Background:Accurate and interpretable brain tumor classification from MRI images remains a key challenge in medical image analysis, particularly when using publicly available datasets of moderate size. Objectives:This study investigates the performance of a ConvNeXt-Tiny based framework for four-class brain tumor classification glioma, meningioma, pituitary tumor, and no tumor and compares it with established convolutional architectures. Materials and methods:Using transfer learning and identical experimental settings, ConvNeXt-Tiny was evaluated against DenseNet169, Xception, MobileNetV3-Large, CNN+DenseNet169, and ResNet50. Standard evaluation metrics (accuracy, precision, recall, and F1-score) were used, and Grad-CAM was applied to visualize model attention for interpretability. Generalization was further assessed using an independent dataset. Results:ConvNeXt-Tiny achieved high overall performance (accuracy = 0.9924, F1-score = 0.9918), comparable to DenseNet169 and Xception but with lower computational cost. The model maintained stable learning behavior, minimal overfitting, and consistent accuracy on unseen data. Grad-CAM visualizations confirmed that the network focused on clinically relevant tumor regions, improving transparency and reliability of predictions. Conclusion:ConvNeXt-Tiny provides a strong and efficient baseline for interpretable brain tumor classification, balancing accuracy and computational efficiency. While the results are promising, differences among recent architectures were modest, and clinical validation using multi-center MRI datasets is necessary to confirm broader applicability.

Beneath-the-surface nitrogen placement sustains maize productivity and soil health under long-term conservation agriculture in the NW Indo-Gangetic Plains

Cadmium and arsenic accumulation potential of ratoon rice under different water managements

Waste-to-energy storage: Bio-Waste derived activated carbon from wood apple shell for symmetric electrochemical supercapacitors

Supercritical CO₂ Brayton cycles coupled with advanced Molten Salt Reactors offer a promising zero-GHG propulsion alternative for large ocean-going vessels. Their high power density, favourable part-load performance, and compatibility with compact heat-exchanger technologies make them strong candidates for deep-sea decarbonisation. The main objective of this study is to identify optimal sCO₂ cycle and powertrain configurations for marine nuclear propulsion through integrated thermoeconomic optimisation, using a very large ore carrier as a representative application case whose scale and operational regularity render it particularly suitable for nuclear propulsion. A comprehensive thermoeconomic modelling framework is formulated, encompassing multiple sCO₂ cycle variants and three shafting configurations (electric, mechanical, hybrid). Detailed design and off-design component models are combined with energy, exergy, and cost correlations within a generic super-configuration, enabling unified synthesis, design, and operational thermoeconomic optimisation. The optimal solution achieves a design-point efficiency of approximately 45%, comparable to modern large two-stroke diesel propulsion systems, while eliminating direct GHG emissions. Optimisation results reveal two dominant configurations depending on the objective: a fully electric recompression cycle for maximum efficiency, and a hybrid mechanical–electric arrangement for minimum annualised cost. Reactor-cost sensitivity shows that recompression cycles remain optimal across a wide cost range, confirming their structural robustness. Part-load optimisation demonstrates high efficiencies down to 50% load and yields optimal operating set-points for future control development. A preliminary operational lifetime comparison with a conventional diesel-based ship indicates that, despite much higher capital expenditure, the nuclear sCO₂ system achieves a lower annualised cost and becomes economically favourable after approximately ten years of operation. Overall, the results highlight the technical and economic viability of nuclear sCO₂ propulsion for large commercial vessels and provide a rigorous framework for future component design, integration, and assessment. • A thermoeconomic optimisation framework is developed for MSR-driven sCO₂ cycle applied to a marine propulsion system. • A generic super-configuration enables unified optimisation of cycle synthesis, component design, and shafting arrangements. • Optimal solutions achieve about 45% efficiency and identify electric and hybrid powertrains as the best-performing configurations. • Sensitivity and lifecycle analyses show the recompression cycle's robustness and the economic competitiveness of nuclear sCO₂ propulsion. • The nuclear sCO₂ system achieves a lower annualised cost and becomes economically favourable after approximately ten years of operation.

Ethanol-mediated electrochemical reconstruction for efficient catalysts for oxygen evolution reaction.

Process and Cost Evaluation of a Successful Palliative Telecare Team Intervention in Heart and Lung Disease.

• Propose a novel generative framework FAFGAN for fuel consumption estimation. • FAFGAN estimates road-level fuel consumption using only macroscopic traffic observations. • A differentiable fuzzy logic module is developed to infer driving style for fuel estimation. • FAFGAN outperforms classic methods with more than 70% reduction in training time. For estimating the road-level fuel consumption, the existing models rely on data collected from high-precision detectors and rarely incorporate behavioral factors such as the impact of driving styles, which limits the accuracy and the scalability of the estimation models for real-world applications. To this end, this paper proposes a novel generative framework that estimates fuel consumption at the road level with low cost and high behavioral consistency, using only macroscopic traffic observations. The model consists of three key modules: a speed generation module conditioned on macroscopic inputs to reconstruct vehicle trajectory data, a differentiable fuzzy logic module to capture the uncertainty and heterogeneity of driving styles, and a style-aware fusion module that embeds behavioral semantics into the fuel estimation process. We tested the proposed model against the baseline models on the NGSIM and HighD datasets. The results show that FAFGAN achieves the best road-level estimation accuracy with RMS E avg , outperforming advanced generative methods including LCM, HRF, and SiT by 3% to 14%, while achieving comparable accuracy to GaGAN with 72% less training time.

Electrochemical discrimination of phenylalanine enantiomers in blood using an ATO-γ-CD nanocomposite-modified SWCNT platform.

Ultrasensitive fluorescence determination of tetracycline in honey using a Cu-MOF-based sensor

A novel ROS switcher potentiates the type-I photodynamic effect of sodium zinc chlorophyllin against Pseudomonas aeruginosa in diabetic wounds.

To investigate whether significant differences existed in the influence among factors considered during the selection process when ranking orthodontic programs and to understand how an individual's race or ethnic background might affect what factors were more important than others. A survey was distributed to orthodontic residents to assess overall preferences and differences in desirability of various factors between historically underrepresented racial and ethnic (HURE) and non-HURE applicants. A total of 115 individuals initiated the survey, and 98 were included for analysis. Sixteen respondents were considered HURE applicants. "Clear aligner therapy training," "good quality of clinical faculty," and "high number of cases started" had the highest mean desirability overall. The most important factors influencing program rank order were "satisfied current residents," "low cost," and "having multiple techniques and treatment philosophies taught." HURE respondents rated "diversity of training of faculty" (P = .0154), "racial and ethnic diversity of current and former residents" (P = .0007), "racial and ethnic diversity of faculty" (P = .0002), "racial and ethnic diversity of patient population" (P = .0131), "male : female ratio of residents" (P = .0225), "participation in care of Medicaid patients" (P = .0251), and "dental school-based program" (P = .0493) higher than their non-HURE counterparts. Clinical factors are the most important to program applicants. However, HURE applicants place greater importance than non-HURE applicants on characteristics that promote inclusivity and representation of individuals of similar racial and ethnic backgrounds (peers, mentors, and patients).

Hard carbon with tailored microstructure via thermal regulation for high-efficiency sodium-ion batteries.

Tailoring cellulose hydrogel electrolyte using deep eutectic solvent for stable zinc-ion hybrid supercapacitors with enhanced ionic transport.

Sodium carboxymethyl cellulose/sericin/calcium bentonite rapid hemostatic sponge fabricated by ambient pressure drying with excellent hemostatic performance and biocompatibility.

• Citizens meets analytical chemistry: a bridge for growing. • Low cost, eco-friendly, and wearable devices. • Health and environmental monitoring. • 3D printing and smartphone as tools for inclusivity in science. Citizen science is rapidly emerging as a transformative force in analytical chemistry by opening research participation to non-specialists and expanding where and how data can be collected. This report explores how recent technological advances have made analytical tools simpler, smaller, and more affordable. These developments have enabled citizens and students to be engaged directly in environmental monitoring, food-quality assessment, and educational activities, often producing data comparable to those generated in laboratories. In addition, several emerging prototypes have been purposefully designed with citizen use in mind, anticipating future applications in participatory science. The discussion also addresses areas where citizen science could play a growing role, including biomedical and forensic analysis, while recognizing challenges related to data reliability, ethics, and validation. Overall, this report highlights how accessible technologies, supported by artificial intelligence and digital communication, are transforming analytical chemistry into a more inclusive and collaborative discipline, connecting scientific research with everyday life.

Microcrystalline cellulose modified with ethylenediamine disuccinate via ring-opening grafting reaction: A novel and efficient biosorbent for heavy metal ions.

Zinc oxide (ZnO) thin films are widely explored for gas sensing due to their low cost, stability, and intrinsic sensitivity, yet their room-temperature performance is often limited by slow sensing response, poor selectivity, and insufficient active sites. This study reports a scalable strategy to enhance ammonia sensing via dielectric barrier discharge (DBD) plasma treatment of spin-coated ZnO films. Plasma exposure for 6 min (ZnO@P6) significantly tailored structural, morphological, optical, and surface chemical properties compared to pristine ZnO. X-ray diffraction and scanning electron microscopy analyses revealed reduced crystallite size and enhanced lattice strain, while Brunauer-Emmett-Teller and Barrett-Joyner-Halenda measurements showed increased surface area (90 m2 g−1) and enlarged mesopores (~5.94 nm). X-ray photoelectron spectroscopy confirmed oxygen-vacancy formation, nitrogen incorporation, while electron paramagnetic resonance spectra demonstrated a strong signal at g = 1.965, indicating abundant paramagnetic oxygen vacancies that act as shallow donors to enhance charge transport and surface reactivity. Optical studies revealed bandgaps narrowing to 3.206 eV, and water contact angle measurements confirmed improved hydrophilicity. ZnO@P6 exhibited ultralow detection limits (1 ppm), rapid response/recovery times (6 s/53 s), and a high sensing response of 2925 at 800 ppm NH3, markedly outperforming pristine ZnO (sensing response 609 at 800 ppm). Density functional theory simulations corroborated strong NH3 chemisorption (−0.94 eV) with significant charge redistribution. These results demonstrate that DBD plasma engineering produces defect-rich, high-surface-area ZnO films, providing a robust platform for high-performance, selective, and stable room-temperature ammonia sensing for environmental monitoring.

Even though the ocean covers the majority of the planet’s surface, it remains the least explored ecosystem. As light and radio waves do not propagate through water, underwater acoustics is the main choice for various ocean applications ranging from marine biology to pollution monitoring. Increasing levels of anthropogenic noise from ships contribute significantly to underwater sound pollution, posing risks to marine ecosystems. This makes monitoring crucial to understand and quantify the impact of the ship radiated noise. Passive Acoustic Monitoring (PAM) systems are widely deployed for this purpose, generating years of underwater recordings across diverse soundscapes. Manual analysis of such large-scale data is impractical, motivating the need for automated approaches based on machine learning. Recent advances in automatic Underwater Acoustic Target Recognition (UATR) have largely relied on supervised learning, which is constrained by the scarcity of labeled data. Transfer Learning (TL) offers a promising alternative to mitigate this limitation. In this work, we conduct the first empirical comparative study of transfer learning for UATR, evaluating multiple pretrained audio models originating from diverse audio domains. The pretrained model weights are frozen, and the resulting embeddings are analyzed through classification, clustering, and similarity-based evaluations. The analysis shows that the geometrical structure of the embedding space is largely dominated by recording-specific characteristics. However, a simple linear probe can effectively suppress this recording-specific information and isolate ship-type features from these embeddings. As a result, linear probing enables effective automatic UATR using pretrained audio models at low computational cost, significantly reducing the need for a large amounts of high-quality labeled ship recordings. • This study presents a comparative empirical analysis of pretrained audio models for UATR. • Transfer learning reduces the need for a large labeled ship-noise datasets. • Linear probing enables effective ship-type classification from frozen audio embeddings. • Ship-type information is linearly decodable from a small subspace of the embeddings. • Pretrained audio models provide a low-cost solution for automatic UATR under minimal supervision.

Regulating hydrogen-bond network via a low-viscosity electrolyte for hydrogen evolution reaction-free aqueous sodium-ion batteries.