bilingual corpus. The model combines Recurrent Neural Networks (RNNs) for image feature extraction with Long Short-Term Memory (LSTM) units for sequence generation, enabling the system to capture temporal dependencies inherent to natural language. Experimental outcomes indicate that the model can generate Hindi captions with about 80% accuracy, effectively describing visual scenes despite some grammatical limitations. Real-time camera integration and a text-to-voice module further enhance usability by delivering immediate audio captions to visually impaired users. Future work will focus on transformer-based multilingual architectures and larger datasets to improve accuracy, contextual richness, and language coverage, moving towards a robust, speech-enabled assistive platform.

Electron Paramagnetic Resonance (EPR) or electron spin resonance (ESR) spectroscopy is a non-destructive technique which used usually for the identification and investigation of compounds containing unpaired electrons and hence it is considered as an effective tool for the identification and quantification of radiation doses delivered to irradiated materials. Here we investigate the EPR spectroscopic differences among poly, di- and monosaccharides irradiated to gamma radiation. Several parameters were studied: spectral features of monomers, dimers, and polymer molecules, microwave saturation behaviour, and the modulation amplitude impact on both of the signal height and the width. Also response to different radiation doses were investigated for each molecule and the time-dependence of the radiation-induced radicals were plotted and analysed. the study included glucose and fructose as monosaccharides, sucrose as a disaccharide, and starch as a polysaccharide. The study revealed thae resembelence of EPR spectra of sucrose and its constituents clucose and fructose with minor differences while the EPR spectra of starch showed more differences either in terms of peaks positions or peaks shapes. Response to ionizing radiation doses for all of them was found to be linear over the dose range (0.5 – 10) kGy. The stabilities of the radiation induced radicals were traced along 50 days after irradiation.

Photodynamic therapy (PDT) is a modern minimally invasive method of treating oncological diseases. PDT is based on the use of a photosensitizer (PS), a light-sensitive drug that triggers a chain of photochemical reactions leading to the formation of cytotoxic singlet oxygen and/or reactive oxygen species that destroy tumor cells. An important challenge for further progress in PDT is to overcome the limitations associated with PS delivery and oxygen availability in tumors. Therefore, the development of drug delivery systems based on hemoglobin (Hb) has attracted increasing attention. Hb serves as a delivery system for both PS and molecular oxygen in hypoxic tumor cells.

Here, we synthesized Zn-substituted hemoglobin (ZnHb), in which heme was replaced by Zn-protoporphyrin IX (ZnPP), an effective PS. The photophysical properties and singlet oxygen generation by ZnPP in a complex with Hb were studied. It was shown that interaction of ZnPP with Hb leads to the increase in the PS's triplet state lifetime by more than 10 times, which is associated with a significant decrease in the access of molecular oxygen to ZnPP embedded in the heme pocket. ZnPP in the complex with Hb does not lose the ability to generate singlet oxygen. It was found that laser irradiation causes photodestruction of ZnHb, with ZnPP not leaving the heme pocket at a pH of 7.2. The release of ZnPP from the protein occurs with an increase in the acidity of the medium, which leads to the aggregation of ZnPP and a significant decrease in singlet oxygen generation.

Understanding real-world documents is no longer just about reading text. Modern documents combine written content with layouts, tables, forms, and even images, creating a level of complexity that traditional natural language processing tools struggle to handle. Many existing approaches treat each part of the documents as text, structure, or visuals separately or fuse them too late, which means they often miss the context that comes from how these elements interact. This research presents a unified framework that integrates text, layout, and visuals into a single, explainable system. Built on a transformer backbone, the framework uses cross-modal attention and graph-based reasoning to capture relationships across different modalities more naturally. The framework will be tested on benchmark datasets, including FUNSD, DocVQA, SROIE, RVL-CDIP, and PubLayNet, which cover tasks such as key-value extraction, document classification, and visual question answering. We will evaluate results using standard metrics (F1, exact match, IoU) and add interpretability checks to ensure transparency. We expect the system to deliver stronger accuracy, adaptability, and interpretability than current methods. Beyond technical gains, this research aims to support practical automation in many precision domains like healthcare, legal services, and government, while advancing the foundations of multimodal reasoning in the field of Artificial Intelligence

Context: Soil fertility plays a vital role in the agricultural field, as it provides a descriptive analysis of the nutrient content present in the soil and helps crops grow well in that soil. According to various sources, the majority of farmers are unaware of their soil fertility type. As a result, they face losses when harvesting crops because the type of crop may not be suitable for their preferred soil. Objective: The primary objective of this paper is to predict different levels of soil fertility by analyzing the nutrient content present in the soil using machine learning. Materials and methods: In this study, several machine learning classification algorithms—namely, Gradient Boosting, Extra Trees, Bagging Classifier, Random Forest, Decision Tree, AdaBoost, and SVM—were individually trained and evaluated on the same soil dataset to compare their predictive performance. Each classifier was employed to learn patterns from the selected soil features and predict the fertility class of each soil sample. The comparative analysis revealed that the Gradient Boosting classifier produced the most accurate predictions (Accuracy – 0.9806). Apart from this, other algorithms, such as Random Forest, Decision Tree, and Extra Trees, provided nearly equal results. However, even after hyperparameter tuning, the Gradient Boosting algorithm still provides the best result among all the algorithms.

Traditional pasteurization (PT), using high temperatures and long process times, can determine significant degradation of bioactive compounds, so research into other methods of preserving fruit products is needed. Strawberry-chokeberry (SC) and strawberry-blackcurrant (SB) nectars were preserved by PT (90°C/10 min), HTST (90°C/15 s) and UHT (130°C/5 s) methods and stored for 6 months under refrigeration. Before this study was performed, the used parameters of HTST and UHT had not been identified in the literature. Qualitative and quantitative analyses of phenolic compounds by the UPLC-PDA-QTOF/MS method were carried out. In total, 23 phenolic compounds in SC and 21 in SB were identified (anthocyanins, flavan-3-ols, phenolic acids and their hexosides, flavonol glycosides). Considering the sum of all identified compounds, there was a decrease of 12%, 1% and 6% in SC after PT, HTST and UHT, respectively, while in SB, there was a decrease of 14% and 10% after PT and UHT, and no significant change after HTST. Taking into account storage, most of the compounds were partially degraded, while in the case of flavan-3-ols, an increase of 26-128% (depending on the nectar and method) was observed after 6 months of storage. At the end of the storage period following PT, HTST and UHT, the sum of identified compounds in SC decreased by 13%, 21% and 20%, respectively, while in SB, decreases of31%, 40% and 41% were reported. HTST and UHT-preserved nectars had more bioactive compounds than PT-preserved samples after 6 months of storage. HTST was the best preservation method as it allowed the highest retention of bioactive components.

Benzothiazolines are heterocyclic compounds that have been developed over time because they have important biological properties, according to the literature [1], with the goal of extending these properties and creating derivatives with multiple biological properties and functions [2,3].
Schiff bases are also physiologically active molecules with a range of characteristics, and researchers have been interested in discovering methods to synthesis Schiff bases.
In this study, we synthesized a Schiff base derivative benzothiazolone (E)-6-(((4-hydroxyphenyl)imino)methyl)-3-methylbenzo[d]thiazol-2(3H)-one , by the condensation reaction between 3-methyl-2-oxo-2,3-dihydrobenzo[d]thiazole-6-carbaldehyde (3) and primary aromatic amine, under ultrasound-assisted green conditions in ethanol, affording a 65% yield. Furthermore, an in-silico ADME analysis was performed using SwissADME online tools to evaluate the drug-likeness and pharmacokinetic behavior of the synthesized compound. The prediction revealed favorable pharmacokinetic properties,nd compliance with Lipinski’s rule of five, suggesting good oral bioavailability potential. These findings highlight the potential application of this compound in pharmaceutical development and support the green methodologies for obtaining bioactive heterocycles.

[1] M. Erdogan et al., “Design, synthesis and biological evaluation of new benzoxazolone/benzothiazolone derivatives as multi-target agents against Alzheimer’s disease,” European Journal of Medicinal Chemistry, vol. 212, p. 113124, Feb. 2021, doi: 10.1016/j.ejmech.2020.113124.

[2] S. H. Ferreira, B. B. Lorenzetti, M. Devissaguet, D. Lesieur, and Y. Tsouderos, “S14080, a peripheral analgesic acting by release of an endogenous circulating opioid‐like substance,” British J Pharmacology, vol. 114, no. 2, pp. 303–308, Jan. 1995, doi: 10.1111/j.1476-5381.1995.tb13227.x.

[3] N. Dharmaraj, P. Viswanathamurthi, and K. Natarajan, “Ruthenium(II) complexes containing bidentate Schi€ bases and their antifungal activity”.