Lattice and grain-boundary diffusions of boron atoms in BaSi2 epitaxial films on Si(111)

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In-situ heavily p-type doping of over 1020 cm−3 in semiconducting BaSi2 thin films for solar cells applications

B-doped p-BaSi2 layer growth by molecular beam epitaxy and the influence of rapid thermal annealing (RTA) on hole concentrations were presented. The hole concentration was controlled in the range between 1017 and 1020 cm−3 at room temperature by changing the temperature of the B Knudsen cell crucible. The acceptor level of the B atoms was estimated to be approximately 23 meV. High hole concentrations exceeding 1 × 1020 cm−3 were achieved via dopant activation using RTA at 800 °C in Ar. The activation efficiency was increased up to 10%

Impact of Bacille Calmette-Guerin Vaccination on Neuroradiological Manifestations of Pediatric Tuberculous Meningitis

The authors conducted this study to identify whether bacille Calmette-Guerin (BCG) vaccination leads to an altered spectrum of neuroimaging findings outcome in pediatric Patients with tuberculous meningitis. This retrospective study was conducted through chart review and review of computed tomography (CT) scans and magnetic resonance imaging (MRI) of Patients with confirmed central nervous system tuberculosis from the year 1992 to 2005, at a large tertiary care hospital in Karachi, Pakistan. A total of 108 pediatric Patients with tuberculous meningitis were included in the analysis. Of the 108 Patients, 63 (58.3%) were male and 45 (41.7%) had received bacille Calmette-Guerin vaccination. There was no difference in terms of severity of clinical presentation and outcome between vaccinated and unvaccinated group. There were no significant differences in CT or MRI findings between the 2 groups except for tuberculomas on MRI, which were significantly higher in the non-bacille Calmette-Guerin vaccinated group (52.2% vs 22.7%, P = .042). Bacille Calmette-Guerin vaccination appears to translate into less tuberculoma formation on MRI

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Tribological property evaluation, optimization and performance of waste sunflower oil based green cutting fluid with silicon dioxide nanoparticles as additive

Mineral oil-based cutting fluids are hazardous and non-biodegradable, and their widespread usage has had a terrible effect on the environment and living things. The creation of a novel, ecologically sustainable cutting fluid technology is essential to avoid the above crisis. Commercial mineral oil alternatives are considered to possess identical lubricating properties as vegetable oils. Most vegetable oils are edible, so waste-cooking sunflower oil (WSO) is selected from this group to serve as the base stock for the green cutting fluid. The green cutting fluid is created using silicon dioxide nanoparticles as an additive and food-grade emulsifiers like Tween 80 and Span 80. According to the experimental findings, 0.05 weight percent silicon dioxide nanoparticles in the green cutting fluid performed better on a pin-on-disc tribometer

Decentralized document version control using ethereum blockchain and IPFS

In this paper, we propose a blockchain-based solution and framework for document sharing and version control to facilitate multi-user collaboration and track changes in a trusted, secure, and decentralized manner, with no involvement of a centralized trusted entity or third party. This solution is based on utilizing Ethereum smart contracts to govern and regulate the document version control functions among the creators and developers of the document and its validators. Moreover, our solution leverages the benefits of IPFS (InterPlanetary File System) to store documents on a decentralized file system. The proposed solution automates necessary interactions among multiple actors comprising developers and approvers. Smart contracts have been developed using Solidity language, and their functionalities were tested using the Remix IDE (Integrated Development Environment). The paper demonstrates that our smart contract code is free of commonly known security vulnerabilities and attacks. The code has been made publically available at Github

DESIGNING OF COUMARIN DERIVATIVES AS SQUALENE SYNTHASE INHIBITORS

Objective: The importance of this research work is to design a library of novel coumarin derivatives by docking evaluation of the designed coumarin derivatives as squalene synthase inhibitor.Methods: The three-dimensional structure of designed molecules of squalene synthase inhibitors was collected from Protein Data Bank. The designed molecules were docked onto the enzymes that are squalene synthase inhibitor - 3WCM, 3WCJ, and 3Q2Z protein using SYBYL-X 2.1. Using a standard protocol, the protein was subjected to minimization and protomol generation.Results: By this method, we visualized the possible binding and also estimated the protein interactions with our intended coumarin library, using SYBYL-X 2.1 software. Into the active site of the selected enzymes, all the 20 coumarins were docked and then the docking scores revealed that the compounds possess high affinity toward the selected enzymes.Conclusion: With the help of virtual evaluation, we have elaborated a fast synthetically accessible coumarin-based compounds, and it is an advanced and original scaffold in the area of probable human squalene synthase inhibitors. Some of the developed compounds show better binding property than ligand, and in 3q2Z, the compound 5d shows better binding property than the protein. Furthermore, 6g and 6c have good binding property. In 3 WCM, the compound 6f has better property. In 3 WCJ, the compounds 6g and 6f show better binding property than the protein

Active removal of waste dye pollutants using Ta[sub]3N[sub]5/W[sub]18O[sub]49 nanocomposite fibres

A scalable solvothermal technique is reported for the synthesis of a photocatalytic composite material consisting of orthorhombic Ta3N5 nanoparticles and WOx≤3 nanowires. Through X-ray diffraction and X-ray photoelectron spectroscopy, the as-grown tungsten(VI) sub-oxide was identified as monoclinic W18O49. The composite material catalysed the degradation of Rhodamine B at over double the rate of the Ta3N5 nanoparticles alone under illumination by white light, and continued to exhibit superior catalytic properties following recycling of the catalysts. Moreover, strong molecular adsorption of the dye to the W18O49 component of the composite resulted in near-complete decolourisation of the solution prior to light exposure. The radical species involved within the photocatalytic mechanisms were also explored through use of scavenger reagents. Our research demonstrates the exciting potential of this novel photocatalyst for the degradation of organic contaminants, and to the authors’ knowledge the material has not been investigated previously. In addition, the simplicity of the synthesis process indicates that the material is a viable candidate for the scale-up and removal of dye pollutants on a wider scale

Enhanced Deep Learning for Robust Stress Classification in Sows from Facial Images

Stress in pigs poses significant challenges to animal welfare and productivity in modern pig farming, contributing to increased antimicrobial use and the rise of antimicrobial resistance (AMR). This study involves stress classification in pregnant sows by exploring five deep learning models: ConvNeXt, EfficientNet_V2, MobileNet_V3, RegNet, and Vision Transformer (ViT). These models are used for stress detection from facial images, leveraging an expanded dataset. A facial image dataset of sows was collected at Scotland’s Rural College (SRUC) and the images were categorized into primiparous Low-Stressed (LS) and High-Stress (HS) groups based on expert behavioural assessments and cortisol level analysis. The selected deep learning models were then trained on this enriched dataset and their performance was evaluated using cross-validation on unseen data. The Vision Transformer (ViT) model outperformed the others across the dataset of annotated facial images, achieving an average accuracy of 0.75, an F1 score of 0.78 for high-stress detection, and consistent batch-level performance (up to 0.88 F1 score). These findings highlight the efficacy of transformer-based models for automated stress detection in sows, supporting early intervention strategies to enhance welfare, optimize productivity, and mitigate AMR risks in livestock production.</p

A Deep Learning Framework for Detecting Cross-Generational Facial Markers Associated with Stress in Pigs

Maternal stress during gestation can alter offspring physiology, behaviour, and immune function. In pigs, such ‘prenatal stress’ is known to increase stress sensitivity, but the potential to automatically detect such sensitivity has remained unexplored. Automatic detection of facial expression has successfully identified differences in pigs dependent on their stress status. This study progresses this work by demonstrating that, for the first time, using a deep learning framework applied to facial analysis, stress-linked phenotypes can be learned from one generation and detected in the next. Using a dataset of over 7000 facial images from 18 gestating sows and 53 of their daughters, we trained and evaluated five state-of-the-art deep learning architectures across six independent daughter cohorts. Attention-based models significantly outperformed CNN-based models, with the Vision Transformer (ViT) model achieving a mean accuracy of 0.78 and an average F1-score of 0.76. Grad-CAM visualisations showed that the ViT consistently attended to biologically relevant facial regions, such as the eyes and snout, whereas CNNs often focused on diffuse or non-informative areas, resulting in reduced low-stress recall and greater batch sensitivity. Models trained on maternal facial images successfully predicted stress responsiveness in daughters from unrelated lineages, indicating that the model captured generalisable facial cues of stress rather than familial resemblance. This approach supports previous work showing that machine vision can detect putatively stress-related alterations to facial expression in pigs. Future application of this approach could offer a scalable, non-invasive tool for early detection of stress in livestock production systems, opening new avenues for welfare-oriented precision livestock management and informed breeding strategies aimed at improving stress resilience