Abstract
The safe treatment of heavy metals in wastewater is directly related to human health and social development. In this paper, a new type of recyclable adsorbent is synthesized through the ...oxidation of enhancer and modification with magnetic nanoparticles. The new adsorbent not only inherits the advantages of multiwall carbon nanotubes (6O-MWCNTs), but also exhibits a new magnetic property and further improved adsorption capacity, which is conducive to the magnetic separation and recovery of heavy metals. The adsorption results indicate that multiwall magnetic carbon nanotubes (6O-MWCNTs@Fe
3
O
4
) have a good performance for Pb(II) selective adsorption, with a maximum adsorption capacity of 215.05 mg/g, much higher than the existing adsorption capacity of the same type of adsorbents. Under the action of an external magnetic field, 6O-MWCNTs@Fe
3
O
4
that adsorbed metal ions can quickly achieve good separation from the solution. The joint characterization results of FTIR and XPS show that under the action of both coordination and electrostatic attraction, the C=O bond in the –COOH group is induced to open by the metal ions and transforms into an ionic bond, and the metal ions are stably adsorbed on the surface of 6O-MWCNTs@Fe
3
O
4
. Pb(II) has a stronger attraction than Cu(II) and Cd(II) to the lone pair of electrons in oxygen atoms to form complexes, due to the covalent index of Pb (6.41) is more larger than that of Cu (2.98) and Cd (2.71).These data provide a new type of recyclable adsorbent for the efficient treatment of heavy metal ions in wastewater and enrich relevant theoretical knowledge.
High-sulfur waste residues (HSWR) is a typical byproduct produced in the printing and dyeing industry that has hazardous properties, such as flammability and reactivity, etc. It is of great ...significance for the directional and harmless treatment of waste residues in the later stage on the research in-depth of the occurrence characteristics of each component in HSWR. In this paper, the combinatorial analysis method is employed to perform in-depth research on the phase composition, surface chemical situation of element, and the occurrence state of functional groups of the waste residue from multiple perspectives. The results show that the organic and inorganic components in HSWR are intricately interwoven, and exhibit significant thermal instability at high temperatures, with a maximum weight less of 86.66%. Carbon mainly exists in the states of C-H/C-OH/C-C, C-O/C-NH
2
, and C=O/C-N/C-S/CH
x
and constitutes the main chain of the carboxylic ring. Sulfur mainly occurs in three forms, namely, amorphous aggregated sulfur, sulfur-containing inorganic salts such as sulfate, and nitrogen-heterocyclic organic compounds containing sulfhydryl groups and methyl sulfur groups. These organic compounds constitute branch chains of the organic phase, and combine with metal cations through hydroxyl or carboxyl groups, and deposit on the surface of inorganic agglomerated sulfur. This wrapping structure increases the stability of volatile compounds in the slag and increases the difficulty of sulfur and chlorine removal. These findings provide a material basis for the later development of safe and effective HSWR disposal techniques.
The dissolution behaviors of copper-containing sulfide (Cu2S) in fayalite-type slag have been deeply studied through experimental research and theoretical calculations. Results show that there are ...three phase changes in the process of Cu2S dissolution in the fayalite- matrix: (1) the formation of metallic copper particles and ferric iron by a redox reaction; (2) the formation of a matte (Cu2−xFexS) by crystallization; and (3) chemical dissolution to form copper-doped fayalite. The formed Fe(III)-O(BO)-Si increases the content of bridging oxygen in the slag, which slows the migration and growth of metal particles. The dissolved Cu(I) substitutes at the M1 (Fe-O-Fe) site in the fayalite lattice to form the Si-O-Fe(II)-O-Cu(I)-O-Fe(III) structure or substitutes at the M2 (Fe-O-Si) site to form the Si-O-Cu(I)-O-Fe(II)-O-Fe(III) structure to obtain a copper-doped fayalite complex, and the probability is equal for Cu(I) substituting at M1 and M2. Copper-doped fayalite is the main phase causing the chemical loss of copper in the smelting slag, and is difficult to recover by traditional flotation. Through the directional modification of the copper-doped fayalite, making Cu-O-Si bond to transform into Cu-O-S bond that is easy to be leached or Cu-S bond that is easy to be reselected, which can realize the efficient recovery of copper resources from slag. These findings provide theoretical support for improving copper resource recovery in slag or reducing copper loss when producing slag during copper smelting.
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•The diffusion rules of Cu(I) in fayalite were proved experimentally and theoretically.•Three reactions of Cu(I) existed in fayalite: redox, ion-exchange and crystallization.•Copper doped fayalite was the main phase causing copper chemical loss in smelting slag.•The substitution mechanism of Fe(II) in Fe2SiO4 by Cu(I) is an ion-exchange process.•The probability was equal for Cu(I) substituting at M1 (Fe-O-Fe) and M2 (Fe-O-Si).
In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the ...correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.
Visual tracking is complicated due to factors, such as occlusion, background clutter, abrupt target motion, and illumination variations, among others. In recent years, subspace representation and ...sparse coding techniques have demonstrated significant improvements in tracking. However, performance gain in tracking has been at the expense of losing locality and similarity attributes among the instances to be encoded. In this paper, a graph regularized and locality-constrained coding (GRLC) technique that encapsulates local manifold structure of the data in order to preserve locality and similarity information among instances is proposed. The GRLC methodology incorporates a similarity-preserving term within the objective function of the locality-constrained linear coding model, thereby overcoming some of the inherent instability issues common to such coding methods. In the proposed GRLC scheme, a graph Laplacian regularizer is chosen as a smoothing operator to learn both the representation dictionary and the coefficients by preserving the local structure of the data. This graph Laplacian smoothing operator ensures that the representations vary smoothly along the geodesics of the data manifold. Thus, by deriving the objective function of the GRLC method, a discriminative dictionary of instances can be iteratively obtained and the corresponding coefficients for each candidate can be computed using this learned dictionary. Finally, an effective observation likelihood function based on reconstruction error and a simple dictionary update scheme for visual target tracking are also proposed. Experimental results on the CVPR2013 visual tracker benchmark have demonstrated a favorable performance of the proposed technique both in terms of accuracy and robustness.
In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank ...dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.
This work was to evaluate the impacts of comprehensive rehabilitation acupuncture therapy on the recovery of neurological function in cerebral infarction (CI) patients and to utilize convolutional ...neural network (CNN) intelligent algorithms to optimize head computed tomography (CT) images and improve lesion localization accuracy. 98 CI patients were divided into a control group (Ctrl group) and an experimental group (Exp group), with 48 patients in each group. The patients in the Ctrl group received CT evaluation combined with comprehensive rehabilitation acupuncture therapy. While, those in the Exp group received CT evaluation with the use of CNN algorithms for optimization, along with comprehensive rehabilitation acupuncture therapy. Acupuncture therapy included selecting acupoints on the patient’s head, selecting two horizontal needling needles from top to bottom at the acupoints on the front side of the lesion, and then horizontal needling along the top midline. The differences in treatment outcomes were compared between the two groups based on Fugl-Meyer upper limb assessment (FMA) scores, Barthel Index (BI) scores, National Institutes of Health Stroke Scale (NIHSS4) scores, Modified Edinburgh-Scandinavian Stroke Scale (MESSS) scores, and hemodynamics. Simultaneously, the CT images were optimized using CNN intelligent algorithms to improve image quality and lesion localization accuracy. The results showed that the CI CT images processed by the CNN-based intelligent algorithm showed significant improvements in clarity and contrast compared to conventional CT images. The CNN-based intelligent algorithm demonstrated higher sensitivity (97.5 %, 93.8 %), higher PSNR (30.14 dB, 24.72 dB), and lower missed detection rate (0.52 %, 1.88 %) in detecting CI lesions. The total effective rate in the Exp group was 95.83 %, which was significantly higher than the 85.42 % in the Ctrl group (P < 0.05). The Exp group showed significantly higher levels in FMA and BI scores (P < 0.05). After treatment, the NIHSS4 and MESSS scores in the Exp group were lower than those in the Ctrl group (P < 0.05). Additionally, post-treatment, the plasma concentrations and whole-blood viscosity (low shear and high shear) in the Exp group were lower than those in the Ctrl group, and the plasma concentration and whole-blood viscosity (high shear) were also lower than those in the Ctrl group (P < 0.05). In conclusion, comprehensive rehabilitation acupuncture therapy had a positive impact on the recovery of neurological function in CI patients. By applying CNN-based intelligent algorithms to optimize head CT images, lesion localization accuracy can be improved, thereby guiding rehabilitation treatment more effectively.
•Comprehensive rehabilitation acupuncture therapy improves neurological function in CI patients.•CNN-based intelligent algorithms significantly enhance CT image clarity and contrast.•Higher sensitivity and lower missed detection rate for CI lesions using CNN-optimized CT.•Exp group showed significant improvements in FMA, BI scores, and hemodynamic indices.•CNN-optimized CT images provide more accurate lesion localization, aiding effective treatment.
•A water-based network binder combining robust mechanical property and strong affinity has been constructed.•LSO, rich in abundant strong polar bonds, can not only effectively adsorb polysulfides but ...also firmly connect TPG biopolymer chains.•An impressive long lifespan of 200 cycles at 0.2C has been achieved, under a high loading of 5.4 mg cm−2.
To achieve practical high energy density of Li-S battery, it is crucial to construct high-loading sulfur electrodes. However, the poor mechanical stability and severe shuttle effect of high-loading sulfur cathodes during cycling limit the cycling stability. Herein, a mechanically robust network binder with strong affinity towards polysulfides has been designed to stabilize the high-loading sulfur electrode. Lithium polysilicate (LSO) possesses abundant strong polar Si-O and Si = O bonds, which not only can effectively adsorb polysulfides but also firmly connect tamarind polysaccharide gum (TPG) biopolymer chains. As a result, the c-LSO-TPG binder can efficiently maintain the integrity of high-loading sulfur cathode and suppress the polysulfide shuttling during cycling. In particular, under a high sulfur loading of 5.4 mg cm−2, the sulfur electrode can run 200 cycles steadily, delivering an areal capacity of 2.7 mAh cm−2. This provides a new avenue for the research and development of robust binders for high-loading and long-lifespan Li-S batteries.
•A one-stage, end-to-end kernel learning method based on random Fourier features is proposed. This method involves a generative network to learn the distribution of kernel and to build random ...features, which are then followed by a linear layer to categorize the features.•In random features learning, this is the first one-stage method. It is different from the existing two-stage methods that first solve a kernel alignment problem and then a classification problem.•Compared with two-stage methods, the one-stage method could improve performance since the features are directly driven by the classification loss, which has been evaluated by numerical experiments.•The random resampling mechanism in the proposed method could alleviate the performance decrease brought by adversarial attack and achieve over 30% accuracy on adversarial examples of MNIST (The attack could destroy the accuracy of CNN with the same architecture to almost 0%).
Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learning an optimal feature map is often formulated as a target alignment problem, which aims to align the learned kernel with a pre-defined target kernel (usually the ideal kernel). In the second-stage process, a linear learner is conducted with respect to the mapped random features. Nevertheless, the pre-defined kernel in target alignment is not necessarily optimal for the generalization of the linear learner. Instead, in this paper, we consider a one-stage process that incorporates the kernel learning and linear learner into a unifying framework. To be specific, a generative network via RFFs is devised to implicitly learn the kernel, followed by a linear classifier parameterized as a full-connected layer. Then the generative network and the classifier are jointly trained by solving an empirical risk minimization (ERM) problem to reach a one-stage solution. This end-to-end scheme naturally allows deeper features, in correspondence to a multi-layer structure, and shows superior generalization performance over the classical two-stage, RFFs-based methods in real-world classification tasks. Moreover, inspired by the randomized resampling mechanism of the proposed method, its enhanced adversarial robustness is investigated and experimentally verified.
Due to the extensive utilization of poly (ethylene terephthalate) (PET), a significant amount of PET waste has been discharged into the environment, endangering both human health and the ecology. As ...an eco-friendly approach to PET waste treatment, biodegradation is dependent on efficient strains and enzymes. In this study, a screening method was first established using polycaprolactone (PCL) and PET nanoparticles as substrates. A PET-degrading strain YX8 was isolated from the surface of PET waste. Based on the phylogenetic analysis of 16S rRNA and gyrA genes, this strain was identified as Bacillus safensis. Strain YX8 demonstrated the capability to degrade PET nanoparticles, resulting in the production of terephthalic acid (TPA), mono (2-hydroxyethyl) terephthalic acid (MHET), and bis (2-hydroxyethyl) terephthalic acid (BHET). Erosion spots on the PET film were observed after incubation with strain YX8. Furthermore, the extracellular enzymes produced by strain YX8 exhibited the ability to form a clear zone on the PCL plate and to hydrolyze PET nanoparticles to generate TPA, MHET, and BHET. This work developed a method for the isolation of PET-degrading microorganisms and provides new strain resources for PET degradation and for the mining of functional enzymes.
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