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Civil infrastructure systems transform over the long-term due to complex interactions among socio-environmental stressors, degradation of physical networks, and decision-making processes of infrastructure agencies. To incorporate such complex transformation mechanisms into infrastructure cost management, this study integrates simulation modeling and life-cycle cost analysis (LCCA) to create a framework for dynamic network-level LCCA in infrastructure systems. The proposed framework was implemented using a multi-agent simulation model in a numerical case study related to a road network affected by sea-level rise, population growth, and uncertain maintenance funding. The results include identification of several state transition, and tipping point behaviors at which small changes in service demand, maintenance budget, and adaptation investment may result in significant improvement in network cost and performance conditions. The results show the capability of the proposed framework in modeling the long-term trajectories of life-cycle costs at the network level under different stressors and decision scenarios. [ABSTRACT FROM AUTHOR]

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more disparities in awareness than European countries, and awareness in Eastern Europe was higher than in central Europe. Finally, the Ratio index had high correlations with global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-31, 35-42, and 13–18 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research. [ABSTRACT FROM AUTHOR]

In the present work, a novel nanofiber-based filter has been presented which is comprised of composite nanofibers of polyamide-66 and TiO2 nanoparticles (PA-66/TiO2) for trapping cement dust. The produced nanofibers under optimum conditions were characterised by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). The effect of some important electrospinning process parameters on the obtained electrospun nanofibers including solution concentration, flow rate, voltage‚ tip to collector distance and the presence of salt was investigated. Studies showed that the solution concentration of 15 wt. % of PA-66 resulted in thinner and smother nanofibers than the other solution concentrations and also the average diameter of the electrospun nanofibers was decreased with increasing 1 wt. % salt to the polymer solution. The produced nanofiber-based filter can be applied for capturing cement dust with a good filtration efficiency of 97% for 3–500 nm particles and a quality factor in the range of 0.069–0.034 Pa−1 [ABSTRACT FROM AUTHOR]

Immersive technologies are transforming all aspects of daily life. In educational context, augmented reality (AR) and virtual reality (VR) offer the promise of experiential learning, where the real world is enhanced with information and graphics or is completely simulated. The project described in this paper, builds on advances of these technologies to develop, implement, and test "Skope," a prototype immersive learning environment. Skope is designed to facilitate interdisciplinary education of architecture, engineering, and construction (AEC) students for design and construction of environmentally sustainable buildings. The Skope learning environment is comprised of an AR and a VR visualization tool as well as learning modules that contain interdisciplinary content. Skope is implemented and tested in three interdisciplinary AEC classes. Quantitative and qualitative analyses of student performance in these classes suggest that Skope has a positive impact and increased students' motivation and understanding of building science principles, ultimately leading to an improvement of learning across domains. In addition, findings of this research indicate that using Skope improves students' attitude toward collaborative learning. [ABSTRACT FROM AUTHOR]

In the present work, a simple procedure is presented for the extraction and determination of pre-concentrated trace amounts of palladium ions through solid phase extraction (SPE) and flame atomic absorption spectrophotometry. This process was performed using Nylon-66/5-(4-dimethylaminobenzylidene) rhodanine composite nanofibres. These nanofibres were produced under optimised conditions via two-axial electrospinning technique and characterised by scanning electron microscopy and Fourier-transform infrared spectroscopy. The effect of experimental parameters including solution pH, the type and volume of eluent and contact time was investigated in extraction and desorption process. Under the optimised conditions, good linearity in the range of 0.07–8 μg L−1 and low detection limit of 0.015 μg L−1 were obtained. High enrichment factor of 187.5 and good relative standard deviation of ±2.2% at 5 μg L−1 of palladium had been achieved. The sorbent capacity for palladium adsorption was obtained 27 mg palladium per gram of nanofibres. So, the SPE was successfully applied to pre-concentrate and determine Pd(II) ions with flame atomic absorption spectrophotometer in real samples. [ABSTRACT FROM AUTHOR]

Climate change has various chronic and acute impacts on civil infrastructure systems (CIS). A long-term assessment of resilience in CIS requires understanding the transformation of CIS caused by climate change stressors and adaptation decision-making behaviors of institutional agencies. In addition, resilience assessment for CIS includes significant uncertainty regarding future climate change scenarios and subsequent impacts. Thus, resilience analysis in CIS under climate change impacts need to capture complex adaptive behaviors and uncertainty in order to enable robust planning and decision-making. This paper presents a system-of-systems (SoS) framework for abstraction and integrated modeling of climate change stressors, physical infrastructure performance, and institutional actors’ decision-making processes. The application of the proposed SoS framework is shown in an illustrative example related to the impacts of sea level rise and subsequent saltwater intrusion on a water system. Through the use of the proposed SoS framework, various attributes, processes, and interactions related to physical infrastructure and actor’s decision-making were abstracted and used in the creation of a computational simulation model. Then, the computational model was used to simulate various scenarios composed of sea level rise and adaptation approaches. Through an exploratory analysis approach, the simulated scenario landscape was used to identify robust adaptation pathways that lead to a greater system resilience under future uncertain sea level rise. The results of the illustrative example highlight the various novel capabilities of the SoS framework: (i) abstraction of various attributes and processes that affect the long-term resilience of infrastructure under climate change; (ii) integrated modeling of CIS transformation based on simulating the adaptive decision-making processes, physical infrastructure performance, and climate change impacts; and (iii) exploratory analysis and identification of robust pathways for adaptation to climate change impacts. [ABSTRACT FROM AUTHOR]

Rosa damascenaMill. is the most important Rosa species for rose oil, concrete and absolute production. In the factories, petals of rose are harvested and distilled to produce rose oil and rose water. Distilled petals are wasted and don’t have any usage. Therefore, the purpose of the present work was to use fresh and, for the first time, distilled petals to produce concrete by Soxhlet method. Then the components of obtained concretes were analyzed by capillary gas chromatography-mass spectrometry (GC-MS) and compared with the concrete of Turkey. Results indicated that, concretes of fresh and distilled petals of Lalehzar of Kerman have 0.5 % and 0.25 % content, respectively. In attention to the content of 2-phenylethanol (36.28 %, 15.48 %), and long-chain hydrocarbons such as nonadecane (9.20 %, 11.18 %), nonadecene (1.84 %, 2.15 %), eicosane (1.37 %, 1.86 %) and heneicosane (7.70 %, 8.81 %) in the concretes of fresh and distilled petals respectively, concrete of distilled rose flowers is as a new product. [ABSTRACT FROM AUTHOR]

In the present study, multiwalled carbon nanotubes (MWCNTs) as solid phase extraction sorbent were developed for preconcentration of arsenic(V) species prior to graphite furnace atomic absorption spectrometry (GFAAS) determination. Arsenic(V) was selectively sorbed on the packed column with MWCNTs within a pH 9.5 in the presence of 2-(5-bromo-2-pyridylazo)-5-diethyl amino phenol (5-Br-PADAP). The adsorbed species was then desorbed with 1 mL of 2.0 M HNO3. Experimental parameters including pH, sample volume and flow rate, type, volume and concentration of eluent that influence the recovery of the arsenic(V) species were optimised. Under the optimised conditions, the calibration curve was linear in the range of 0.2–10.0 µg L−1with detection limit of 0.016 µg L−1. The relative standard deviations (RSD) for seven replicate determinations at 1.0 µg L−1level of arsenic was 6.69%. The proposed method was successfully applied to the determination of arsenic in water samples and certified reference material (NIST RSM 1643e). [ABSTRACT FROM AUTHOR]