Stability properties of asphalt mixture incorporating coconut shell

This study aims to evaluate the effect of coconut shell at varying percentages as an aggregatereplacement on the stability properties of the asphalt mixture. The performance of thespecimen was evaluated through stability, stiffness, density and flow tests. Five coconut shellpercentages namely 0%, 10%, 20%, 30% and 40% by weight volumes were used as aggregates replacements in asphaltic concrete. The mixture design incorporating the penetration grade 60/70 bitumen was used for specimen testing. The optimum content of bitumen for asphalt mixtures was 5%. Test results showed that ththe stability, stiffness, density and flow of specimen increased with the increase of coconut shell content to a peak level (10%) and then decreased with further additions of coconut shell. Results also indicated that 10% coconut shell was the optimum replacement as an aggregate in the asphaltic concrete.Keywords: stability; coconut; stiffness; density; flow

Performance of Kaolin Clay on the concrete pavement

This paper investigates the performance of concrete pavement containing kaolin clay with their engineering properties and to determine the optimum kaolin clay content. The concrete used throughout the study was designed as grade 30 MPa strength with constant water to cement ratio of 0.49. The compressive strength, flexural strength and water absorption test was conducted in this research. The concrete mix designed with kaolin clay as cement replacement comprises at 0%, 5%, 10% and 15% by the total weight of cement. The results indicate that the strength of pavement concrete decreases as the percentage of kaolin clay increases. It also shows that the water absorption increases with the percentage of cement replacement. However, 5% kaolin clay is found to be the optimum level to replace cement in a pavement concrete

Effect of black rice husk ash on asphaltic concrete properties under aging condition

The scarcities of natural resources and increment in waste production rates have promoted efforts to investigate the potential incorporation of various by-products in roads construction. Reusing of waste materials such as black rice husk ash (BRHA) in asphaltic concrete was considered as one of the proper management of the waste, which ensures economic and environmental benefits. Hence, this study investigates the effect of black rice husk ash on asphalt mixtures properties under different aging condition. BRHA was added in the asphalt mix in a proportion of 0%, 2%, 4% and 6% by weight of bitumen. 5% optimum bitumen content with 60/70 penetration grade binder was selected for this study. The asphalt mixtures for each fraction was prepared in three different aging conditions i.e. un-aging (UA), short term aging (STA) and long term aging (LTA). The properties of asphalt mixtures were evaluated by voids, stiffness and dynamic creep tests. The results indicate that asphalt mixtures consisting of BRHA have exhibited better performance in term of voids, stiffness and creep modulus when compared to the conventional asphalt mixtures. The STA and LTA mixtures modified with BRHA produced higher performance than the unmodified mixtures. It can be concluded that the optimum additional percentage of BRHA was in the range of 4% to 6%

Genotype-Phenotype Correlation in NF1: Evidence for a More Severe Phenotype Associated with Missense Mutations Affecting NF1 Codons 844–848

Neurofibromatosis type 1 (NF1), a common genetic disorder with a birth incidence of 1:2,000–3,000, is characterized by a highly variable clinical presentation. To date, only two clinically relevant intragenic genotype-phenotype correlations have been reported for NF1 missense mutations affecting p.Arg1809 and a single amino acid deletion p.Met922del. Both variants predispose to a distinct mild NF1 phenotype with neither externally visible cutaneous/plexiform neurofibromas nor other tumors. Here, we report 162 individuals (129 unrelated probands and 33 affected relatives) heterozygous for a constitutional missense mutation affecting one of five neighboring NF1 codons—Leu844, Cys845, Ala846, Leu847, and Gly848—located in the cysteine-serine-rich domain (CSRD). Collectively, these recurrent missense mutations affect ∼0.8% of unrelated NF1 mutation-positive probands in the University of Alabama at Birmingham (UAB) cohort. Major superficial plexiform neurofibromas and symptomatic spinal neurofibromas were more prevalent in these individuals compared with classic NF1-affected cohorts (both p < 0.0001). Nearly half of the individuals had symptomatic or asymptomatic optic pathway gliomas and/or skeletal abnormalities. Additionally, variants in this region seem to confer a high predisposition to develop malignancies compared with the general NF1-affected population (p = 0.0061). Our results demonstrate that these NF1 missense mutations, although located outside the GAP-related domain, may be an important risk factor for a severe presentation. A genotype-phenotype correlation at the NF1 region 844–848 exists and will be valuable in the management and genetic counseling of a significant number of individuals

Robust fault analysis in transmission lines using Synchrophasor measurements

Abstract As more electric utilities and transmission system operators move toward the smart grid concept, robust fault analysis has become increasingly complex. This paper proposes a methodology for the detection, classification, and localization of transmission line faults using Synchrophasor measurements. The technique involves the extraction of phasors from the instantaneous three-phase voltages and currents at each bus in the system which are then decomposed into their symmetrical components. These components are sent to the phasor data concentrator (PDC) for real-time fault analysis, which is completed within 2–3 cycles after fault inception. The advantages of this technique are its accuracy and speed, so that fault information may be appropriately communicated to facilitate system restoration. The proposed algorithm is independent of the transmission system topology and displays high accuracy in its results, even with varying parameters such as fault distance, fault inception angle and fault impedance. The proposed algorithm is validated using a three-bus system as well as the Western System Coordinating Council (WSCC) nine bus system. The proposed algorithm is shown to accurately detect the faulted line and classify the fault in all the test cases presented

Comparative kinetic study of functionalized carbon nanotubes and magnetic biochar for removal of Cd2+ ions from wastewater

We did a comparative study between functionalized multiwall carbon nanotube (FMWCNTs), and magnetic biochar was carried out to determine the most efficient adsorbent to be employed in the Cd 2+ ion removal. We optimized parameters such as agitation speed, contact time, pH and adsorbent dosage using design expert vrsion 6.08. The statistical analysis reveals that optimized condition for highest removal of Cd 2+ are at pH 5.0, with dosage 1.0 g, agitation speed and contact time of 100 rpm and 90 minutes, respectively. For the initial concentration of 10mg/l, the removal efficiency of Cd 2+ using FMWCNTs was 90% and and 82% of magnetic biochar. The maximum Cd 2+ adsorption capacities of both FMWCNTs and magnetic biochar were calculated: 83.33mg/g and 62.5mg/g. The Langmuir and Freundlich constants for FMWCNTs were 0.056 L/mg and 13.613 L/mg, while 0.098 L/mg and 25.204 L/mg for magnetic biochar. The statistical analysis proved that FMWCNTs have better adsorption capacity compared to magnetic biochar and both models obeyed the pseudo-second-order

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant. © 2022 The Author(s