This study examines the key factors hindering the adoption of Building Information Modeling (BIM) in wastewater treatment plant projects. Despite the growing importance of BIM in infrastructure development, its application in the water sector remains limited and fragmented. To address this gap, a quantitative research approach was employed, utilizing a structured questionnaire distributed to professionals in the water and construction sectors. The collected data were analyzed using Structural Equation Modeling (SEM) to assess the relationships between The gathered data were examined using Structural Equation Modeling (SEM) to evaluate the relationships among variable five key elements: personnel, coordination, regulations, task-related factors, and economic factors.The results revealed that task-related factors have the strongest and most significant impact on BIM adoption, followed by coordination and economic factors, which exhibit moderate effects. In contrast, personnel and regulations have weaker but statistically significant effects. These findings suggest that operational efficiency, workflow integration, and inter-organizational coordination are more important than traditional and widespread barriers such as cost and regulatory constraints in driving BIM implementation in wastewater treatment plant projects.
This paper presented an Automatic Role Prompting System that seeks to improve the performance of the Large Language Model (LLM) by allowing models to assume varied roles through role-based prompting and, as a result, qualitatively improve the relevance of outputs. Our Automatic Role Prompting System's target audience is people who do not have domain knowledge. The guiding framework (consisting of an Automated Script for discovering roles and fields layered on top of prompt engineering, and Natural Language Inference (NLI) models trained in advance), was robustly tested through the use of three datasets: our set of 1990 curated prompts, WikiQA, and the AwesomeChatGPTPrompts. We implemented a novel evaluation strategy using GPT-Eval, which scales prompts according to completeness, clarity, and relevance. We found substantially better performance than traditional rule- and template-based approaches, yielding accuracy improvements as high as 97.6%. Overall, this work demonstrates the promise of an Automated Role Prompting System to help people engage and work more effectively and efficiently with Large Language Models (LLMs).
Vehicular ad hoc networks (VANETs) are fundamental to intelligent transportation systems (ITS), enabling secure and low-latency vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Conditional privacy-preserving authentication (CPPA) is essential for safeguarding message integrity and anonymity, yet traditional ECC- and pairing-based CPPA schemes are both computationally intensive and vulnerable to quantum attacks. Although lattice-based CPPA (L-CPPA) schemes offer post-quantum resistance and batch verification, their reliance on a single roadside unit (RSU) introduces verification bottlenecks and a single point of failure in dense traffic scenarios. To overcome these limitations, we propose a multi-aggregator lattice-based CPPA (MA-LCPPA) framework that distributes verification tasks across cooperating RSUs and integrates a (k,n)-threshold traceability mechanism. This design significantly reduces verification delay, improves scalability and enhances fault tolerance while maintaining conditional privacy and post-quantum security. Formal analysis demonstrates unforgeability, traceability and resilience against replay, impersonation, and collusion attacks under the hardness of the ISIS problem. Simulation results confirm that MA-LCPPA reduces verification delay by over 50% and lowers RSU computation costs, with minimal communication overhead, making it a scalable and quantum-secure solution for next-generation vehicular networks.
Cigarette smoking leads to the cause of mortality. Current smoking cessation often fails to prevent cravings. Intranasal drugs offer a rapid alternative, but existing nasal sprays require frequent dosing, affecting patient adherence. We developed sustained-release nicotine-encapsulated chitosan nanoparticles (NHT-loaded CSNPs) to prolong nicotine effects. NHT CSNPs were synthesized using ionic gelation. Their size and zeta potential were determined. Stability studies were conducted for 90 days. In vitro release kinetics and cytotoxicity were assessed. Pharmacokinetic properties of the NHT-loaded CSNPs were performed in male Sprague Dawley rats. The NHT: CS: Sodium Tripolyphosphate (NHT: CS: TPP) ratio of 1:4:4 resulted in the lowest particle size of 120.36 ± 3.23 nm, the zeta potential of 36.06 ± 1.70 mV, the encapsulation efficiency of 96.16 ± 0.76
Dengue fever is a growing global health concern, especially in regions like the United Arab Emirates (UAE), where environmental factors and high levels of international travel increase the risk of outbreaks. Despite this, public awareness and adoption of preventive measures remain understudied. This cross-sectional study aimed to assess the public's knowledge, attitudes, and practices (KAP) regarding dengue fever and its prevention. A validated online questionnaire was distributed to 1106 individuals using convenience sampling. The results revealed significant knowledge gaps, with only 24.6% correctly identifying dengue as a viral infection and 24.5% recognizing mosquito transmission. Median knowledge scores were higher among participants with elementary education (median = 13, IQR: 13-18), postgraduate degrees (median = 12, IQR: 8-15) than among those with other education levels, and higher among those with health insurance (median = 11, IQR: 10-13) than among those without insurance. Attitudes varied significantly based on education and residency, while regression analysis showed that male gender, older age, and smoking were associated with higher practice scores. These findings emphasize the need for targeted educational campaigns and public health interventions to improve awareness and engagement with dengue prevention, particularly in vulnerable demographic groups.
