Impact may refer to:.
The present work explores the utilisation of sugarcane bagasse and coconut shell, which are cost-effective, renewable materials for activated biochar adsorbents. The raw biomasses were carbonised and activated using H₃PO₄, KOH, and ZnCl₂. The main objective of the present work was to characterise the physicochemical properties of the synthesized adsorbents and investigate their efficiency for the removal of phenol from aqueous solutions. GRA was adopted to convert the multi-objective optimization into a single optimization for evaluate the performance of adsorption. The ANOVA was performed on the GRA data, revealing that phenol concentration is the most important influencing factor (55.2
Abstract Background Road traffic injury remains a leading cause of death and serious injury in the United Kingdom, yet the post-collision phase of care has received comparatively little research attention. The Road Injury Chain of Survival framework identifies five interdependent links where coordinated action can improve outcomes. To address evidence gaps across this pathway, we conducted the first UK Priority Setting Partnership focused specifically on post-collision care, following James Lind Alliance methodology. Methods A national open survey collected research uncertainties from patients, carers, bystanders, clinicians, emergency responders and policy stakeholders between July and August 2025. This was supplemented by a targeted literature review identifying research uncertainties from clinical guidelines and systematic reviews. All submissions underwent evidence checking using the BestBETs methodology. The Steering Group, comprising patients with lived experience, emergency service representatives, clinicians, and researchers, conducted interim prioritisation to produce a shortlist. A final prioritisation workshop was held in November 2025, using nominal group technique across three facilitated small-group rounds followed by plenary consensus. Methodological adaptations enabled structured remote participation for contributors unable to attend due to injury-related barriers. Results In total, 179 survey submissions and 73 literature-derived questions were consolidated into 57 indicative uncertainties. Following evidence checking and interim prioritisation, 23 questions proceeded to the final workshop. Thirty-nine participants reached consensus on ten priorities. These emphasised preventable deaths and critical intervention windows, recognition of occult life-threatening injuries, multi-agency coordination, technology-assisted bystander care, automatic crash notification, first aid training effectiveness, emergency call-handler decision support, inequalities in injury patterns and care, treatment of entrapped casualties, and patient-centred recovery outcomes. Conclusion This national Priority Setting Partnership provides an inclusive, stakeholder-driven foundation for research commissioning and policy development in post-collision care. The priorities highlight significant evidence gaps in the earliest phases of the care pathway and underscore the importance of addressing inequalities and aligning research with outcomes meaningful to survivors. Protocol Nutbeam, T., Leech, C., Baker, CE. et al Identifying research priorities for post-collision care in the United Kingdom: protocol for a road injury priority setting partnership. Scand J Trauma Resusc Emerg Med 33, 203 (2025). https://doi.org/10.1186/s13049-025-01513-0 .
In many applications, structures are exposed to various corrosive media. The purpose of this article is to examine the influence of corrosion media on lap welding joints of EN-8. Steel. Corrosive media considered are a 1M HCl solution, salt water, and distilled water. Corrosion rates are computed. Microstructural studies are done, and microhardness values at various zones are determined. It is observed that the severity of the corrosion attack when exposed to a 1M HCl solution is more.
Click fraud remains one of the most pressing threats in digital advertising, inflating campaign costs and distorting marketing analytics. Conventional detection approaches are frequently unable to adapt to sophisticated and evolving fraud patterns. This study proposes an explainable hybrid framework that integrates deep learning and gradient boosting for ad click fraud detection using the Kaggle Ad Click Fraud Detection Dataset (5,000 records, 21 features). During preprocessing, multicollinearity was systematically removed through Pearson correlation analysis (threshold ρ > 0.85), reducing the feature set from 21 to 14 highly discriminative attributes. Random Under-Sampling (RUS) and SMOTE were applied to correct class imbalance. Thirteen classical machine learning and deep learning models were benchmarked, including CNN, DNN, RNN, LSTM, GRU, and hybrid LSTM–GRU networks. A Voting Classifier combining XGBoost and Bagging with Decision Tree served as the proposed architecture, achieving 100% accuracy, precision, recall, and F1-score. Explainability was incorporated through LIME (local, per-prediction waterfall explanations) and SHAP (global feature importance rankings). A Flask-based web interface enables real-time fraud prediction. The architecture demonstrates inference latency below 50 ms per request, making it suitable for large-scale, real-time advertising platforms.
