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This study investigates the application of the vertical zone refining process to produce ultra-high-purity tin. Computational fluid dynamics (CFD) simulations were conducted using an Sn-1 wt.%Bi binary alloy to assess the effects of two key parameters—heater temperature and pulling rate—on Bi impurity segregation. The simulations revealed a dynamic evolution in molten zone height, characterized by an initial rapid rise, followed by a gradual increase and ending with a sharp decline. Despite these fluctuations, the lower solid–liquid interface consistently remained slightly convex. After nine zone passes, impurities accumulated at the top of the sample, with dual vortices forming a rhombus- or gate-shaped negative segregation zone. The simulations demonstrated that lower heater temperatures and slower pulling rates enhanced impurity segregation efficiency. Based on these results, experiments were performed using 6N-grade tin as the starting material. Glow discharge mass spectrometry (GDMS) analysis showed that the effective partition coefficients (keff) for impurities such as Ag, Pb, Co, Al, Bi, Cu, Fe, and Ni were significantly less than 1, while As was slightly below but very close to 1, and Sb was above 1. Under optimal conditions—405 °C heater temperature and a pulling rate of 5 μm/s—over 60% of impurities were removed after nine zone passes, approaching 6N9-grade purity. These findings provide valuable insights into optimizing the vertical zone refining process and demonstrate its potential for achieving 7N-grade ultra-high-purity tin. [ABSTRACT FROM AUTHOR]

The present investigation delves into the potential of vertical zone refining as an effective purification technique for achieving high-purity tin (Sn) metal. The utilization of vertical zone refining offers distinct advantages over traditional horizontal zone refining, as it allows for enhanced control over the molten zone and solid–liquid interface, ultimately leading to superior impurity separation efficiency. The present study reveals that the solute partition coefficients (k0) of various impurity elements, such as Zn, Ag, Al, Mg, Ca, Ni, In, Co, Cu, As, Pb, Fe, and Bi, during the vertical zone refining process consistently demonstrate values below one. Notably, the partition coefficient of Sb deviates slightly from the others, being greater than one but approaching one. The authors achieve exceptional levels of purity in both the bottom and middle regions of the rod by subjecting the Sn melt to nine passes of vertical zone refining at a heating temperature of 405 °C and a downward pulling rate of 10 µm/s, resulting in purities exceeding 6N4. Furthermore, by evaluating the effective partition coefficients (keff), it was determined that impurity elements, such as Cu and Bi, closely approach their equilibrium partition coefficients, reaching values of approximately 0.492 and 0.327, respectively. To further enhance the purity of Sn metal and maximize product yield, we propose the utilization of electrolytic refining and vacuum distillation, with particular emphasis on the efficient separation of five specific elements, including Cu, Fe, As, Pb, and Sb. By elucidating these findings, this study not only contributes valuable insights into the efficacy of vertical zone refining as a purification technique for high-purity tin metal, but also offers important recommendations for refining strategies and impurity element separation. [ABSTRACT FROM AUTHOR]

Spatial boundary effect can significantly reduce the performance of a learned discriminative correlation filter (DCF) model. A commonly used method to relieve this effect is to extract appearance features from a wider region of a target. However, this way would introduce unexpected features from background pixels and noises, which will lead to a decrease of the filter's discrimination power. To address this shortcoming, this paper proposes an innovative method called enhanced robust spatial feature selection and correlation filter Learning (EFSCF), which performs jointly sparse feature learning to handle boundary effects effectively while suppressing the influence of background pixels and noises. Unlike the ℓ 2 -norm-based tracking approaches that are prone to non-Gaussian noises, the proposed method imposes the ℓ 2 , 1 -norm on the loss term to enhance the robustness against the training outliers. To enhance the discrimination further, a jointly sparse feature selection scheme based on the ℓ 2 , 1 -norm is designed to regularize the filter in rows and columns simultaneously. To the best of the authors' knowledge, this has been the first work exploring the structural sparsity in rows and columns of a learned filter simultaneously. The proposed model can be efficiently solved by an alternating direction multiplier method. The proposed EFSCF is verified by experiments on four challenging unmanned aerial vehicle datasets under severe noise and appearance changes, and the results show that the proposed method can achieve better tracking performance than the state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

In the field of the muscle–computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models. [ABSTRACT FROM AUTHOR]

Despite their success, existing human pose estimation approaches mostly have complex architectures, high cost, and lack of lightweight modules. To address this problem, this paper proposes a Ghost Shuffle Lightweight Pose Network (GSLPN) with a more lightweight and efficient network architecture than the popular Lightweight Pose Network. First, in order to condense the scale of the network while maintaining its performance, we stack two lightweight modules, depthwise convolution and the Ghost module, to build our initial prototype bottleneck. Then, we impose a channel shuffle operation on the prototype bottleneck to shuffle the sequence of the feature maps for constructing Ghost Shuffle Bottleneck (Ghost Shuffle Bottleneck) with effective feature representation so as to develop a GSLPN. Second, a lightweight, efficient parallel attention mechanism, Lightweight Pose Parallel Attention, is proposed to improve keypoint locating accuracy. An experiment validating the proposed method showed that GSLPN achieved competitive performance with a smaller model size and less computational complexity than state‐of‐the‐art methods, indicating that the GSLPN is a superior approach for human pose estimation. [ABSTRACT FROM AUTHOR]

Existing prototype learning methods provide limited interpretation on which patches from input images are similar to the corresponding prototypes. Moreover, these methods do not consider the diversities among the prototypes, which leads to low classification accuracy. To address these problems, this paper proposes Characteristic Prototype Network (CDPNet) with clear interpretation of local regions and characteristic. The network designs the feature prototype to represent the discriminative feature and the characteristic prototype to characterize the prototype's properties among different individuals. In addition, two novel strategies, dynamic region learning and similarity score minimization among similar intra-class prototypes, are designed to learn the prototypes so as to improve their diversity. Therefore, CDPNet can explain which kind of characteristic within the image is the most important one for classification tasks. The experimental results on well-known datasets show that CDPNet can provide clearer interpretations and obtain state-of-the-art classification performance in prototype learning. • We propose a novel interpretable prototype learning method for classification. • The prototype learning mechanism in the previous research is analyzed and the drawbacks are revealed. • This paper presents a new strategy to learn the prototype within the dynamic region by similarity score. [ABSTRACT FROM AUTHOR]

Clinical trials investigating cardiovascular safety of dipeptidyl peptidase-IV inhibitors (DPP-4i) among patients with cardiovascular and renal disease rarely recruit patients with renal impairment, despite associations with increased risk for major adverse cardiovascular events (MACE). We investigated the risk of MACE associated with the use of DPP-4i among these high-risk patients. Using a new-user, retrospective, cohort design, we analyzed 2010–2015 IBM MarketScan Commercial Claims and Encounters for patients with diabetes, comorbid with cardiovascular disease and/or renal impairment. We compared time to first MACE for DPP-4i versus sulfonylurea and versus metformin. Of 113,296 individuals, 9146 (8.07%) were new DPP-4i users, 17,481 (15.43%) were new sulfonylurea users, and 88,596 (78.20%) were new metformin users. Exposure groups were not mutually exclusive. DPP-4i was associated with lower risk for MACE than sulfonylurea (aHR 0.84; 95% CI 0.74, 0.93) and similar risk for MACE to metformin (aHR 1.07; 95% CI [1.04, 1.16]). DPP-4i use was associated with lower risk for MACE compared to sulfonylureas and similar risk for MACE compared to metformin. This association was most evident in the first year of therapy, suggesting that DPP-4i is a safer choice than sulfonylurea for diabetes treatment initiation in high-risk patients. [ABSTRACT FROM AUTHOR]

Abstract Binary observation has been widely reported in the literature to localize or track moving objects due to its simple realization and good performance in improving energy efficiency. However, with the implementation of logic operators, the new observation models are out of the range of standard compressive sensing context, and thus lack of effective recovery algorithm. The purpose of this paper is to develop effective recovery algorithms and analyze their performance. Two kinds of recovery algorithms are developed and they are inspired from the matching pursuit method and Bayesian method, respectively. Theoretical conditions are also formulated to guarantee the successful recovery and the proposed algorithms are verified by a series of numerical experiments. Moreover, a construction method for the measurement matrix is also proposed, which is essential for model design. It is hoped that the proposed theories and algorithms can make contribution to the related applications of pattern recognition. [ABSTRACT FROM AUTHOR]

Abstract Designing a general motion detection method that has self-adaptive parameters remains a challenging issue in video surveillance. To address this problem, in this paper, a dual-target nonparametric background modeling (DTNBM) method is proposed. This model integrates the gray value and gradient to represent each pixel, which enhances the discriminative ability of the background model. We design a simple but effective classification rule for determining whether a pixel belongs to a motionless object or dynamic background. Moreover, DTNBM provides suitable updating strategies for the two categories of pixels. Most importantly, DTNBM utilizes a dual-target updating strategy to preserve the completeness of static objects and prevent false detections that are caused by background initialization or frequent background variations. To improve the updating effectiveness and efficiency, we combine similar and random schemes for background updating. The key features of DTNBM include nonparametric modeling and a controlling threshold adaptation process, which render our method easy to use on various scenarios. Comprehensive experiments have been conducted, and the results demonstrate that DTNBM outperforms the state-of-the-art methods in foreground detection. Highlights • A simple but effective pixel classification rule is explored to identify pixel. • A dual-target updating strategy is proposed to update background model. • A controlling threshold adaptation process is designed for diverse scenarios. • The proposed algorithm is very applicable for different applications. [ABSTRACT FROM AUTHOR]

A basic issue of sparse signal recovery (SSR) is to explore the condition of the uniqueness with regard to the solution of the relevant optimization framework. However, the standard uniqueness conditions, such as spark condition, NSP (null space property), RIP (restricted isometry property) and mutual coherence condition, are with respect to any sparse signal with the same sparsity. Therefore, these four conditions require certain structural or metric properties of all possible sub-matrices from the measurement matrix corresponding to the possible support indices and are quite restrictive for a given sparse signal with its support indices fixed. This work mainly considers the uniqueness issue of SSR. With the extra information of the support indices, the requirements of the measurement matrix for guaranteeing the uniqueness are released. Theoretical analysis has been performed on the uniqueness for l 0 -norm and l 1 -norm frameworks, in which loosed conditions are further validated by constructed examples. These discoveries can explain the phenomena that the occasional success of SSR in numerical simulation occurs when the above four conditions cannot be satisfied. Besides, it is analyzed that the spark condition and NSP condition are the minimal requirements for the unique recovery of the standard l 0 -norm framework and the l 1 -norm framework respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a matrix-based feature selection and classification method that takes the advantage of L 2,1 -norm regularization. Current studies show that feature extraction and selection have been important steps in classification. However, the existing methods consider feature extraction and selection to be separated phases, which generates suboptimal features for the recognition task. Aiming at making up for this deficiency, we designed a novel classification framework that performs unsupervised optimal feature selection (UOFS) to simultaneously integrate dimensionality reduction, sparse representation, jointly sparse feature extraction and feature selection as well as classification into a unified optimization objective. Specifically, an L 2,1 -norm-based sparse representation model is constructed as an initial prototype of the proposed method. Then a projection matrix with L 2,1 -norm regularization is introduced into the model for subspace learning and jointly sparse feature extraction and selection. Finally, we impose a scatter matrix-like constraint on the proposed model in pursuit of the features with less redundancy for recognition. We also provide an alternative iteration optimization with convergence analysis for solving UOFS. Experiments on public gesture and human action datasets validate the superiority of UOFS over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the rapid development of portable electronic devices and electric vehicles, lithium-ion batteries (LIBs) are being used extensively as clean and efficient power sources. Among the various anode materials being explored for use in LIBs, Ge has attracted significant attention owing to its high theoretical specific capacity. However, low electronic conductivity, relative poor Li-ion mobility, large volume expansion impede its commercial application. Herein, we synthesize a single-phase AlGeP solid solution through a facile high-energy ball milling of the raw materials of Al, P and Ge. The ternary AlGeP exhibits superior Li-storage properties than the single-component phase of Ge in terms of longer cycling stability, and faster rate performance profiting from its faster electronic conductivity confirmed by electrochemical impedance spectra, and higher Li-ion mobility confirmed by galvanostatic intermittent titration technique measurements. Moreover, we further promote the electrochemical Li-storage properties of the ternary AlGeP through carbon modification which accommodate the volume expansion and improve the electronic conductivity. Broadly this work will stimulate more research interest on ternary all-lithium-reactive compounds in the energy storage field. • Synthesis of AlGeP solid solution by high energy ball milling. • AlGeP solid solution exhibits better electrochemical performance than Ge and Al+Ge+P mixture. • Abundant lithium storage products can be generated during lithium storage. • The AlGeP/C electrode exhibits a high capacity of 312 mAh g−1 at a current density of 20 A g−1. [ABSTRACT FROM AUTHOR]

Introduction. Prostate cancer is the most common cancer among men in USA. The surgical outcomes of prostate cancer remain inconsistent. Barriers such as socioeconomic factors may play a role in patients’ decision of refusing recommended cancer-directed surgery. Methods. The Nebraska Cancer Registry data was used to calculate the proportion of prostate cancer patients recommended the cancer-directed surgery and the surgery refusal rate. Multivariate logistic regression was applied to analyze the socioeconomic indicators that were related to the refusal of surgery. Results. From 1995 to 2012, 14,876 prostate cancer patients were recommended to undergo the cancer-directed surgery in Nebraska, and 576 of them refused the surgery. The overall refusal rate of surgery was 3.9% over the 18 years. Patients with early-stage prostate cancer were more likely to refuse the surgery. Patients who were Black, single, or covered by Medicaid/Medicare had increased odds of refusing the surgery. Conclusion. Socioeconomic factors were related to the refusal of recommended surgical treatment for prostate cancer. Such barriers should be addressed to improve the utilization of surgical treatment and patients’ well-being. [ABSTRACT FROM AUTHOR]

Non-negative Matrix Factorization (NMF) is an unsupervised algorithm for low-rank approximation of non-negative data and has been widely used in many fields, but its performance in feature extraction is not satisfactory. The main reason is that the model of NMF and its variants did not take into account the label information of the samples, which can add the discriminant ability of the methods. In this paper, we proposed a novel method, called discriminant non-negative graph embedding (DNGE) algorithm in which the label information of the samples and the local geometric structure are all integrated in the objective function. Furthermore, we incorporated the between-class graph and within-class graph into the objective functions to indicate that we not only used the local separability but also used the whole separability of the samples. To guarantee convergence, we use the KKT condition to calculate the non-negative solution of the DNGE. A convergent multiplicative non-negative updating rule is then derived to learn the transformation matrix. Experiments are conducted on the CMU PIE, ORL, Yale, FERET and AR database. The results show that the DNGE algorithm provides better facial representation and achieves higher recognition rates than naïve Non-Negative Matrix Factorization and its extension methods. [ABSTRACT FROM AUTHOR]

The recently proposed robust principle component analysis (RPCA) has been successfully applied in background subtraction. However, low-rank decomposition makes sense on the condition that the foreground pixels (sparsity patterns) are uniformly located at the scene, which is not realistic in real-world applications. To overcome this limitation, we reconstruct the input video frames and aim to make the foreground pixels not only sparse in space but also sparse in time. Therefore, we propose a joint video frame set division and RPCA-based method for background subtraction. In addition, we use the motion as a priori knowledge which has not been considered in the current subspace-based methods. The proposed method consists of two phases. In the first phase, we propose a lower bound-based within-class maximum division method to divide the video frame set into several subsets. In this way, the successive frames are assigned to different subsets in which the foregrounds are located at the scene randomly. In the second phase, we augment each subset using the frames with a small quantity of motion. To evaluate the proposed method, the experiments are conducted on real-world and public datasets. The comparisons with the state-of-the-art background subtraction methods validate the superiority of our method. [ABSTRACT FROM AUTHOR]

Dissolved organic matter (DOM) or iron/manganese (hydro)oxides were important factors in the migration of Cd in sediments of wetlands. DOM and Fe oxides simultaneously affect the longitudinal and transverse migration of Cd in wetlands sediments of plants was still unclear. In this study, a 14-day rhizobox experiment was conducted and the result showed that the rhizosphere effect of Cd migration was only limited to the upper layer of sediments (− 2 to − 4 cm). Fe with fulvic acid (FA) simultaneously existed can precipitate Cd(II) from supernatant to sediments downward. Fe oxides at sediment concentration could effectively prevent Cd(II) from migrating to root surface (0.21 vs 0.02 at%). While Fe oxides with FA together at sediment concentration could effectively promoted the migration of Cd(II) to root surface (0.07 vs 0.08 at%). The formation of organo-metallic complexes of Fe in the presence of FA profoundly proved this finding (increased by ~33.0%). And the polysaccharides and aromatics in organic matter were the chief functional groups participating in the incorporation of Cd and Fe oxides. The findings reveal the migration rules of Cd(II) in sediments by FA and Fe oxides and give an insight into the mechanisms of Cd(II) migration to the root surface around wetland plants. [Display omitted] • Migration of Cd in sediments influenced by Fe and FA were firstly studied. • Cd migration to the rhizosphere was only limited to the upper layer. • Fe lonely prevent the Cd(II) from migrating to the root surface. • Fe and FA together promoted the migration of Cd(II) to the root surface. • Formation of organo-metal complexes were the main mechanisms. [ABSTRACT FROM AUTHOR]

Abstract: Sparse representation uses all training samples to represent a test sample only once, which can be regarded as a one step representation. However, in palmprint recognition, the appearances of palms are highly correlated which means the information provided by all the training samples are redundant while using the representation-based methods. Hence, how to obtain suitable samples for representation deserves exploring. In this paper, we devise a multi-step representation manner to extract the most representative samples for representation and recognition. In addition, the proposed sample selection strategy is based on contributions of the classes, not merely the effort of a single sample. Compared with some other appearance-based methods, the proposed method obtained a competitive result on PolyU multispectral palmprint database. [Copyright &y& Elsevier]

Heterogeneous domain adaptation is a challenging problem in transfer learning since samples from the source and target domains reside in different feature spaces with different feature dimensions. The key problem is how to minimize some gaps (e.g., data distribution mismatch) presented in two heterogeneous domains and produce highly discriminative representations for the target domain. In this paper, we attempt to address these challenges with the proposed incremental discriminative knowledge consistency (IDKC) method, which integrates cross-domain mapping, distribution matching, discriminative knowledge preservation, and domain-specific geometry structure consistency into a unified learning model. Specifically, we attempt to learn a domain-specific projection to project original samples into a common subspace in which the marginal distribution is well aligned and the discriminative knowledge consistency is preserved by leveraging the labeled samples from both domains. Moreover, domain-specific structure consistency is enforced to preserve the data manifold from the original space to the common feature space in each domain. Meanwhile, we further apply pseudo labeling to unlabeled target samples based on the feature correlation and retain pseudo labels with high feature correlation coefficients for the next iterative learning. Our pseudo-labeling strategy expands the number of labeled target samples in each category and thus enforces class-discriminative knowledge consistency to produce more discriminative feature representations for the target domain. Extensive experiments on several standard benchmarks for object recognition, cross-language text classification, and digit classification tasks verify the effectiveness of our method. • Distribution alighment is learnt to preserve discriminative knowledge consistency. • The data attribute can be preserved by optimizing the reconstruction loss. • Pseudo labeling strategy is developed for classifying unlabeled target samples. • The developed IDKC framework outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Chlorella@Mn catalyst had excellent resistance to alkali/alkaline earth metal poisoning. • P O Mn site could be used as a sacrificial site to bind to the alkali/alkaline earth metals. • Chlorella@Mn catalyst surface S, P and Si elements were bound to K, Na, Ca and Mg elements. • Poisoning effects of alkali/alkaline earth metals on Chlorella@Mn had been determined. • NH 3 -SCR reaction mechanism in the presence of alkali/alkaline earth metals was determined. In the selective catalytic reduction of NOx by NH 3 (NH 3 -SCR), conventional Mn-based denitration catalysts often suffered from susceptibility to poisoning by alkali and alkaline earth metals, this paper presented an innovative self-protected Chlorella@Mn denitration catalyst. Remarkably, in the presence of high concentrations (2 wt%) of alkali and alkaline earth metal oxides, the Chlorella@Mn catalyst sustained a NOx conversion exceeding 96 % at 175 °C. At an even higher concentration (4 wt%), NOx conversion above 90 % at 175 °C, surface analysis revealed that P O Mn sites acted as sacrificial sites, binding to the alkali and alkaline earth metals, the Chlorella@Mn catalyst surface naturally carried a spectrum of acidic species (such as SO 4 2−, PO 3 −, SiO 3 2−), proficient in capturing alkali/alkaline earth metal effectively, elements such as S, P, and Si formed bonds with K, Na, Ca, and Mg. The synergistic protection of the active sites and the surface elements avoided the deactivation of the catalyst. The detrimental effects of high concentrations of alkali and alkaline earth metals were primarily due to promoting an excessively high valence state of Mn on the catalyst surface and the reduction or loss of NH 3 adsorption and activation at Brønsted acid sites. This research provided valuable insights for advancing the development of low-temperature denitration catalysts with improved resistance to alkali and alkaline earth metal poisoning. [ABSTRACT FROM AUTHOR]

The β-Mn-Y-1 (Mn@Yeast-1) catalyst is successfully synthesized by introducing yeast into Mn-ions solution using one-step method, which show good catalytic activity in NH 3 -SCR DeNO x process benefiting from the Mn-organic complexes in covalent Mn C N roles and NH 3 activation on coordination sites. [Display omitted] • Perfectly effective NH 3 -SCR activity is achieved on β-Mn-Y-1 prepared by one-step method. • Mn-organic complexes are derived from the coordination between amorphous Mn and yeast. • Covalent Mn3+ C N coordination sites can activate L-NH 3 adsorbed on Mn C O during NH 3 -SCR. • The σ-π bond of Mn3+ C N sites can weaken C N and generate N-N between C N and NH 3. • Gaseous NO react with Mn-CN-NH 2 intermediates rather than B-NH 4 + from Mn-COOH by E-R mechanism. The yeast was innovatively used to participate in the catalyst synthesis. Herein, the β-Mn-Y-1 (Mn@Yeast-1) catalyst for NH 3 -SCR DeNO x was produced by introducing yeast into the solution of Mn ions using one-step method during the process of synthesizing β-MnO 2. β-Mn-Y-1 catalyst achieved the NO x conversion more than 90 % at 175 ∼ 225 °C, and still kept at > 80 % at 250 °C, in contrast, β-Mn-Y-2 (Mn@Yeast-2 by two-step method) has no catalytic activity similar to β-MnO 2 that all the NO x conversion were below 10 % at 50 ∼ 250 °C. The abundant organic functional groups on yeast surface mostly were complexed with amorphous Mn ions to generate various Mn-organic groups (Mn3+ C N, Mn C O, Mn-COOH, etc) on β-Mn-Y-1 catalyst, and some could work as acid and coordination sites to adsorb and activate NH 3 to participate in low-temperature SCR process, but rather than the traditional Mnm+-O in β-MnO 2 crystal structure in both β-MnO 2 and β-Mn-Y-2, which indicated that the pre-obtained MnO 2 crystalline phase was difficult to cooperate with yeast. More importantly, the covalent Mn3+ C N due to the effect of σ-π bond can activate L-NH 3 adsorbed on Mn C O to react with NO (g) by Eley-Rideal mechanism belonging to the coordination catalysis, while B-NH 4 + species from Mn-COOH cannot react with NO at low temperatures. [ABSTRACT FROM AUTHOR]

Recently, Multi-view Discriminant Analysis (MVDA) has been proposed and achieves good performance in multi-view recognition tasks. However, as an extension of LDA, this method still suffers from the small-class problem and has the sensitivity to outliers. In order to address these drawbacks and achieve better performance on multi-view recognition tasks, we proposed Multi-view Robust Regression (MVRR) for multi-view feature extraction. MVRR is a regression based method that imposes L 2 , 1 norm as the metric of the loss function and the regularization term to improve robustness and obtain jointly sparse projection matrices for effective feature extraction. Moreover, we incorporate an orthogonal matrix to regress the extracted features to their scaled label to avoid the small-class problem. Therefore, MVRR guarantees the projection matrix to break through the restriction of the number of class for solving the small-class problem. We also propose an iterative algorithm to compute the optimal solution of MVRR and the convergence of MVRR is proved. Experiments are conducted on four databases to verify the performance of MVRR and the result illustrates that MVRR is robust on multi-view feature extraction. • L 2,1 norm was used as the metric in loss function and the regularization term. • The proposed method is robust to outliers and can obtain jointly sparse projection matrices. • Enough dimensions can be obtained to break through the limitation of the small-class problem. [ABSTRACT FROM AUTHOR]

Exosomes are a type of extracellular vesicle actively secreted by almost all eukaryotic cells. They are ideal candidates for reliable next-generation biomarkers in the early diagnosis and therapeutic response evaluation of cancer. Thus, the quantification of exosomes is crucial in facilitating clinical research and application. Compared with traditional materials, nanomaterials have better optical, magnetic, electrical, and catalytic properties due to their small size, high specific surface area, and variable structure. The incorporation of nanomaterials into sensing systems is an attractive approach towards improving sensitivity and can provide improved sensor selectivity and stability. In this paper, we summarize the progress in nanomaterial-based exosome detection methods, including electrochemical biosensors, photoelectrochemical biosensors, colorimetric biosensors, fluorescence biosensors, chemiluminescence biosensors, electrochemiluminescence biosensors, surface plasmon resonance biosensors, and surface-enhanced Raman spectroscopy biosensors. Moreover, future research directions and challenges in exosome detection methods are discussed. We hope that this article will offer an overview of nanomaterial-based exosome detection techniques and open new avenues in disease research. Graphical abstract [ABSTRACT FROM AUTHOR]

In this paper, a new and one-pot electrodeposition method was expanded for the preparation of NiS nanoparticles-based electrochemical biosensor using metal-ion complexes as a precursor. Thioacetamide was used to control the production rate of NiS nanoparticles for the first time. The proposed electrochemical sensor was characterized by energy dispersive X-ray spectroscopy (EDX), field emission scanning electron microscope (FESEM), cyclic voltammograms (CV), and electrochemical impedance spectra (EIS). Experiment parameters were optimized. Under the optimized condition, the prepared NiS-based biosensor exhibited excellent electrocatalytic oxidation of H2O2 and glucose due to their small size. It provided fast and sensitive strategy for detecting H2O2 and glucose in the range of 1–5000 and 1–1000 μ M. The detection limit of 0.257 and 0.3 μ M was obtained for H2O2 and glucose. The mechanisms were also analyzed. The proposed biosensor exhibited excellent anti-interference and repeatability. Furthermore, it was applied in the actual sample analysis, such as human blood serum. One-pot electrodeposition method was expanded for the preparation of NiS nanoparticles-based electrochemical biosensor using metal ion complexes as precursors. Thioacetamide was used to control the production rate of NiS nanoparticles for the first time. The prepared NiS-based biosensor exhibited excellent electrocatalytic oxidation of H2O2 and glucose, and thus NiS-based electrochemical sensor was constructed for fast and sensitive detection of H2O2 and glucose. Furthermore, it was applied in the actual sample analysis, such as human blood serum. [ABSTRACT FROM AUTHOR]

Two-Dimensional Principal Component Analysis (2D-PCA) is one of the most simple and effective feature extraction methods in the field of pattern recognition. However, the traditional 2D-PCA lacks robustness and the function of sparse feature extraction. In this paper, we propose a new feature extraction approach based on the traditional 2D-PCA, which is called Nuclear Norm Based Two-Dimensional Sparse Principal Component Analysis (N-2D-SPCA). To improve the robustness of 2D-PCA, we utilize nuclear norm to measure the reconstruction error of loss function. At the same time, we obtain sparse feature extraction by adding -norm and -norm regularization terms to the model. By designing an alternatively iterative algorithm, we can solve the optimization problem and learn a projection matrix for use with feature extraction. Besides, we present a bilateral projections model (BN-2D-SPCA) to further compress the dimensions of the feature matrix. We verify the effectiveness of our method on four benchmark face databases including AR, ORL, FERET and Yale databases. Experimental results show that the proposed method is more robust than some state-of-the-art methods and the traditional 2D-PCA. [ABSTRACT FROM AUTHOR]

The theory of three-way decision has been widely utilized across various disciplines and fields as an efficient method for both knowledge reasoning and decision making. However, the application of the three-way decision theory to partially labeled data has received relatively less attention. In this study, we propose a semi-supervised co-training model based on the three-way decision and pseudo labels. We first present a simple yet effective method for producing two views by assigning pseudo labels to unlabeled data, based on which a heuristic attribute reduction algorithm is developed. The three-way decision is then combined with the concept of entropy to form co-decision rules for classifying unlabeled data into useful, uncertain, or useless samples. Finally, some useful samples are iteratively selected to improve the performance of the co-decision model. The experimental results on UCI datasets demonstrate that the proposed model outperforms other semi-supervised models, exhibiting its potential for partially labeled data. [ABSTRACT FROM AUTHOR]

• We proposed a novel method cross-domain structure learning (CDSL) for visual data recognition. • CDSL can minimize the domain shift and maximize domain-invariant feature extraction in the same time. • CDSL incorporates global distribution alignment and local discriminative structure preservation. • A simple but effective classwise structure learning strategy with a specific compactness hierarchy is designed to promote intraclass knowledge transfer and reduce the risk of negative transfer between domains. • We also extend CDSL to different kinds of kernelization to address complex situations in the real world. Unsupervised domain adaptation methods are used to train an effect model by utilizing available knowledge from a labeled source domain for solving tasks in an unlabeled target domain. The most difficult challenge is determining methods to reduce distribution discrepancies and extract the largest number of domain-invariant features between the source and target domains to improve model performance. With the aim of minimizing the domain shift and maximizing domain-invariant feature extraction, we propose a cross-domain structure learning (CDSL) method for visual data recognition, which incorporates global distribution alignment and local discriminative structure preservation to capture the common, underlying features between domains. Specifically, we design a simple but effective classwise structure learning strategy with a specific compactness hierarchy to promote intraclass knowledge transfer and reduce the risk of negative transfer between domains. We also extend CDSL to different kinds of kernelization to address complex situations in the real world. Extensive experiments on several visual data benchmarks demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

[Display omitted] More conventional low-temperature denitration catalysts are prepared chemically, and the active sites are mainly metal oxides. In this paper, the catalysts are prepared biologically, and novel denitration active sites of nonmetal-oxygen-metal are formed. The NOx conversion was first increased and then decreased with the increase of calcination temperature, and was below 20 % at 200 ℃ at a reaction temperature of 175 ℃, and was close to 100 % at calcination temperatures of 365 ℃ and 435 ℃, and was below 80 % at 600 ℃. Compared to the Chlorella@Mn catalyst prepared at 435 ℃, which possesses the highest specific surface area (104.7 m2/g), the Chlorella@Mn catalyst prepared at 365 ℃ possesses stronger NOx and NH 3 adsorption performance, which is related to the presence of a large number of P-O-Mn and C-O-Mn bonds, the presence of P-O-Mn bond could enhance the electron transfer between P and Mn, C-O-Mn bond can enhance the redox performance. The calcination temperature between 365 ℃ and 435 ℃ is the equilibrium range for denitration performance. L-H and E-R reaction mechanisms presence on Chlorella@Mn catalysts at calcination temperatures from 305 ℃ to 600 ℃, the presence of P-O-Mn and C-O-Mn bonds can also promote the generation of active nitrate species. This study will provide some reference for research on the formation of novel denitration active sites using biological methods. [ABSTRACT FROM AUTHOR]

In this study, melamine sponge was utilized as a substrate to investigate the impact of carbonization temperature on the properties of the sponge. The Response Surface Methodology was employed to optimize the preparation of a flexible NH 3 -SCR catalyst。 The surface energy of the material was modulated through temperature variation to enhance hydrophobicity and improve the water resistance of the catalyst. [Display omitted] • Carbonization temperature affects the morphology of melamine sponges. • The carbonization process causes hydrophobic modification of melamine sponges. • Response Surface Methodology is used to optimize the preparation conditions. • The catalyst surface energy is adjusted by changing the calcination temperature. • MnCo-CMS catalysts have excellent water resistance and stability for 24 h. Melamine sponge is a flexible organic rubber-plastic foam characterized by its highly porous three-dimensional mesh structure. By optimizing the carbonization process of the melamine sponge, NH 3 -SCR catalysts with enhanced flexibility were developed. In this study, MnCo-CMS (Carbonized melamine sponge) catalysts were synthesized using the impregnation method. Response surface methodology (RSM) and central composite design (CCD) were employed to investigate the effects of impregnation time, loading, and calcination temperature on the mass reaction rate and N 2 selectivity. The results showed that loading had the most significant influence, followed by calcination temperature and impregnation time. Under the optimal preparation conditions of 32.5 wt% loading, the calcination temperature at 420 °C, and the impregnation time for 342 min, the mass reaction rate could reach 11.02 cm3min-1g−1, and the N 2 selectivity could achieve 87.4 %. Experimental validation confirmed that various preparation processes resulted in catalysts with high SCR activity (rate constant k > 9.5 cm3min-1g−1 and N 2 selectivity > 80 %). The Mn-Co loading should be within the range of 28–36 wt%, the calcination temperature within the range of 380–420 °C, and the impregnation time should be 150 min or more. Materials with low surface energy could result in excellent hydrophobicity. In this study, it was found that melamine sponges could be modified into low surface energy carbon foams by temperature modulation. The change in surface energy was utilized as a modification approach to enhance the water resistance of the NH 3 -SCR catalyst. Importantly, the optimized process significantly improved the stability and water resistance of the catalysts. [ABSTRACT FROM AUTHOR]

Developing effective and stable catalysts for low-temperature selective catalytic reduction (SCR) of NO x remains challenging. Herein, we constructed a hierarchical structure by loading CoMn 2 O 4 onto Ti-doped CeO 2 , that CoMn 2 O 4 /CeTiO x catalyst has shown superior deNO x activity (>95% at 100–225 °C), prominent reaction activation energy (28.8 ± 0.9 kJ mol−1) and outstanding stability (>75% at 100–200 °C within H 2 O and SO 2). The "low-temperature active sites" and "dual anti-poisoning sites" contribute to excellent activity and stability. Firstly, the hierarchical structure boosts generation of active metal-support interface, which is conducive to oxygen migration (including adsorbed oxygen (O ads), lattice oxygen (O lat) and oxygen vacancy (O v)) and metal charge transfer (Mn2+/3++Ce4+↔Mn3+/4++Ce3+, Ti4++Ce3+↔Ce4++Ti3+). This is the key to breaking through the limits of catalytic activity stability. Secondly, enhanced surface acidity favors NH 3 adsorption and activation, which accelerates -NH 2 /-NH concatenate with NO x through Eley-Rideal mechanism to generate N 2 and H 2 O. Thirdly, the dual strong SO 2 affinity sites by Ti-induced CeO 2 crystal reconstruction retard the active center affected by the sulfate species, which contributes to striking stability. This work highlights the importance of design of isolated active sites to improve SO 2 and H 2 O endurance. [Display omitted] • The CoMn 2 O 4 /CeTiOx catalyst exhibited excellent low-temperature SCR performance. • The CoMn 2 O 4 /CeTiOx catalyst took on a superior endurance to water and sulfur volatility at low-temperature. • The existence of cooperative interface of metal-support promotes electron cycling and reaction gas adsorption activation. • The supported catalysts were dominated by Eley-Rideal mechanism which was less effected by SO 2. • The presence of dual sacrifice sites delayed the deactivation of low-temperature active components. [ABSTRACT FROM AUTHOR]

Biochar-derived dissolved organic matter (BDOM), which has a substantial impact on the environmental behavior of heavy metals, is critical for understanding the environmental efficacy of biochar. Here, we used a suite of advanced spectroscopic and mass spectroscopic methods to investigate the relationship among the pyrolysis temperature of biochar, composition of BDOM, and interactions of BDOM with Cu. The binding affinity of BDOM and Cu showed incredibly increase, with the increasing pyrolysis temperature (300–500 °C) which promoted the release of condensed aromatic compounds and oxygen-containing functional groups from biochar into dissolved phase. A notable difference in the sequences binding with Cu was occurred during the changing pyrolysis temperature. The amide only involved in the binding process between Cu and BDOM at low-temperature (300 and 400 °C), whereas phenolic only associated with the such binding process at high-temperature (500 °C). Apart from this, the carboxyl and polysaccharides took part in the binding process of Cu with BDOM, no matter how higher the temperature is. A further analysis by X-ray absorption spectroscopy revealed that bidentate carboxylic–Cu complexes appear to be the predominant binding pattern for Cu to BDOM. Our results might contribute to provide novel information for the environment applications of biochar. [Display omitted] • Rising pyrolysis temperature (300–500 °C) enhances BDOM aromaticity. • High-temperature BDOM contains more O-containing functional groups. • DOM released from higher temperature biochar has higher Cu binding affinity. • DOM in different-temperature biochar showed various Cu binding sites and orders. • Bidentate carboxylic–Cu complex is the main Cu-BDOM binding model. [ABSTRACT FROM AUTHOR]

Proton-binding study of humic acid (HA) is critical for describing and modeling the binding mechanism of HA with heavy metals. However, little is known about the intrinsic relationship between protonation behavior and HA characteristics, especially in sediments. In this study, HA was extracted from sediments and combination of spectrographic titration with parallel factor analysis, Gaussian fitting model and two-dimensional correlation spectroscopy analysis was developed as a novel in-situ tool. Results indicated that the intensity changes of fluorophores of sediment HA might be dependent on the structure characteristics (fused or non-fused ring) of phenolic species in the protonation process. Compared with phenolic groups (A1, 5.27 ± 0.05 eV; A3, 3.91 ± 0.02 eV), the carboxyl groups (A2, 4.65 ± 0.03 eV) exhibited greater contribution in the response of chromophores to the protonation process of sediment HA. Furthermore, proton binding to sediment HA first occurred in carboxyl groups and then in phenolic groups. The combined technique is a promising approach for the examination of the binding sites, binding capacities, and binding order in proton-HA binding process under environmental concentrations. Importantly, this method is a sensitive, effective and non-destructive technique without any need pre-concentrate. Unlabelled Image • A combined method was employed to elucidate in situ the protonation of sediment HA. • Relationship of protonation with chromophores and fluorophores of HA was revealed. • Changes in fluorophore in protonation process depend on the structure of phenolic. • Carboxyl showed greater contribution in response of chromophore to HA protonation. [ABSTRACT FROM AUTHOR]

Abstract It is well established that water management can influence the availability of Cd in paddy soil but the role of dissolved organic matter (DOM) characteristics in this process is still unclear. Here, we measured and compared the DOM quantity and quality between flooded and wetted treatments by spectroscopic and chemometric analysis and applied correlation analysis to relate DOM characteristics with availability concentrations of Cd. Ultraviolet–visible showed that aromaticity and hydrophobicity of DOM significantly decreased with time in wetted paddy soil (p < 0.05) but had no significant difference in flooded paddy soil (p > 0.05). According the results from two-dimensional correlation spectroscopy analytical method, humic- and protein-like substances had fast response during cultivation process. Two humic-like substances (C1, C2) and two protein-like substances (C3, C4) were identified from paddy soil-derived DOM by combining emission and excitation matrix spectroscopy with parallel factor. Compared to component C1, C3, and C4, component C2 has stronger aromaticity and hydrophobicity and higher molecular size (665–1000 Da). Its proportion declined markedly during the wetting periods but increased slightly during flooding. Pearson correlation analysis illustrated that flooding was more helpful in immobilizing Cd than wetting due to the aromatic, hydrophobic, and high molecular weight constituents remained in flooded treatments and the substantial decomposition of component C2 in wetted treatments. These results suggested that spectroscopic and chemometric methods are helping to further explain the impacts of DOM quality on Cd availability under different water management practices. Graphical abstract Image 1 Highlights • Spectroscopic and chemometric analysis methods were used in this paper. • Flooding is more conducive to preserve aromatic and hydrophobic substances. • After 60d, available Cd in wet soil is 2.55 mg kg−1 more than that in flooded soil. • Cd availability is strongly related to DOM quality during flooding and wetting. • Wetting mobilizes Cd because aromaticity, hydrophobicity and molecular weight fall. [ABSTRACT FROM AUTHOR]

Abstract The binding properties of dissolved organic matter (DOM) to iron oxides and heavy metal are critical to track their dynamic biogeochemistry cycling in aqueous environment. In this study, the mechanism of DOM-Fe-Cu coupled interaction based on coprecipitation (CPT) and adsorption (SOR) was compared, and the characteristics of fulvic acid (FA) in binding process were investigated. Different sorption affinities for Cu in coprecipitation versus adsorption were confirmed according to the changes of zeta potential. Fulvic acid, characterized by FTIR, EEM-PARAFAC and 2D-COS indicated the form of functional groups and their subsequent Fe Cu binding process were influenced to a great extent via the surface charge, pH and reaction mode. Three components were confirmed in Fe FA Cu complexes and the adsorption was more dominated by the protein-like component. The C O bonds and O C O bonds of FA played significant roles during the Fe Cu binding process, and aromatic C C showed the highest sensitivity to pH in adsorption but lowest in coprecipitation due to more faction of FA with high aromaticity. With these promising results, an enhanced picture about structural changes of FA in the Fe Cu aqueous solutions was constructed and the role of reaction modes was compared, highlighting the potential of combined spectroscopic technology in studying complex interactions in aquatic systems. Highlights • The carboxylate-metal bond caused a higher intensity of carboxylate in adsorption. • Molecular weight of FA led to the difference in the sensitivity of aromatic C C to pH. • The carboxyl and polysaccharides groups generally gave the fastest responses. • Precursor to compare the characteristics of FA in coprecipitation versus adsorption. [ABSTRACT FROM AUTHOR]

Hollow, porous NiS nanocubes were prepared by a hydrothermal method starting from Ni-Co Prussian blue analogue nanocubes as the template. The morphology and structure of the NiS nanocubes were tuned by adjustment of the ion-exchange rate and the degree of chemical etching, and they were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, X-ray diffraction, and nitrogen sorption measurements. The NiS nanocubes are shown to act as a peroxidase mimic that can catalyze the oxidization of 3,3′,5,5′-tetramethylbenzidine by hydrogen peroxide (H2O2), producing a visible color change, for which the absorbance is best measured at 652 nm. The outstanding activity may result from the unique structure of the NiS nanocubes. The catalytic oxidation follows Michaelis-Menten kinetics and shows a ping-pong mechanism of enzyme action. The findings were used to develop a rapid, sensitive, and selective colorimetric H2O2 assay with a response that is linear in the 4-40 μM range with a detection limit of 1.72 μM (signal-to-noise ratio of 3).ᅟ [ABSTRACT FROM AUTHOR]

A blood glucose meter is an electronic medical device used for determining the concentration of glucose in blood. These meters have undergone five phases of development: washed blood glucose meters, wiped blood glucose meters, colorimetric blood glucose meters, electrochemical blood glucose meters, and micro, multiple site blood glucose meters. Thanks to their speed, portability, low cost, and easy operation, blood glucose meters have been widely available for use in clinical diagnosis. Recently, coupling of target recognition elements (antibody-antigen recognition, nucleic acid hybridization, enzyme recognition, and click chemistry) with signal transduction and amplification strategies (glucose-generating enzymes, nicotinamide adenine dinucleotide (NADH)-generating enzymes, encapsulated glucose, nanomaterials, and cyclic amplification of DNA) has allowed various targets to be determined via the relationship between the signal of the blood glucose meter and the concentration of targets. In this paper, a brief review of the development and mechanism of blood glucose meters is given first. Then, more details on the application of blood glucose meters in analysis are described, including biomedical analysis, food analysis, and environmental analysis. Finally, the prospect of future development of blood glucose meters is also discussed.ᅟ [ABSTRACT FROM AUTHOR]

Abstract Biochar-derived dissolved organic matter (DOM) is important for determining the application potential of biochar in soil remediation. However, little is known about the degradation behavior of biochar-derived DOM and its interaction with heavy metals. Here, incubation experiments combined with quenching titration experiments, which analyzed by spectroscopic technology and chemometric method, were conducted to reveal such behaviors and mechanisms. Ultraviolet–visible (UV–Vis) spectra showed that high aromatic and hydrophobic fractions were enriched in biochar-derived DOM and enhanced during the cultivation process, thus the biochar-derived DOM may retain a high aromaticity, stability, and resistance. However, the environmental risk of Cu caused by the increase of DOM hydrophobicity cannot be overlooked while applying biochar to polluted soil. One fulvic-like (C1), one protein-like (C2) and two humic-like (C3, C4) substances were identified from biochar-derived DOM by using parallel factor analysis of excitation-emission matrix. Additionally, the fluorescence intensity variations of these components in DOM offered an additional interpretation for the observations from UV–Vis spectra. Two-dimensional correlation spectroscopy revealed that Cd binding to biochar-derived DOM first occurred in the protein- and fulvic-like fraction while protein- and humic-like substances had a stronger affinity for Cu. Furthermore, both phenolic and carboxyl groups firstly participated in the binding process of Cd with biochar-derived DOM, while polysaccharide gave the fastest response to Cu binding. These results clearly demonstrated the differences in specific heavy metal binding features of individual fluorescent substances and functional groups in biochar-derived DOM and contribute to improving the application effect of biochar in a multi-heavy metal polluted soil system. Graphical abstract Unlabelled Image Highlights • The biochar-derived DOM has a high aromaticity, stability, and resistance. • Environmental risk of Cu cannot be neglected while using biochar in polluted soil. • Binding behaviors of DOM with heavy metals was analyzed using 2DCOS of SF and FTIR. • The work enhances the knowledge of behavior of Cd and Cu in biochar-amended soils. [ABSTRACT FROM AUTHOR]

Abstract As a famous linear manifold learning method, orthogonal neighborhood preserving projections (ONPP) is able to provide a set of orthogonal projections for dimensionality reduction. However, a problem of ONPP is that it takes the L 2 -norm as the basic measurement and therefore tends to be sensitive to the outliers or the variations of the data. Aiming at strengthening the robustness of the conventional method ONPP, in this paper, a robust and sparse dimensionality reduction method based on linear reconstruction, called Robust Jointly Sparse Embedding (RJSE), is proposed by introducing L 2, 1 -norm as the basic measurement and regularization term. We design a simple iterative algorithm to obtain the optimal solution of the proposed robust and sparse dimensionality reduction model. Experiments on four benchmark data sets demonstrate the competitive performance of the proposed method compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A multiview method, GRMDA, is proposed for extracting the robust and sparse features, which reconstructs within-class scatter term and between-class scatter term in multiview scenario using L 2 , 1 norm to enhance robustness. • We conduct the analysis of small-class problem and adopt another criterion inspired by MMC to solve this problem. In terms of overfitting problem, the L 2 , 1 norm is also imposed on the regularization term for extracting sparse features. • The paper presents an iterative algorithm to compute optimal linear transform for each view. The poof of convergence is provided, and the computational complexity is analyzed. Several experiments were conducted to evaluate the performance of GRMDA Multiview data leads to the demand for classifying samples from various views, and the large gap between different views makes the classification task challenging. Recently, researchers have extended linear discriminant analysis (LDA) to multi-view scenarios. However, the extended methods are generally associated with the small-class problem, that is, the projection size is limited by the number of classes. In addition, they are sensitive to variations in images or outliers. To solve these problems, this study proposes a generalized robust multiview discriminant analysis (GRMDA) to obtain a linear transform for each view and for learning multiview jointly sparse discriminant common subspace. GRMDA aims to achieve both maximal between-class and minimal within-class variation for data from multiple views in a common space. Instead of formulating the ratio trace problem, we reformulate GRMDA inspired by maximum margin criterion (MMC) to address the small-class problem. Moreover, the proposed method achieves stronger robustness by reconstructing the within-class and between-class scatter terms from the definition of L 2 , 1 norm. Furthermore, GRMDA ensures joint sparsity using the L 2 , 1 norm-based regularization term. Additionally, we present an iterative algorithm, convergence proof, and complexity analysis. Experiments on six popular databases, that is, COIL100, USPS/MNIST, Extended Yale Face B, AR, BBCSport, and multiple feature datasets, were conducted to evaluate the performance of GRMDA against the state-of-the-art multiview methods. The experimental results demonstrate that the proposed method can achieve a significant performance with strong robustness and fast convergence. [ABSTRACT FROM AUTHOR]

• Drying-wetting cycles help to retain high aromatic and molecular weight DOM. • The proportion of condensed aromatics increased by 7% after drying-wetting cycles. • Availability of Cd had higher sensibility to dry-wet cycles than that of others. • Available Cd decreasing was related to an increase in aromatic and hydrophobic DOM. Investigating the influence mechanism of drying-wetting cycles on the availability and mobility of heavy metals in sediment from the perspective of the molecular composition of dissolved organic matter (DOM) may gain a new understanding, but little current information exists. Here, we used spectral technologies, high-resolution mass spectrometry, and elemental stoichiometry method to trace the change rules of the molecular composition of DOM in the riparian sediment of the river. Results showed that the drying-wetting cycles could benefit the degradation of labile fractions (e.g., proteins, aliphatics, and lipids) of DOM and retain the fractions with high aromaticity and molecular size (e.g., lignin). The decrease in the availability of Cd after drying-wetting alternation processes was highly related to these changes in DOM composition. However, the availability of Zn and Cu remained almost unchanged, which probably resulted from the release and depletion of N and S in sediment-derived DOM under drying-wetting alternation conditions. As for Cr, its exchangeable fraction was unchanged during the drying-wetting alternation process, likely due to its high stability in the sediment. These results have implications on the environmental geochemical cycling of heavy metals in the riparian sediment with frequent drying-wetting alternation. [Display omitted] [ABSTRACT FROM AUTHOR]

Understanding the environmental behavior of biochar-derived dissolved organic matter (BDOM) is crucial for promoting the extensive utilization of biochar and meeting the carbon neutrality targets. However, limited studies focused on the binding mechanism of protons and Cd with DOM released from biochar produced at different pyrolysis temperatures. By combining excitation-emission matrix spectroscopy and parallel factor analysis, we found that the humic-like fluorophores in BDOM had higher aromaticity, molecular weight, and contents of carboxylic and phenolic groups relative to the protein-like fluorophores. Conversely, the protein-like fluorophores exhibited a stronger binding affinity for Cd than humic-like fluorophores. With the pyrolysis temperature increased from 300 °C to 500 °C, the quenching effects of Cd on the protein-like components were enhanced significantly. Their fluorescence intensities could be quenched up to 51.64%. The results of ultraviolet–visible absorbance spectroscopy and differential absorbance spectroscopy showed that the carboxylic-like and phenolic-like chromophores were involved in the protons and Cd binding process of BDOM. The binding ability of phenolic-like chromophores with Cd was reduced as a function of increasing pyrolysis temperature. These findings implied that these carboxylic and phenolic groups were mainly contained in the non-fluorescent components. Besides, protons and Cd could also induce inter-chromophore interactions in BDOM, and the interaction was proportional to the pyrolysis temperature. These results clearly demonstrated the pyrolysis temperature-dependent changes in the protons and Cd binding properties of BDOM. More importantly, the possible risk of Cd mobility caused by the protein-like components in BDOM cannot be ignored when the biochar was applied in contaminated soils. This research extends our knowledge of the application potentiality of biochar in heavy metal polluted soil. [Display omitted] • Protein-like fluorophores had higher affinity for Cd than humic-like fluorophores. • Rising pyrolysis temperature promoted the binding of protein-like components to Cd. • Cd preferred to bind to the carboxyl and phenol groups found in non-fluorophore. • Protons and Cd could induce inter-chromophore interactions in biochar-derive DOM. • The inter-chromophore interactions improved as pyrolysis temperature increasing. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an efficient fire detection method for intelligent monitoring of self-service banks. This method includes the following steps: motion region detection, color-based fire detection and fire region refinement. These steps are as follows. First, the method uses four Gaussian distributions to construct an adaptive background model of the scene, and exploits this model to obtain the moving regions of the image. Second, the method uses RGB color and HSI color to determine whether the moving regions are fire candidates or not. These two steps are able to detect almost all of the true fire regions but it also takes a portion of non-fire regions as the fire. Third, the proposed frame difference procedure and the distance of two fire regions of two adjacent frames to eliminate the false fire regions obtained using the first two steps. The experimental results show that the proposed method performs very well in small-scale fire detection. The false reject rate (FRR) and false accept rate (FAR) of our method are 10.4%, and 6.02%, respectively. [Copyright &y& Elsevier]

Though there have been many applications of object tracking, ranging from surveillance and monitoring to smart rooms, object tracking is always a challenging problem in computer vision over the past decades. Mean Shift-based object tracking has received much attention because it has a great number of advantages over other object tracking algorithms, e.g. real time, robust and easy to implement. In this survey, we first introduce the basic principle of the Mean Shift algorithm and the working procedure using the Mean Shift algorithm to track the object. This paper then describes the defects and potential issues of the traditional Mean Shift algorithm. Finally, we summarize the improvements to the Mean Shift algorithm and some hybrid tracking algorithms that researchers have proposed. The main improvements include scale adaptation, kernel selection, on-line model updating, feature selection and mode optimization, etc. [ABSTRACT FROM AUTHOR]

Sediments are the major sinks for Cd(Ⅱ) in the aquatic environment. Here, the detailed binding mechanisms and effects of environmental factors on Cd(Ⅱ) adsorption onto lake sediment were tested by a batch of adsorption and characteristic experiments. Sediment samples and sediment-Cd complexes were characterized using Scanning electron microscopy, Energy dispersive spectroscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and X-ray diffraction spectral analyses. The interactive and main effect of parameters such as pH, flow velocity, Cd(II) concentration, sediment particle size, humic acid, fulvic acid and adsorption time involved in the adsorption process were determined using two models based on response surface methodology (RSM) and a back-propagation neural network with genetic algorithm (GABP). Results showed that Cd(II) adsorption onto sediment was mainly achieved through surface complexation with O-containing groups and precipitation with carbonate and sulfide. RSM was favorable for modeling Cd(II) adsorption in lake systems because it intuitively reflected the influence of the factors and had a good fitting precision (R2 = 0.8838, RSME = 2.5496) close to that of the GABP model (R2 = 0.8959, RSME = 2.5410). pH, sediment particle size, and humic acid exerted strong influences on Cd(II) immobilized by the sediment. Overall, our findings facilitate a better understanding of Cd(II) mobility in lakes and provide a reference for controlling heavy metals derived from both aqueous and sediment sources. • Surface complexation and precipitation were main mechanism for Cd(Ⅱ) adsorption by lake sediment. • Superiority of CCD-RSM to GABP in explaining factors effect. • pH, sediment particle size, and HA are most significant parameters on Cd(Ⅱ) adsorption. • The adsorption of Cd(Ⅱ) reaches 99.36% at obtained optimum condition. [ABSTRACT FROM AUTHOR]