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A new chelating resin was synthesized by modification of Amberlite XAD-4 resin with 1,8-diaminonaphthalene through an azo linkage ( N N ). The synthesized resin was characterized by scanning electron microscope, Fourier transform infrared spectroscope, elemental analysis and thermogravimetric analysis. It was applied to preconcentrate and separate Cu(II), Cd(II) and Pb(II) ions, which were determined subsequently by graphite furnace atomic absorption spectrometry. Various physico-chemical parameters that influence the quantitative preconcentration and recovery of the metals were optimized by both static and dynamic techniques. Among them, optimum pH was in the range 6–7. 2.0 M HNO 3 was selected as eluent. Optimum flow rates of sample and the eluent were found to be 2 and 4 mL min −1 , respectively. Preconcentration factors were 220 for Cu(II) and Cd(II) and 240 for Pb(II), sorption half times ( t 1/2 ) were 5 min for Cu(II) and Cd(II) and 6 min for Pb(II), respectively. The Langmuir, Freundlich and Temkin adsorption isotherms were also studied in detail. The procedure was validated by analysis of a certified reference material (A-11, powder milk). The new method was employed to estimate the metal ion concentrations quantitatively from dairy cow’s milk collected from dairy farm, Savar, Dhaka, Bangladesh. [ABSTRACT FROM AUTHOR]

Abstract: Evaluation of crash-related injuries by medical specialists in hospitals is believed to be more exact than rather a cursory evaluation made at the crash scene. Safety analysts sometimes reach for hospital data and use them in combination with the police crash data. One issue that needs to be addressed is the, so-called, selectivity (or selection) bias possible when data used in analysis are not coming from random sampling. If not properly addressed, this issue can lead to a considerable bias in both the model coefficient estimates and the model predictions. This paper investigates pedestrian injury severity factors using linked police-hospital data. A bivariate ordered probit model with sample selection is used to check for the presence of the selectivity bias and to account for it in the MAIS estimates on the Maximum Abbreviated Injury Scale (MAIS). The presence of the sample selection issue has been confirmed. The selectivity bias is considerable in predictions of low injury levels. The pedestrian injury analysis identified and estimated several severity factors, including pedestrian, road, and vehicle characteristics. Male and older pedestrians were found to be particularly exposed to severe injuries. Rural roads and high-speed urban roads appear to be more dangerous for pedestrians, particularly when crossing such roads. Crossing a road between intersections was found to be particularly dangerous behavior. The size and weight of the vehicle involved in a pedestrian crash were also found to have an effect on the pedestrian injury level. The relevant safety countermeasures that may improve pedestrian safety have been proposed. [Copyright &y& Elsevier]

• A bio-inspired strategy is followed to fabricate two catalysts containing Au and Ag. • Magnetic graphene oxide is used as supporting material for the noble metals. • Both nanocatalysts exhibit high catalytic reduction efficiency. • A comparison of Au and Ag nanoparticles as catalysts is investigated. Noble metal nanoparticles have been widely employed for catalysis owing to their remarkable ability to reduce the activation energy of certain reactions. In this work, we followed a bio-inspired strategy to fabricate two magnetic nanocatalysts comprised of gold (AuNPs) and silver (AgNPs) nanoparticles. Magnetic graphene oxide (MGO), synthesized by deposition of Fe 3 O 4 nanoparticles on graphene oxide (GO), was used as the support material. Inspired by the adhesive property of marine mussel, polydopamine (PDA) was used as the organic linker for robust immobilization of AuNPs and AgNPs on MGO surface to produce MGO-PDA@Au and MGO-PDA@Ag, respectively. We employed a wide range of microscopic and spectroscopic techniques such as HRTEM, FESEM, EDS, XPS, XRD, FTIR etc. to characterize the catalytic nanomaterials. The catalytic performances of these as-fabricated catalysts were investigated by monitoring the reductions of methylene blue (MB) and 4-nitrophenol (4-NP) by NaBH 4 as model reactions. Thermogravimetric analysis and magnetization tests were also done to study the thermal stability and magnetic properties of the nanocomposites. The reaction rate constants obtained for both nanocomposites were found to be higher than most of the similar investigations reported elsewhere. In addition, we proposed a comparison of the catalytic performances of gold and silver nanoparticles when immobilized on same supporting material. This comparison provides means to have better insights of the factors affecting the catalytic performances of AuNPs and AgNPs along with identifying their selectivity towards specific catalytic applications. [ABSTRACT FROM AUTHOR]

• A framework for assessing competitiveness of unconventional modes (UCM) of transport. • Fusions PESTLE qualitative framework with AHP-TOPSIS based quantitative methods. • Modes compared include: Rickshaw, Easy Bike and Leguna. • Standings of competitiveness in order: Rickshaw, Easy Bike and Leguna. • Findings help in design conventional public transport and integration of UCM. Unconventional modes of transport (UCMs) play a substantial role in many developing cities in meeting the mobility demand in spite of having negative image in the eyes of the authorities. This manuscript outlines a conjoint framework where various factors affecting sustainability and competitiveness of UCMs are identified through observation study and organized using PESTLE qualitative framework. Afterwards, it presents an AHP-TOPSIS based qualitative approach where inputs from experts as well as major stakeholders (owners/operators, drivers, conductors and passengers) are combined based on the PESTLE framework to fathom the competitiveness of UCMs. Along this process, the manuscript also unveils the business model of UCMs along with their area of operation, advantages and weaknesses. For this, the study uses Dhaka, the capital city of Bangladesh as study area and selects three popular UCMs – locally known as Rickshaw, Easy bike and Leguna. The findings suggest that economic, social and political factors respectively have the highest influence among all factors of PESTLE. Rickshaw was found to be the most competitive followed by Easy bike and Leguna. The study is expected to shed light into the perception and expectation of major stakeholders of UCMs which in turn will provide valuable insight for designing conventional public transport services, and/or, help in slowly integrating the UCMs into the mainstream transportation service through addressing their shortcomings. [ABSTRACT FROM AUTHOR]