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• A new target-cognisant siamese-based anchor-free tracker. • The proposed method computes cross-spatial attention for refining the measurement of spatial similarity. • Two tracking mechanisms are used to promote the precision of bounding box prediction. • A max filtering module is proposed to filter out similar distractors. • Our method achieves competitive performance on several tracking datasets. Siamese trackers have become the mainstream framework for visual object tracking in recent years. However, the extraction of the template and search space features is disjoint for a Siamese tracker, resulting in a limited interaction between its classification and regression branches. This degrades the model capacity accurately to estimate the target, especially when it exhibits severe appearance variations. To address this problem, this paper presents a target-cognisant Siamese network for robust visual tracking. First, we introduce a new target-cognisant attention block that computes spatial cross-attention between the template and search branches to convey the relevant appearance information before correlation. Second, we advocate two mechanisms to promote the precision of obtained bounding boxes under complex tracking scenarios. Last, we propose a max filtering module to utilise the guidance of the regression branch to filter out potential interfering predictions in the classification map. The experimental results obtained on challenging benchmarks demonstrate the competitive performance of the proposed method. [ABSTRACT FROM AUTHOR]

[Display omitted] • The relationships between network topological characteristics and the different ESVs were analyzed. • The important sources and corridors are mainly located along the sides of the Wei River and the Fen River. • Higher ESVs related to water resources have better network connectivity. • The "one central source and three forested belts" radially ecological arrangement is planned. There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern. [ABSTRACT FROM AUTHOR]

In the realm of the video captioning field, acquiring large amounts of high-quality aligned video–text pairs remains laborious, impeding its practical applications. Therefore, we explore the modelling techniques for unsupervised video captioning. Using text inputs similar to the video representation to generate captions has been a successful unsupervised video captioning generation strategy in the past. However, this setting relies solely on the textual data for training, neglecting vital visual cues related to the spatio-temporal appearance within the video. The absence of visual information increases the risk of generating erroneous video captions. In view of this, we propose a novel unsupervised video captioning method that introduces visual information related to text features keywords to implicitly enhance training for text generation tasks. Simultaneously, our method incorporates sentence to explicitly augment the training process. our method injects additional implicit visual features and explicit keywords into the model, Which can inject the generated captions with more accurate semantics. the experimental analysis demonstrates the merit of the proposed formulation, achieving superior performance against the state-of-the-art unsupervised studies. • Contrast learning is used to minimise the disparities between pseudo-text labels and video features. • Visual clues are aligned with the text generator for consistent semantic enhancement. • Leveraging the Found within the sentences for semantic preservation. • Outperforming existing unsupervised video captioning approaches. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of meteorological and land surface hydrothermal on LAI in mountainous areas was assessed. • A clear greening trend was observed in Yan Mountains, and the greening trend was the largest in summer. • LAI was negatively correlated with land surface temperature and soil moisture but positively correlated with precipitation and air temperature. • The hysteresis of LAI of different vegetation types to changes in hydrothermal conditions was explored. Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection. [ABSTRACT FROM AUTHOR]

• A quadratic optimisation method with dimension reduction for fast sparse representation based classification. • The linear discriminative analysis is used to further improve the discriminative capability of the proposed method. • State-of-the-art deep CNN features is used for performance boosting. Extended sparse representation-based classification (ESRC) has shown interesting results on the problem of under-sampled face recognition by generating an auxiliary intraclass variant dictionary for the representation of possible appearance variations. However, the method has high computational complexity due to the l 1 -minimization problem. To address this issue, this paper proposes two strategies to speed up SRC using quadratic optimisation in downsized coefficient solution subspace. The first one, namely Fast SRC using Quadratic Optimisation (FSRC-QO), applies PCA and LDA hybrid constrained optimisation method to achieve compressed linear representations of test samples. By design, more accurate and discriminative reconstruction of a test sample can be achieved for face classification, using the downsized coefficient space. Secondly, to explore the positive impact of our proposed method on deep-learning-based face classification, we enhance FSRC-QO using CNN-based features (FSRC-QO-CNN), in which we replace the original input image using robust CNN features in our FSRC-QO framework. Experimental results conducted on a set of well-known face datasets, including AR, FERET, LFW and FRGC, demonstrate the merits of the proposed methods, especially in computational efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design of MOF-derived hollow hybrids covered with a polyphosphazene coating. • Cobalt-phosphorus-nitrogen flame retardant elements synergistic effect. • Mismatch structure significantly improves the dispersity of fillers in EP. • The hybrids endow EP with better flame retardancy and mechanical performance. Polyphosphazene has drawn an abundance of attention as a flame retardant due to its eco-friendliness and phosphorus-nitrogen synergistic effect. ZIF-67 and cobalt compounds derived from cobalt ions released by ZIF-67 were employed as dual templates for loading polyphosphazene. We skillfully designed a polyphosphazene hollow hybrid material via the polymerization of phosphazene accompanied by the etching of ZIF-67 and cobalt compounds by generated HCl. The hybrid material (CPPHS) was composed of cobalt-doped polyphosphazene hollow sub-micron polyhedrons (CP) and polyphosphazene hollow nanospheres (PHNS) with different dimensions and magnitudes of sizes. After adding 2.0 wt% CPPHS, the limiting oxygen index of epoxy resin increased to 27.6%, while the peak heat release rate and total smoke production decreased by 40.1% and 38.8%, respectively. The formation of a continuous and strongly extended dense char layer in the EP composites was fostered by the synergistic effect of Co-P-N flame retardant components, significantly decreasing the heat flow and combustible gas exchange rate and effectively hindering the combustion chain reaction. Furthermore, the unique dimensional mismatch structure of CPPHS enhanced its dispersion in the EP matrix, enabling the EP to have superior mechanical properties that match realistic application requirements, consequently expanding the variety of applications for fire-resistant EP composites. [ABSTRACT FROM AUTHOR]

Abstract Traditional collaborative representation based classification (CRC) method usually faces the challenge of data uncertainty hence results in poor performance, especially in the presence of appearance variations in pose, expression and illumination. To overcome this issue, this paper presents a CRC-based face classification method by jointly using block weighted LBP and analysis dictionary learning. To this end, we first design a block weighted LBP histogram algorithm to form a set of local histogram-based feature vectors instead of using raw images. By this means we are able to effectively decrease data redundancy and uncertainty derived from image noises and appearance variations. Second, we adopt an analysis dictionary learning model as the projection transform to construct an analysis subspace, in which a new sample is characterized with the improved sparsity of its reconstruction coefficient vector. The crucial role of the analysis dictionary learning method in CRC is revealed by its capacity of the collaborative representation in an analytic coefficient space. Extensive experimental results conducted on a set of well-known face databases demonstrate the merits of the proposed method. [ABSTRACT FROM AUTHOR]

Active-targeting nanocarriers can significantly improve the transcytosis of poorly water-soluble or bio-macromolecular drugs across biological barrier. However, reasons for the improvement are not understood enough, which hampered the reasonable design of active targeting nanocarriers. To illustrate how different factors influence the transport of active-targeting nanocarriers, we established ligand-decorated micelles targeting different receptors to study how the decorations influence the transcytosis of the micelles by comparing the endocytosis, transport pathway and exocytosis process. Three different kinds of receptors, Neonatal Fc receptor (FcRn), transferrin receptor (TfR) and αvβ3 integrin were selected. They presented three different transport pathways, mainly mediating transcytosis, recycling pathway and cell binding, respectively. Their corresponding ligand FcBP, 7pep and c(RGDfK) decorated micelles with different ligand densities were prepared first. Then the effects of receptor and ligand density on the transcytosis across biological barrier were investigated. The results showed that the uptake rate of active micelles was higher than passive micelles and an optimum ligand density with most endocytosis appeared in all functional micelles. Transport pathway study showed 7pep decorated micelles were transferred into apical recycling endosome (ARE) and exocytosed to apical plasma membrane in a ligand depended way. c(RGDfK) decorated micelles were transferred through common recycling endosome (CRE) and Golgi complex to basolateral plasma membrane instead of ARE. While FcBP decorated micelles took both the recycling pathway and transcytosis through CRE, but not Golgi complex. Proper ligand density, not the higher the better, led the most uptake. Also the apical to basolateral transcytosis ratio may not be in accordance with the uptake. Among all the itineraries, transcytosis through CRE is the best itinerary for transcytosis. So, in the design of active targeting nanocarriers to overcome biological barrier, receptor character should be considered priorly, and then ligand density should be optimized. [ABSTRACT FROM AUTHOR]

• IL-37 reverses ox-LDL-induced decreased cell survival in HUVECs. • IL-37 inhibits ox-LDL-induced NLRP3 inflammasome activation in HUVECs. • IL-37 alleviates increased apoptosis and ROS production in ox-LDL-induced HUVECs. • IL-37 alleviates ox-LDL-induced endothelial dysfunction by enhancing autophagy. Atherosclerosis is a lipid-driven chronic inflammatory disease. Endothelial dysfunction is the initiating factor of atherosclerosis. Although much work has been done on the antiatherosclerotic effects of interleukin-37 (IL-37), the exact mechanism is still not fully understood. The aim of this study was to investigate whether IL-37 attenuates atherosclerosis by protecting endothelial cells and to confirm whether autophagy plays a role in this effect. In apolipoprotein E knockout (ApoE−/−) mice fed with a high fat diet, IL-37 treatment significantly attenuated progression of atherosclerotic plaques, reduced endothelial cell apoptosis and inflammasome activation. Human umbilical vein endothelial cells (HUVECs) were treated with oxidized low-density lipoprotein (ox-LDL) to establish an endothelial dysfunction model. We observed that IL-37 alleviated ox-LDL-induced endothelial cell inflammation and dysfunction, as evidenced by decreased nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation, ROS production, apoptosis rate and secretion of inflammatory cytokines IL-1β and TNF-α. Furthermore, IL-37 could activate autophagy in endothelial cells, which is characterized by the upregulation of LC3II/LC3I, the downregulation of p62 and an increase in autophagosomes. The autophagy inhibitor 3-Methyladenine (3-MA) dramatically reversed the promotion of autophagy and the protective effect of IL-37 against endothelial injury. Our data illustrate that IL-37 alleviated inflammation and apoptosis of atherosclerotic endothelial cells by enhancing autophagy. The current study provides new insights and promising therapeutic strategies for atherosclerosis. [ABSTRACT FROM AUTHOR]

gem -Difluorocyclopropanes are an important fluorinated class of compounds with applications in medicinal chemistry, material sciences and organic synthesis. The transformations based on their ring-opening reactions have been recognized to be useful methods for rapidly synthesizing various fluorinated organic molecules. In this digest paper, we describe these efforts and highlight their powerfully potential and applications as fluorine-containing synthons in organic chemistry. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate ( x 0 ), center vertical coordinate ( y 0 ), semi-major axis ( a ) and rotation angle ( θ ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m 3 /m 3 when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R 2 = 0.395, RMSE = 0.061 m 3 /m 3 ) compared to SSM measurements at the Yucheng Comprehensive Experimental Station. [ABSTRACT FROM AUTHOR]

Traditional discriminant analysis (DA) methods are usually not amenable to being studied only with a few or even single facial image per subject. The fundamental reason lies in the fact that the traditional DA approaches cannot fully reflect the variations of a query sample with illumination, occlusion and pose variations, especially in the case of small sample size. In this paper, we develop a multi-scale fuzzy sparse discriminant analysis using a local third-order tensor model to perform robust face classification. More specifically, we firstly introduced a local third-order tensor model of face images to exploit a set of multi-scale characteristics of the Ridgelet transform. Secondly, a set of Ridgelet transformed coefficients with respect to each block from a face image are respectively generated. We then merge all these coefficients to form a new representative vector for the image. Lastly, we evaluate the sparse similarity grade between each training sample and class by constructing a sparse similarity metric, and redesign the traditional discriminant criterion that contains considerable fuzzy sparse similarity grades to perform robust classification. Experimental results conducted on a set of well-known face databases demonstrate the merits of the proposed method, especially in the case of insufficient training samples. [ABSTRACT FROM AUTHOR]

As is well known, most of the sparse representation based classification (SRC) schemes are not the standard form of SR model, that is, the dictionary in SRC model is not a strict one compare to sparse representation model. Actually, a strict and standard dictionary will create sparser coefficient, but it might not achieve better classification accuracy in SRC model. Therefore, constructing a rational and optimal dictionary for SRC model is a challenging task for us. Understanding the good performance of such unconventional dictionaries demands new algorithmic and analytical techniques. In this paper, we propose a novel SRC fusion method using hierarchical multi-scale local binary patterns (LBP) and greedy search strategy (SRC-GSLBP) for face recognition. The proposed method involves three aspects: dictionary optimization, training variables selection and classification strategy, sparse coding coefficient decomposing. First of all, we get an optimized dictionary through extracting hierarchical multi-scale LBP features from the original training samples. Second, the training variables selection and classification strategy aim to represent a query sample as a linear combination of the most informative training samples, and exploits an optimal representation of training samples from the classes with major relevant contributions. Instead of eliminating several classes at one time, we choose eliminating classes one by one with greedy search (GS) sparse coding process until the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to perform classification. In the context of the proposed method, an important goal is to select a subset of variables that accomplishes one objective: the provision of a descriptive representation for sparse category knowledge structure. We develop a heuristic learning strategy to achieve this goal. Experimental results conducted on the ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Sparse representations using over complete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a pre-specified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment, which means that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel parameterized fuzzy adaptive way to adapting dictionaries. In order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by embedding a new mechanism of fuzzy set, which is called parameterized fuzzy K-SVD. Experimental results conducted on the ORL, Yale and FERET face databases demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a novel fuzzy supervised learning algorithm based on the dynamical parameter estimation. First, a reformative supervised fuzzy LDA algorithm (RF-LDA) for the training samples is proposed. Compared with the conventional fuzzy LDA algorithm, the presented algorithm computes the discriminant vectors associated with the membership grade from each training sample, which is theoretically effective in overcoming the classification limitation originating from the imprecise samples. Second, when a novel fuzzy LDA model is required in order to take some decision about the feature extraction and classification, the dynamical parameter estimation method of the fuzzy LDA model should recursively process the measured data as they become available. In the line of previous arguments, we approach the problem of control parameter estimation of RF-LDA by considering the formulation of a Hopfield Neural Network (HNN), which is named HRF-LDA. Third, considering the fact that the Kernel Fisher Discriminant (KFD) is effective in extracting the nonlinear discriminative information of the feature space by using kernel trick, a kernel version of HRF-LDA is presented subsequently, which has the potential to outperform the traditional fuzzy learning algorithms, especially in the cases of nonlinear small sample sizes. The advantage of this learning algorithm is that it successfully utilizes the improved kernel fuzzy LDA algorithm as a supervised feature extraction tool. Meanwhile, by means of the control parameter estimation, we address the problem that the particular value of offset in the calculation of the grade of membership is dynamically assigned. Extensive experimental studies conducted on the ORL, NUST603, FERET, Yale and XM2VTS face images show the effectiveness of the proposed fuzzy integrated algorithm. [Copyright &y& Elsevier]

• A drought index was obtained for drought monitoring based on soil moisture series. • Drought characteristics (e.g., return period) were presented on a regional scale. • Wavelet analysis was adopted to study the area extent characteristics of drought. As the critical parameter of the agricultural drought process, soil moisture (SM) has the potential to monitor agricultural drought, which can provide practical support for water management. In this study, a drought index, namely Soil Dryness Index (SDI), was proposed based on long-term gridded ERA-5 land SM, and its performance was evaluated. Furthermore, we employed the run theory and copula to study the spatiotemporal drought characteristics based on this index in Inner Mongolia. Although the return period of drought events has been assessed in many studies, the drought tends to occur with a nonstationary frequency. The wavelet transform was then applied to drought area proportion time series in sub-regions to study the temporal characteristics of droughts. The results show that SDI performs well in monitoring agricultural drought. There is strong heterogeneity in the spatial distribution of drought events. Frequent and low-severity droughts dominated arid and semi-arid regions, while some high-severity droughts mainly occurred in humid and semi-humid regions (mainly in Hulunbuir and Xing'an). The droughts, especially those with high severity, have become much more frequent in the last two decades. The period from 2000 to 2010 had seen more frequent droughts in recent 40 years, and most 20-year return period droughts occurred during this period. These findings could contribute to a better understanding of spatiotemporal patterns of agricultural droughts and provide a reference for future drought trends in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Spray drying is an efficient, rapid and relatively low-cost method for preparing microencapsulated lactic acid bacteria (LAB) in industry, but high temperature reduces the survival rate of LAB. Oil can polymerise with LAB by hydrophobicity and reduce the thermal and oxidative damage on bacteria during spray drying. However, oil is easily oxidised during spray drying, and its insolubility makes it difficult to evenly disperse the emulsion in the oil phase. In this comprehensive review, the oils used for LAB microcapsule and its protectional mechanism were discussed. And then wall materials and modification methods, which stabilizes co-microcapsule containing lactic acid bacteria and oil, is presented. Oils were a promising material for spray-dried LAB. Liquid oils with more unsaturated fatty acids reduced oxidative stress. Solid oils with high saturated fatty acid content are often added in the form of solid small particles, reducing the bacterial thermal damage by absorbing heat during spray drying. Appropriate wall materials and pre-treatment on oil-LAB co-microcapsule can reduce lipid oxidation, stabilize the emulsion and enhance the survival rate of lactic acid bacteria after spray drying. [Display omitted] • Oil provides a protective barrier to oxygen and heat. • The polysaccharide matrix forms a mesh gel that stabilizes bacteria. • Proteins unfold and reorient at the water-oil interface to stabilize the emulsion. • The survival rate of bacteria could be improved by wall material modification. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, some studies have been made on the essence of a novel fuzzy discriminant analysis (FDA) on the fourfold-objective model (FOM). First, a fourfold-objective model on the discriminant analysis is developed, by which a set of integrated subspaces derived from within-class and between-class scatter matrices are constructed, respectively. Second, an improved FDA (IFDA) algorithm based on the relaxed normalized condition is proposed to achieve the distribution information of each sample represented with fuzzy membership grade, which is incorporated into the redefinition of Fisher''s scatter matrices. Therefore, the presented algorithm has the potential to outperform the traditional subspace learning algorithms, especially in the cases of small sample size. Experimental results conducted on the ORL, NUST603, FERET and Yale face image databases demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

To enhance char formation of flame retardant epoxy (EP) composites, carboxymethyl β-cyclodextrin (CM-β-CD) is employed as an etchant for or ZIF-67 derivatives. In the early stage, etching plays a dominant role. The mismatch in size between CM-β-CD opening and ZIF-67 pore leads to the stacking of carboxyl cobalt complexes on the shell. When the reaction time is prolonged, crosslinking occurs between carboxyl and hydroxyl groups. Crosslinked CM-β-CD weakens and eventually stops the etching process. Triethyl phosphate (TEP), an additive to improve flame retardancy, is also absorbed on the shell in this one-pot synthesis. Herin, the synthesis of metal-organic framework (MOF) derivatives can impart multiple functions to MOF. This novel nanohybrid significantly improved flame retardancy of EP composites with only 2.0 wt% loading. The peak heat release rate (pHRR) and total smoke production (TSP) were reduced by 54.8 and 46.9%, respectively. The integrated multi-element system resulted in an expanded and reinforced char layer. This study proposes a simple and precise method for controlling the structure of MOF-carbohydrate hybrids through competition between chemical reactions. [Display omitted] [ABSTRACT FROM AUTHOR]

Biological tissues possess high stretchability and nonlinear mechanical response owing to their wavy and crimped microstructure. The metamaterials exhibit unusual physical and mechanical properties, dominated by the configuration of artificial periodic microstructures. However, the development of bio-inspired designs and three-dimensional (3D) soft metamaterials that can achieve a variety of unusual mechanical properties remains challenging. Here, we reported a class of 3D horseshoe-shaped soft network metamaterials with tunable nonlinear mechanical response and tension-torsion coupling effect. Based on the 3D horseshoe-shaped microstructure and octahedron lattice arrangement, the designed 3D soft network metamaterials can exhibit J-shaped stress-strain curve and large angle of torsion with large levels of deformation under tension. The mechanical properties of the 3D soft network metamaterials in different characteristic directions are investigated, where a negative Poisson's ratio appears. Furthermore, the extended design that the 3D horseshoe-shaped microstructures are replaced by the semi-elliptic and quarter-arc-straight joint microstructures can tune a wide range of desired J-shaped stress-strain curves. Thus, the multi-unusual mechanical properties provide potential uses in multi-functional metamaterials and flexible bio-integrated devices. [ABSTRACT FROM AUTHOR]

[Display omitted] • A simple method for synthesizing nano-composite flame retardants with hollow prismatic structures. • POSS can be used as a structure-directing agent to maintain the nanostructure. • Cobalt-silicon synergy for enhanced flame retardancy and smoke suppression. • Nano-composite flame retardants achieve good dispersion in epoxy resin. Polyhedral oligomeric silsesquioxanes (POSS) containing transition metals have excellent flame retardant properties. However, the flame retardant obtained by direct coordination of transition metal cobalt (Co) with POSS tends to agglomerate, resulting in unsatisfactory flame retardant efficiency. Herein, a synthetic route was designed to use a water-soluble cobalt-polyvinylpyrrolidone (Co-PVP) complex as the template and cobalt source to first grow cobalt-metal–organic frameworks (Co-MOFs) on its surface, and then an aqueous solution of Na-POSS was used as both a structure-directing agent and an etching agent to load POSS and remove the template synchronously. Finally, a hollow prism with POSS wrapped around the Co-MOF (designated Co-MOF@POSS) was obtained. The hollow structure not only showed an optimized dispersion in the matrix but also exposed more active sites, catalyzing the formation of the carbon layer, and the silica ceramic layer formed by POSS further enhanced the strength of the carbon layer. An excellent flame retardant effect was obtained by adding Co-MOF@POSS to epoxy at 2 wt%. The heat release and smoke release of the epoxy composites were significantly reduced by the synergistic effect of cobalt and silicon, and the addition of Co-MOF@POSS improved the limiting oxygen index to 27.5% and decreased the peak of heat release rate, total heat release, and total smoke production by 34.6%, 16.6% and 23.8%, respectively. This work provides new ideas for the preparation of MOF and POSS-based flame retardants. [ABSTRACT FROM AUTHOR]

[Display omitted] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [ABSTRACT FROM AUTHOR]

By introducing the misclassification and delayed decision costs into the probabilistic approximations of the target, the model of decision-theoretic rough set is then sensitive to cost. However, traditional decision-theoretic rough set is proposed based on one and only one cost matrix, such model does not take the characteristics of multiplicity and variability of cost into consideration. To fill this gap, a multicost strategy is developed for decision-theoretic rough set. Firstly, from the viewpoint of the voting fusion mechanism, a parameterized decision-theoretic rough set is proposed. Secondly, based on the new model, the smallest possible cost and the largest possible cost are calculated in decision systems. Finally, both the decision-monotocity and cost criteria are introduced into the attribute reductions. The heuristic algorithm is used to compute decision-monotonicity reduct while the genetic algorithm is used to compute the smallest and the largest possible cost reducts. Experimental results on eight UCI data sets tell us: 1. compared with the raw data, decision-monotocity reduct can generate greater lower approximations and more decision rules; 2. the smallest possible cost reduct is much better than decision-monotocity reduct for obtaining smaller costs and more decision rules. This study suggests new research trends concerning decision-theoretic rough set theory. [ABSTRACT FROM AUTHOR]

• Causality models were applied to discover ecosystem causal networks. • Growing season temperature dominated changes in ecosystem structure and function. • Only weak causal links existed between ecosystem structure and function. • Relative causality-driven intensities were identified. The understanding of the interplay between the environment, ecosystem structure, and function is crucial for effectively managing ecosystems in the face of climate change and environmental disturbances. Numerous observations and simulation experiments relying on correlation analysis have yielded the conclusion that environmental factors can directly or indirectly affect ecosystem community structure and function. However, the limitations of traditional ecological approaches lie in their inadequate control over confounding factors, thus hindering our comprehensive understanding of the true causal relationships. Our study aims to address these limitations and obtain a more accurate picture of causality about environment, ecosystem structure and function within a long-term time series data of an ecosystem. Here, utilizing a 31-year dataset of typical temperate grassland ecosystems, we developed an advanced causal discovery model and further enhanced it to effectively identify the causal network within the ecosystem, while also quantifying the intensity of causal pathways connecting the environment, ecosystem structure, and function. We found that the primary driver for changes in ecosystem structure and function was the temperature during the growing season, with soil water content being the secondary factor. However, there was only a weak causal relationship between ecosystem structure and gross primary productivity (GPP), which was mediated by eurytopic xerophyte plants. These findings challenge the conventional notion that precipitation is a key driver of changes in ecosystem carbon fluxes, highlighting the importance of plant-available water in regulating ecosystem structure and function. Our results suggest that the effects of global warming and severe droughts on plant growth and carbon sequestration capacity necessitate significant attention and further mitigation measures in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new approach to knowledge acquisition in the incomplete decision system with preference-ordered domains of attributes. In such incomplete system, the concepts of ↑ and ↓ descriptors are proposed. Two types of certain rules, ↑ and ↓ “credible rules” are then generated by using the ↑ and ↓ descriptors. With introduction of the relative reducts of ↑ and ↓ descriptors into the incomplete decision system, ↑ and ↓ “optimal credible rules” are further proposed. The judgment theorems and discernibility functions associated with the relative reducts of ↑ and ↓ descriptors are also obtained, from which we can derive ↑ and ↓ “optimal credible rules” from the incomplete decision system. Some numerical examples are employed to substantiate the conceptual arguments. [Copyright &y& Elsevier]

• Precipitation was repackaged to simulate 4-level of precipitation variability. • Extra-extreme precipitation variability scenario potentially reduced N 2 O emissions. • Effects of precipitation variability on N 2 O fluxes had high interannual variations. • Soil, microbes, functional genes, and plants explained 61% variation in N 2 O fluxes. High temporal precipitation variability, characterized by less frequent but larger-magnitude precipitation events, is increasing. However, how precipitation variability affects N 2 O emissions and the underlying mechanisms remain unclear, especially at multiple-year scales. We conducted a 3-year manipulative experiment in which the same long-term mean growing season precipitation total was repackaged into events of inversely varying magnitude and frequency to simulate four levels of precipitation variability (extra-extreme, extreme, medium, and normal) in a semiarid grassland. Cumulative N 2 O emissions was the smallest under the extra-extreme precipitation variability scenario (6 very large rainfall events), 26% less than emission under the other three treatments (10, 16, and 24 rainfall events) over the three growing seasons. However, this difference was almost entirely due to three sampling events in the third year. Plant community (biomass and biodiversity), soil abiotic properties (water, dissolved organic carbon and pH), soil microbial biomass (carbon, nitrogen and the ratio), and soil functional genes (archaeal and bacterial amoA, nirS, nirK, narG , and nosZ) explained 61% of the variation in N 2 O emissions in response to the precipitation variability. Structural equation modelling indicated that the precipitation variability had direct positive effects, and indirect negative effects via soil abiotic properties, on soil functional genes that ultimately had positive effects on N 2 O emissions. The reduction in N 2 O emission in the extra-extreme precipitation variability scenario in the third year was due to low levels of soil water content, soil pH, aboveground biomass, and microbial biomass carbon simultaneously. Our results suggest that at the multi-year timescale, semiarid grasslands may have negative feedbacks to future precipitation regimes with higher variability. [Display omitted] [ABSTRACT FROM AUTHOR]

• Seasonal timing strongly regulates the response of N 2 O emissions to extreme droughts. • Early drought affected N 2 O emission with high interannual variability. • Middle drought consistently suppressed N 2 O emissions. • Late drought had little effect on N 2 O fluxes. Terrestrial ecosystems are important sources of nitrous oxide (N 2 O), a powerful greenhouse gas which can be strongly impacted by increasing droughts in association with climate change. However, detailed information on whether and how drought timing regulates N 2 O fluxes is still lacking. Here, we conducted a 3-year field experiment on a semiarid grassland in which extreme drought was imposed in either early-, mid-, or late-growing seasons repeatedly from 2014 to 2016. We found that early drought affected N 2 O emission with high interannual variability (increased, decreased and unchanged N 2 O emission in 2014, 2015, and 2016, respectively), coincident with changes in inorganic nitrogen (SIN), dissolve organic carbon (DOC), microbial biomass carbon (MBC), and soil functional genes (bacterial amoA , nirK , nirS , and nosZ). However, middle drought consistently suppressed N 2 O emissions due to simultaneous decreases in MBC, DOC and the abundances of archaeal amoA , nirK , and narG genes , causing the largest reduction in N 2 O emissions across the three years. In contrast, late drought had little effect on N 2 O fluxes, even though DOC and SIN decreased and the abundance of nirK , nirS , and nosZ increased. As a result, soil organic C and mineral N availability and functional gene abundances were not always robust factors for predicting N 2 O emissions under droughts across all treatments, except for abundance of AOA and nosZ. Our results highlight the vital role of seasonal timing in regulating the response of N 2 O emissions to extreme droughts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel semi-supervised learning framework for violence detection in video surveillance. With this framework, a classifier which distinguishes violent behavior from normal behavior can be trained using inexpensive unlabeled data with the assistance of human operators. Our approach can learn a single dictionary and a predictive linear classifier jointly. Specifically, we integrate the reconstruction error of labeled and unlabeled data, representation constraints and the coefficient incoherence into an objective function for dictionary learning, which enhances the representative and discriminative power of the established dictionary. This has contributed to that the dictionary and the classifier learned from the labeled set yield very small generalization error on unseen data. Experimental results on benchmark datasets have demonstrated the effectiveness of our approach in violence detection. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel global refined local binary pattern (GRLBP) by analyzing the nature of the distribution of pixel intensity in local neighborhoods. • GRLBP consists of two descriptors termed as magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M). • MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns. • CRLBP_M identifies local neighborhoods with gray differences by employing global central gray anchors to refine local magnitude patterns. • RLBP has obvious advantages in classification performance, computational complexity, and feature dimension. Local binary pattern (LBP) and its variants have been successfully applied in texture feature extraction. However, it is hard for most LBP-based methods to effectively describe and distinguish the local neighborhoods with similar structures (that is, the calculated feature patterns are identical) but different contrasts or grayscales. To alleviate such problems, we propose a novel global refined local binary pattern (GRLBP) by analyzing the nature of pixel intensity distribution in local neighborhoods. GRLBP consists of two descriptors called magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M). MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns. And CRLBP_M identifies local neighborhoods with grayscale differences by employing global central grayscale anchors to refine local magnitude patterns. Finally, frequency histograms of MRLBP_S and CRLBP_M from each image are cascaded to generate the GRLBP. Extensive experimental results on seven benchmark texture databases: Outex, CUReT, KTH-TIPS, UMD, UIUC, KTH-T2b, and DTD demonstrate that the proposed GRLBP can represent the detailed information of texture images. Furthermore, compared with state-of-the-art LBP variants, GRLBP has competitive advantages in classification accuracy, feature dimension, and computational complexity, respectively. [ABSTRACT FROM AUTHOR]

• N 2 O flux is sensitive and insensitive to drought and heat wave, respectively. • Soil water content is a first-order predictor of seasonal variation of N 2 O flux. • Inter-annual variations of N 2 O flux are impacted by precipitation distribution. • Archaeal amoA and nirK mainly regulates climate extremes effects on N 2 O flux. Nitrous oxide (N 2 O), the third most important greenhouse gas, contributes to the increasing frequency and severity of climate extremes. Disentangling feedbacks of climate extremes on terrestrial N 2 O emission is important for forecasting future climate changes. Here, we experimentally imposed extreme drought and heat wave events during three years in a semiarid grassland to investigate the responses of N 2 O flux. We identified that N 2 O flux suppression during droughts was mediated by soil water content (SWC), microbial biomass carbon (MBC), soil inorganic nitrogen (SIN) and dissolved organic carbon (DOC) contents, and the abundance of archaeal amoA, nirK, and narG. However, bacterial amoA, nirS, and nosZ remained stable. Upon rewetting following droughts, the SWC, SIN, DOC, archaeal amoA, nirK, narG, and resultant N 2 O fluxes recovered to the magnitude of the ambient control. In contrast, heat waves alone or in combination with drought did not impact N 2 O fluxes or the underlying physical, chemical and microbial states. Stepwise multiple linear regression suggested that SWC, DOC, and MBC were the key factors regulating immediate responses of N 2 O flux to climate extremes while the major factors regulating seasonal mean N 2 O flux in response to climate extremes were archaeal amoA abundance, nirK abundance, and MBC. Our results suggest that N 2 O fluxes were sensitive to droughts but insensitive to heat waves. Soil moisture induced changes in substrate availability, and the community size of total and functional microorganisms in soil jointly regulated N 2 O responses to climate extremes. The relative importance of regulating factors shifted at different timescales. [ABSTRACT FROM AUTHOR]

Increasing heavy rainfalls can strongly affect ecosystem nitrogen (N) cycling processes and thereby alter soil N fluxes and pools. However, the effects of heavy rainfalls on soil N fluxes and pools are poorly understood, particularly with regards to high rainfall timing under field conditions. We conducted a 3-year (2014–2016) manipulative experiment in which heavy rainfall was imposed in middle (plant peak growing stage) or late (plant senescent growth stage) growing season in a semiarid grassland of Inner Mongolia, China to explore the responses of N 2 O fluxes and soil total N contents and the underlying microbial mechanisms. Mid-season heavy rainfall promoted soil N 2 O emissions by 65% on average across the three years, attributable mainly to increases in denitrifying nirK and nirS abundances induced large denitrification at higher soil water contents. However, archaeal and bacterial amoA and narG genes did not change significantly due probably to counteracting effects of increased soil water content (positive) and soil pH (negative). Mid-season heavy rainfall led to 17% reduction in soil total N by the end of the last year of the study (2016), partly due to the enhanced accumulated N 2 O emissions over the three years. In contrast, late-season heavy rainfall did not change N 2 O emissions and soil total N contents even though soil water content, soil pH and nirK and nirS abundance were significantly increased, perhaps due to limitation by low temperature. Timing of the heavy rainfall events during the plant growing season strongly influenced their impacts on soil N fluxes and pools and heavy rainfalls in the peak stage of plant growth may potentially cause a positive feedback to global warming and exacerbate N limitation in terrestrial ecosystems. • N 2 O flux and soil total N responses to heavy rainfall depended on seasonal timing. • Mid-season heavy rainfall increased N 2 O flux by 65% due to higher denitrification. • Enhanced N 2 O emissions coincided with 17% of reduction in soil total N. • Late-season heavy rainfall did not change N 2 O emissions and soil total N contents. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic updating of the rough approximations is a critical factor for the success of the rough set theory since data is growing at an unprecedented rate in the information-explosion era. Though many updating schemes have been proposed to study such problem, few of them were carried out in a multigranulation environment. To fill such gap, the updating of the multigranulation rough approximations is firstly explored in this paper. Both naive and fast algorithms are presented for updating the multigranulation rough approximations with the increasing of the granular structures. Different from the naive algorithm, the fast algorithm is designed based on the monotonic property of the multigranulation rough approximations. Experiments on six microarray data sets show us that the fast algorithm can effectively reduce the computational time in comparison with the naive algorithm when facing high dimensional data sets. Moreover, it is also shown that fast algorithm is useful in decreasing the computational time of finding both traditional reduct and attribute clustering based reduct. [Copyright &y& Elsevier]

Abstract: This paper describes pilot test results obtained on cooling tower blowdown of a power plant in HeBei province, using both 0.1 μm MF and 0.03 μm UF, which are outside-in hollow fiber membrane modules. In this pilot, the MF and UF modules were installed at the same system, although at different testing periods. Operation parameters such as flux, backwash (BW) time, BW frequency, frequency of air backwash (ABW) were changed; the MF and UF modules were operated at constant operating conditions during the same testing period. The study showed that both the MF and the UF membrane systems can produce permeate with high and consistent quality. The filtrate SDI15 of the permeate produced by both systems was below 3 for most of the time (except the period of flocculation addition). This water quality meets the requirements of the reverse osmosis (RO) system. In-line rapid pre-coagulation using polyaluminium chloride (PAC) at a concentration of 2.5–5 ppm did not decrease the filtrate silt density index (SDI15) and may have even led to a sharp increase in filtrate SDI15, especially when combined with polyacrylamide (PAM). In addition, the injection of polymerized ferric sulfate (PFS) and sodium hypochlorite (NaClO) to the pretreatment system feed water had little influence on the filtrate SDI15. Other water quality parameters such as total Fe, Cu, COD and colloidal Si were also measured. The UF membrane module reached a higher level of FPI than the MF membrane module did, while the latter had more stable performance than the former. The MF/UF test has achieved stable membrane permeability, and both MF and UF modules were found suitable to provide adequate pretreatment prior to RO desalination. [Copyright &y& Elsevier]

New under bump metallization (UBM) is required for future microelectronics to minimize reliability concerns with solder interconnects. In this study, electroless FeP film was prepared as UBM. Solderability of electroless NiP, electrodeposite Fe and electroless FeP films with eutectic SnAgCu solder was investigated by static wetting tests and wetting balance measurements. Results revealed that the solderability of NiP film has almost no change with thermal aged for the film in air atmosphere increasing. The contact angles for FeP and Fe films with SnAgCu solder increased and the maximum wetting force decreased with heating time increasing from 0 h, 48 h, 89 h–160 h at 180 °C. But the solderability of FeP film was found to be better than those of NiP and Fe whether the films in base or thermal aged at 180 °C for 89 h. Even after thermal aged in air atmosphere at 180 °C for 160 h, the FeP also performed the smallest contace angle compared with the NiP and the Fe. Oxide layer on the surface of FeP was much easier to be removed by rosin mildly activated flux during reflow process because loose structure. It was proved to be the key for the highly solderability of electroless FeP. Image 1 • Solderability of electroless NiP has almost no change with oxide time increasing from 0 hour to 160 hours at 180 oC in air. • Solderability of electroless FeP and electrodeposite Fe films reduced with the oxide time increasing. • Solderability of FeP was found to be better than those of NiP and Fe whether in base or oxidation at 180 oC for 160 h. • Difference of oxide layer was proved to be the key for highly solderability of electroless FeP. [ABSTRACT FROM AUTHOR]