Moisture-induced damage is one of the primary causes of premature distress in asphalt pavements, leading to reduced service life and increased maintenance costs. Although nanomaterials have shown potential in enhancing asphalt performance, the underlying composite interaction mechanisms among nanomaterials, asphalt binder, and aggregate phases under moisture exposure are still not fully understood. In addition, comparative evaluations under consistent experimental conditions remain limited. This study investigates the influence of five nanomaterials: nano-silica (NS), nano-alumina (NA), nano-titanium dioxide (NT), nano-zinc oxide (NZ), and carbon nanotubes (CNT) on the physical and mechanical properties of asphalt binders and mixtures, with particular emphasis on moisture damage resistance. The nanomaterials were incorporated at dosages of 1.5%, 3.0%, 4.5%, and 6.0% by binder weight. Binder performance was evaluated using conventional and performance grading (PG) tests, while mixture performance was assessed through Marshall properties and moisture susceptibility indicators, including the tensile strength ratio (TSR) and the index of retained strength (IRS). Fluorescence microscopy (FM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were employed to investigate nanomaterial dispersion characteristics, microstructural morphology, and physicochemical interactions within the asphalt composite system. The results indicate that nanomaterial modification reduced penetration and increased softening point and Marshall stability, reflecting enhanced stiffness and thermal resistance, although ductility decreased at higher dosages. Significant improvements in moisture resistance were observed, particularly under conditioned states. The TSR increased from 81.2% for the control mixture to 92.4% for NS and 91.7% for NA, while the IRS improved from 72.7% to 88.5% for NS. Statistical analysis indicated that both nanomaterial type and dosage significantly affected TSR and IRS performance, with dosage exhibiting comparatively greater influence on moisture resistance improvement. FM and SEM analyses revealed comparatively better dispersion and lower agglomeration tendency for NS and NA, which corresponded to their superior moisture resistance performance. FTIR analysis indicated that the modification process was predominantly physical, with no major formation of new chemical functional groups. Among the investigated nano materials, NS at 6% dosage exhibited the most pronounced improvement, followed by NA at similar dosage levels. Overall, the findings suggest that nanomaterial modification can considerably improve the moisture resistance and mechanical performance of asphalt mixtures under laboratory conditions. However, higher nanomaterial dosages may adversely affect binder workability due to increased viscosity, particularly in CNT-modified binders.
Joint dysfunction disables are impacting millions of individuals worldwide. It significantly interferes with essential daily tasks like eating, drinking, and writing, often making self-care challenging for those affected. Exoskeleton robots are developed to enable individuals with impaired physical functions to perform daily activities and maintain independence. This study introduces a wearable exoskeleton control system for the elbow joint designed, providing an alternative assistive solution to traditional treatment methods. The elbow exoskeleton system used for therapy has nonlinearity and time-dependent parameters. To address these challenges, this work presents a sliding mode control (SMC) for tracking the path of an EES. To reduce the
... Show MoreThe aim of this work is to evaluate the onc-electron expectation values < r > from the radial electronic density funetion D(r) for different wave ?'unctions for the 2s state of Li atom. The wave functions used were published in 1963,174? and 1993 , respectavily. Using " " ' wave function as a Slater determinant has used the positioning technique for the analysis open shell system of Li (Is2 2s) State.
We conducted a theoretical study on the potential use of amorphous hydrogenated silicon (a-Si:H) as the high-index material in quarter-wave-stack Bragg mirrors for cavity quantum electrodynamics applications. Compared to conventionally employed
abstract
the research discussed a stage of strategic management. The strategic of the evaluation of the proposed strategy through feedback is to ensure that it is implemented with the least possible variation. The research aims at evaluation a proposed strategy for the Ministry of Planning for the years 2018-2022 in line with the orientations of the state, taking into account the surrounding environmental conditions. It relies on scientific bases and steps to formulate the strategy The extent of the strategy suitability was tested through a set of statistical means and its objectivity was verified through a survey of a number of specialized experts who were selected in accordance with the principle
... Show MoreThe aim of this work is to design an algorithm which combines between steganography andcryptography that can hide a text in an image in a way that prevents, as much as possible, anysuspicion of the hidden textThe proposed system depends upon preparing the image data for the next step (DCT Quantization)through steganographic process and using two levels of security: the RSA algorithm and the digitalsignature, then storing the image in a JPEG format. In this case, the secret message will be looked asplaintext with digital signature while the cover is a coloured image. Then, the results of the algorithmare submitted to many criteria in order to be evaluated that prove the sufficiency of the algorithm andits activity. Thus, the proposed algorit
... Show MorePatients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated
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