This work studies the Pb(II) removal onto bentonite clay modified by hexadecyl trimethyl ammonium bromide (HDTMA). Characterizations of the unmodified and modified materials were performed by using ...XRD, SEM, TG-DSC, FT-IR, and BET surface area analyses. Factors influencing the uptake of Pb(II) from aqueous solution, such as pHsolution, ion strength, uptake time, adsorbent dosage, and initial Pb(II) concentration, were examined. The obtained results showed that bentonite clay was successfully modified by HDTMA, resulting in an increase in its surface area by about 70 %. The Pb(II) adsorption onto modified bentonite clay reached equilibrium at pH = 5.0 after 120 min. Studies within the isotherm and kinetic models demonstrated that the adsorption followed the Sips isotherm and pseudo-second-order kinetic models. The maximum monolayer adsorption capacity calculated from the Langmuir model at 30 °C was 25.8 mg/g, which is much higher than that obtained for the unmodified sample (18.9 mg/g). The FT-IR and TG-DSC analyses indicated that the formation of inner-sphere complexes plays a fundamental role in the mechanism of Pb(II) uptake onto HDTMA-bentonite clay. This mechanism of Pb(II) adsorption was further investigated, for the first time, by using the positron annihilation lifetime (PAL) and electron momentum (EMD) measurements. The PAL and EMD analyses indicated that the existence of Al and Si mono-vacancies in the HDTMA-bentonite should have essential contributions to the adsorption mechanism. In particular, we found a very interesting mechanism that the Pb(II) adsorption should occur inside the interlayer spaces of the HDTMA-bentonite.
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•Bentonite clay was modified by HDTMA to enhance the Pb(II) removal.•The Langmuir monolayer adsorption capacity of HDTMA-modified bentonite was 25.8 mg/g.•The mechanisms of Pb(II) adsorption were studied via different analytical methods.•The formation of inner-sphere complexes played a major mechanism in the Pb(II) uptake.
This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation ...of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page.
Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that ...leverages both instance segmentation and bio-inspired attack stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the bio-inspired attack stream corresponding with the original image and its flipped image, respectively. The output from the bio-inspired attack stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts.
PurposeThis study aims to develop a benchmarking model with productivity, management, and sustainability indicators (PMS), measure the performance of furniture firms in Vietnam, explore the causes of ...performance gaps, and identify the barriers and factors of benchmarking practice.Design/methodology/approachThe article uses both qualitative and quantitative methods. Literature review, exploratory interviews and a grounded-theory process are employed to develop a benchmarking framework and identify performance gaps, barriers and factors of benchmarking practice. The PMS benchmarking model and quantitative analysis are utilized to assess performance indicators.FindingsThe study proposes the PMS benchmarking model and measures performance indicators of furniture firms. The sources of performance gaps are explored as design, material supply, the economy of scale, market, management systems and openness. Benchmarking practice encounters barriers of difficult indicators, unsuitable firms, insufficient benchmarking knowledge, reluctance to share data, unavailable and unreliable data, and weak engagement. Benchmarking practice is determined by core factors: leader; internal factors: systems, engagement, strategy, scope, culture; external factors: customers, suppliers, associations, support, competition.Practical implicationsFirms could learn benchmarking indicators and the causes of these gaps to improve their performance. When implementing a benchmarking study, scholars and practitioners need to pay attention to barriers and factors of the benchmarking practice to ensure effective results.Originality/valueThis study develops the PMS benchmarking model and estimates performance indicators in an emerging country with the performance gap justification. It provides readers with benchmarking barriers with solutions and success factors of benchmarking practice.
Object detection has always attracted a lot of attention in computer vision due to its practical applications, i.e., robotics engineering, autonomous vehicles, and surveillance systems. Recently deep ...learning approaches have successfully improved the performance of object detection by a significant amount. However, there exist many challenging objects in the images that state-of-the-art approaches still fail to detect. In this paper, we propose an efficient approach that intentionally learns to detect the unseen (missing) objects. In particular, we utilize a dual-level of deep networks to efficiently detect difficult objects in images. The extensive experiments on three benchmarking datasets, PASCAL VOC, KITTI, and MS-COCO, show the superiority of our approach over the state-of-the-art methods.
A new carvotacetone sphaeranthone A and four known compounds 3-angeloyloxy-5-2″,3″-epoxy-2″-methylbutanoyloxy-7-hydroxycarvotacetone (2), ...3-angeloyloxy-5-3″-chloro-2″-hydroxy-2″-methylbutanoyloxy-7-hydroxycarvotacetone (3), chrysosplenol D (4), and 3-O-methylquercetin (5) were isolated from leaves of Sphaeranthus africanus growing in Vietnam. Their chemical structures were elucidated by extensive 1D and 2D NMR analysis and high-resolution mass spectroscopy as well as comparisons in literature. Compounds 1-3 were evaluated for the alpha-glucosidase inhibition. They showed moderate activity with IC
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values of 103 ± 1.7, 146.8 ± 2.5, 49 ± 0.8 µg/mL, respectively.
Masked face counting is the counting of faces at various crowd densities and discriminating between masked and unmasked faces, which is generally considered to be an object (i.e., face) detection ...task. Counting accuracy is limited, especially at higher densities, when the faces are relatively small, unclear, and viewed at various angles. Furthermore, it is costly to create the ground-truth bounding boxes needed to train object detection methods. We formulate masked face detection as a fine-grained crowd-counting task, which is appropriate for tackling this challenging task when used with density map regression. However, adopting fine-grained crowd-counting methods for masked face counting is not trivial. It is necessary to identify strategies appropriate for both counting and multi-class classification. We contrasted the strategies of various approaches and examined their benefits and drawbacks. These strategies include (1) simple regression with mixed regression and detection for counting, (2) using class-aware density maps with semantic segmentation maps and class probabilities for classification, and (3) counting with or without depth information enhancement. Analysis of seven crowd-counting methods on three datasets with a total of about 900k annotations demonstrated that the level of congestion affects how well simple regression and mixed regression and detection work for counting. Meanwhile, the most effective approach for classification is using semantic segmentation maps. Evaluation of the usefulness of using depth data demonstrated the need for a depth map to achieve accurate counting. These findings should be useful for future studies.
Thermostable and highly water-soluble polymers are essential for polymer flooding—one of the most effective methods used in the enhanced oil recovery (EOR) in high-temperature (HT) offshore ...reservoirs. In this research, the copolymerization reaction of the acrylamide (AM) and N-vinylpyrrolidone (NVP) monomers was performed via a free-radical mechanism induced by gamma-rays (γ-rays) irradiation. The impact of input data (the ratio and the concentration of the monomer to the viscosity of resultant solution) was scanned in detail and used to optimize the copolymerization conditions. The optimal viscosity values of the polymer concentration were 0.5 wt%. The optimal conditions for copolymerization were obtained at 1.7 for the AM/NVP monomer ratio and 23.2 wt% for the monomer concentration. The copolymerization induced by γ-rays irradiation under the optimized conditions was then carried out, and the obtained viscosity of 0.5 wt% of produced copolymers' solutions was 5.02 cP. These results were in good agreement with the calculated values. The obtained copolymers were then covalently coupled with graphene oxide (GO) synthesized from natural graphite using the modified Hummer’s method. The product nanocomposites (GO–P(AM-NVP) were characterized by Fourier transform infrared spectroscopy, Raman spectroscopy, scanning electron microscopy, and gel permeation chromatography. The thermal and chemical stabilities of the brine-dispersed P(AM-NVP) copolymers annealed at 123 °C (the WT Miocene reservoir temperature) and the GO–P(AM-NVP) nanocomposite dispersion annealed at 135 °C (the WT Oligocene reservoir temperature) for 31 days were observed through the visual inspection and viscosity testing. Results indicated that the dispersions of the P(AM-NVP) copolymers and P(AM-NVP) copolymers conjugated on the GO nanosheets exhibited excellent thermal and chemical stabilities; therefore, they can serve asa promising agent for EOR in HT offshore reservoirs.
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•Thermo- and salinity-resisted P(AM-NVP) copolymers were synthesized by optimal free-radical radiation-induced polymerization•Highly thermo- and salinity-resisted GO–P(AM-NVP) nanocomposite was synthesized by conjugating copolymers on GO sheets.•P(AM-NVP) and GO–P(AM-NVP) dispersions showed chemical and thermal stability when annealed at 123 °C and 135 °C for 31 days.
•This research article investigates the use of pin–fin cooling in gas turbine blades, a crucial technique for managing extreme thermal conditions during operation.•It introduces a novel geometry, the ...C-shaped-recessed endwall, and explores its impact on pin–fin cooling channels.•The study reveals significant improvements in Heat Transfer Efficiency Index (HTEI) of up to 49.75 % compared to conventional flat endwalls, promising enhanced heat transfer capabilities in gas turbine blades.
Pin-fin cooling has long been a crucial technique employed in gas turbine blades to manage the extreme thermal conditions experienced during operation. While numerous studies have investigated the heat transfer characteristics of different pin–fin configurations, the substantial impact of the endwall of the cooling channel on the heat transfer capability of turbine blades has not received adequate attention or thorough investigation. This research paper focuses on studying the influence of a novel geometry, termed C-shaped-recessed endwall, on pin–fin cooling channels in gas turbine blades. The primary objective of this investigation is to analyze the vortex formation and its impact on heat transfer characteristics within the cooling system. The study involved testing five different pin–fin arrays with C-shaped-recessed endwalls inserted between them, spanning a Reynolds number range of 7400 to 36000. The results show that with reference geometrical values, the new geometry increases the Heat Transfer Efficiency Index (HTEI) by 36.53 % compared to the flat endwall at Re = 29000. Higher heat transfer capacities were achieved by manipulating the C-shaped-recessed endwall heights and width of indentations, and the peak HTEI recorded an increase of 49.75 % at Re = 29000 compared to the flat endwall. The findings from this study underscore the potential of the C-shaped-recessed endwall geometry to improve the heat transfer capability of pin-fins by optimizing endwall configurations.
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