Enhancing asphalt binder performance against anticipated distresses is a critical focus in pavement engineering. This study investigates the synergistic influence of nano titanium dioxide (NT) and nano zinc oxide (NZ) on asphalt binder performance. Nine NT:NZ combinations (1:1 to 3:3) were prepared with 1–3% by binder weight, in addition to a reference binder (RB). The performance test program included; conventional tests (penetration, softening point, viscosity, and ductility), Dynamic Shear Rheometer (DSR) for performance grading, Multiple Stress Creep Recovery (MSCR) for rutting evaluation, and Linear Amplitude Sweep (LAS) for fatigue resistance. Furthermore, Statistical analysis (ANOVA) was performed to determine the significa

A new series of schiff base and aminothiadiazole derivatives of N- substituted phthalimide (I-VI) were synthesized. In this work, the intermediate 4-(1,3-dioxoisoindolin-2-yl)benzaldehyde compound (I), was formed by reaction of 4-amino benzaldehyde with phthalic anhydride in glacial acetic acid(GAA). A series of Schiff bases (IV-VI) was prepared by the reaction of benzidine with compound (I) in ethanol and presence of GAA as a catalyst to form compound (IV) which react with compound (I) and p-nitro benzyldehyde to give compound (V) and (VI) respectively. A new phthalimide thiosemi-carbazone derivative (ll) was prepared by reaction of compound (l) with thiosemi-carbazide HCl in the presence of equimolar amount of sodium acetate. Fina

This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values

The objectives of the study were to identify the incidence rate and characteristics of adverse drug events (ADEs) in nursing homes (NHs) using the ADE trigger tool and to evaluate the relationships between resident and facility work system factors and incidence of ADEs using the System Engineering Initiative for Patient Safety (SEIPS) model. The study used 2 observational quantitative methods, retrospective resident chart extraction, and surveys. The participating staff included Directors of nursing, registered nurses, certified nurse assistants (CNAs). Data were collected from fall 2016 to spring 2017 from 11 NHs in 9 cities in Iowa. Binary logistic regression with generalized estimated equations was used to measure the association

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberph

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification