The manufacturing of clinker is the main reason of cement and concrete sector contributes significantly to global carbon dioxide emissions. Agricultural waste ashes have drawn more attention as sustainable supplemental cementitious materials (SCMs) in response to the growing need for low-carbon and resource-efficient building materials. Rice husk ash, sugarcane bagasse ash, wheat straw ash, millet husk ash, wood waste ash, and corn cob ash are among the experimentally reported agricultural waste ashes that are thoroughly and critically reviewed in this study. Their physical, chemical, mechanical, durability, and microstructural performance in cementitious composites is also systematically evaluated. The review analyzes agro-ashes according to chemical composition (SiO₂–AlO₃–FeO₃ concentration), pozzolanic reactivity, particle properties, and compliance with ASTM-based SCM criteria by synthesizing data from a variety of peer-reviewed publications. Important results show that silica-rich agro-ashes have a high pozzolanic activity and may substitute cement to a certain extent (usually between 5 and 20
Small-molecule drugs have gained significant interest in therapeutic development. However, their therapeutic efficacy is often limited by poor target specificity, low bioavailability, and increased toxicity at higher doses. In recent years, biomolecule-based therapeutics such as proteins and peptides have emerged as promising alternatives due to their high specificity, solubility, and low toxicity. Despite these advantages, the intracellular delivery of these molecules remains challenging due to their poor stability, short half-life, and endo-lysosomal entrapment. Several delivery strategies have been developed to overcome these limitations, such as chemical modifications, structural modifications like poly(ethylene glycol) conjugation (PEGylation), and advanced carrier systems such as cell-penetrating peptides (CPPs), nanoparticles (NPs), functional polymers, and antibody-based delivery systems. Non-viral delivery systems have gained considerable attention due to their biocompatibility, low toxicity, and relatively high efficiency. This review provides a comprehensive overview of the various intracellular delivery strategies of therapeutic proteins, highlighting their mechanisms of cellular uptake, advantages, limitations, and future perspectives in biomedical applications.
Accurate hand gesture recognition plays a vital role in advancing human–computer interaction, particularly in domains such as sign language translation and gesture-based control systems. Despite notable progress, existing methods often struggle in uncontrolled environments, where variations in lighting, background clutter, occlusion, and hand orientation significantly hinder performance. Moreover, conventional approaches typically treat segmentation and classification as independent stages, while isolated models and traditional ensemble schemes often underperform in case of visually similar gestures and the variability of unconstrained environments. This paper introduces DCapNet, an integrated deep learning framework that employs U-Net–based hand segmentation and multi-model classification with a novel confidence-based ensemble mechanism. The segmentation module isolates hand regions from RGB images, and the extracted regions are subsequently fed to four state-of-the-art convolutional neural networks (ResNet50, EfficientNetB0, InceptionV3, and XceptionNet). The ensemble strategy then aggregates the confidence scores from all models to determine the final class corresponding to the highest cumulative score, effectively utilizing the complementary strengths of each architecture while minimizing individual model biases. Performance across several experiments and related ablation studies underscore the superiority of the developed framework, achieving 96.32 https://github.com/taniyasahana-19/DCapNet .
In this article we consider the classical inference of a semi-parametric stage life testing model under time constraint. We assume only two stress levels of a stage life testing experiment and a simple step-stress life testing experiment becomes a special case under this set up. We do not assume any specific parametric form of the lifetime of experimental units, rather we assume a piece wise increasing, decreasing or a constant hazard rate for the experimental units. The Weibull distribution becomes a special case under this model assumption. It is well known that due to its flexibility the Weibull distribution is a widely used lifetime distribution for analyzing time to event data. The proposed semi-parametric model is more flexible than the Weibull model and based on the data obtained from a stage life testing experiment we assume the piece wise increasing or decreasing or a constant hazard rate function. We have obtained the maximum likelihood estimators of the model parameters. Though the model involves significant number of unknown parameters, we just need to solve one or two dimensional optimization problem. Since the small sample properties of the maximum likelihood estimators are difficult to obtain we propose to use asymptotic properties for the construction of the confidence intervals of the unknown parameters. An extensive simulation study has been performed to assess the performance of the proposed estimator. One simulated data from stage life testing experiment and one real data from step-stress life testing experiment have been analyzed for illustrative purpose. In real data analysis, it has been observed that, proposed semi-parametric model fits the data better than the Weibull model.
Serotonin is one of the crucial neurotransmitters that plays a vital role in human physiology and normally exists in human serum in the 200 nM to 1.1 mu M range along with a wide variety of interfering agents. This makes it a quite challenging task to detect serotonin in an efficient, electrochemical way at ambient temperature in a relatively low concentration (nanomolar) of abundance. So, this work is focused on developing an enzyme-less electrochemical sensing platform that can detect serotonin with a very low limit of detection (LOD) in a highly sensitive and selective way. For this purpose, carbon quantum dots (CQDs), one of the advanced carbonaceous materials, were prepared from a natural source (corn seeds) and applied to make a nanocomposite sensing platform with an electrochemically deposited NiWO(4 )thin film on FTO-coated glass substrate. The prepared materials were thoroughly characterized by using sophisticated instrumentation techniques. The fabricated NiWO4/CQD nanocomposite thin film was then subjected to detailed electrochemical probing toward serotonin sensing. Fascinatingly, the developed sensor prototype yielded an LOD of 134.0 nM without compromising the sensitivity (16.9 mu A mu M-1 cm(-2)), which makes it well capable for detecting serotonin that is present in human serum in the nM to mu M order.
