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Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor $l_{2,2,1}$ norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor $l_{2,2,1}$ norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC). [ABSTRACT FROM AUTHOR]

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue 'Towards symbiotic autonomous systems'. [ABSTRACT FROM AUTHOR]

• This paper proposes a new low-rank kernel regression method with preserved locality for multi-class analysis. • In this method, the regression analysis is conducted based on the low-rank components of a non-linear random feature space with preserved locality. • The impacts of the outliers hidden in the low-rank components are relieved by the l2,p norm. • The proposed method may be the first regression method that simultaneously has the abilities of non-linear analysis, redundancy reduction, and locality preservation. Kernel ridge regression (KRR) is a kind of efficient supervised algorithm for multi-class analysis. However, limited by the implicit kernel space, current KRR methods have weak abilities to deal with redundant features and hidden local structures. Thus, they may get indifferent results when applied to analyze the data with complicated components. To overcome this weakness and obtain better multi-class regression performance, we propose a new method named low-rank kernel regression with preserved locality (RLRKRR). In this method, data are mapped into an explicit feature space by using the random Fourier feature technique to discover the non-linear relationship between data samples. In addition, during the training of the regression coefficient matrix, the low-rank components of this explicit feature space are simultaneously extracted for reducing the effect of the redundancy. Moreover, the graph regularization is performed on the extracted low-rank components to preserve local structures. Furthermore, the l 2 , p norm is imposed on the regression error term for relieving the impact of outliers. Based on these strategies, RLRKRR is capable to achieve rewarding results in complicated multi-class data analysis. In the comprehensive experiments conducted on various types of datasets, RLRKRR outperforms several state-of-the-art regression methods in terms of classification accuracy (CA). [ABSTRACT FROM AUTHOR]

Big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive -dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering. [ABSTRACT FROM AUTHOR]

Abstract: One of the fundamental human cognitive processes is problem solving. As a higher-layer cognitive process, problem solving interacts with many other cognitive processes such as abstraction, searching, learning, decision making, inference, analysis, and synthesis on the basis of internal knowledge representation by the object–attribute-relation (OAR) model. Problem solving is a cognitive process of the brain that searches a solution for a given problem or finds a path to reach a given goal. When a problem object is identified, problem solving can be perceived as a search process in the memory space for finding a relationship between a set of solution goals and a set of alternative paths. This paper presents both a cognitive model and a mathematical model of the problem solving process. The cognitive structures of the brain and the mechanisms of internal knowledge representation behind the cognitive process of problem solving are explained. The cognitive process is formally described using real-time process algebra (RTPA) and concept algebra. This work is a part of the cognitive computing project that designed to reveal and simulate the fundamental mechanisms and processes of the brain according to Wang’s layered reference model of the brain (LRMB), which is expected to lead to the development of future generation methodologies for cognitive computing and novel cognitive computers that are capable of think, learn, and perceive. [Copyright &y& Elsevier]

Abstract: Background: Screws with strong pull-out strength have been sought for the treatment of cancellous bone. We hypothesized that an obliquely angled screw thread has advantages over conventional vertical thread with a minimal proximal half angle. Methods: Metal and bone screws were made of stainless steel and porcine cortical bone. Their proximal half angle was set at 0°, 30°, or 60°. The screws were inserted into porcine cancellous bone. At 0°, the thread faced the recipient bone vertically. Pullout tests at a rate of 30mm/min (n =40, each screw type) and microcomputed tomography (n =6) were conducted. Findings: The pull-out strength of the screws was maximal at 30°; 348.8 (SD, 44.1)N with metal and 326.6 (39.4)N with bone. It was intermediate at 0°; 301.9 (35.9)N with metal and 278.2 (30.6)N with bone. It was minimal at 60°; 126.5 (39.0)N with metal and 174.8 (29.7)N with bone. Cancellous bone was damaged between the threads at 30°, while intact cancellous bone was preserved between the threads at 0°. Interpretation: A proximal half angle of around 30° is appropriate because the pullout force is applied to the recipient bone evenly. Commercial cancellous screws can be improved by changing the thread shape to minimize the damage to recipient bone. [Copyright &y& Elsevier]

Object-Oriented Information System (OOIS) is an information system that employs object-oriented technologies in system design and implementation. Recent research advances and industrial innovations in distributed system modeling and Internet applications have enabled OOIS design and implementation to be carried out on the basis of new technologies and platforms. This special section onModeling Object-Oriented Information Systemspresents readers with a set of best papers selected from the 7th International Conference on OOIS. Reviews of theories and applications of OOIS’s are also provided for predicating trends in OOIS modeling. [ABSTRACT FROM AUTHOR]

Schwertmannite (Sch) is known to be an effective scavenger of arsenic (As) due to its strong binding affinity for toxic As species. However, the evolution of As-bearing schwertmannite under extreme pH conditions is poorly understood. In this study, we investigated the effects of extremely acidic and alkaline conditions on the stability of schwertmannite with structurally incorporated As(V) (CO-Sch) and schwertmannite with adsorbed As(V) (AD-Sch). The results show that both extremely acidic and alkaline conditions have significant effects on the evolution of minerals and liberation of iron and sulfate. At extremely acidic pH, the maximal release of ferric iron (Fe(III)) and sulfate from CO-Sch were greater than that from AD-Sch, whereas 6.2% and 0.3% of total As released from AD-Sch and CO-Sch, respectively. At extremely alkaline pH, aqueous Fe(III) was not observed, and Fe(III) was retained in As-bearing schwertmannite due to the chemical equilibrium between the dissolution of schwertmannite and re-precipitation of goethite; structurally incorporated As(V) promoted the liberation of sulfate. In addition, the adsorbed As on schwertmannite is more stable, which led to a minor release of As (0.8%) over a 30-d period, however, the liberated As(V) from CO-Sch accounts for up to 3.2%. Under extremely acidic and alkaline conditions, portions of AD-Sch and CO-Sch transformed from schwertmannite to goethite after 30 d, while schwertmannite was still the dominant mineral. Adsorbed As(V) inhibited the transformation of As-bearing schwertmannite to goethite more significantly than structurally incorporated As(V). • Extreme acidic pH favored the transformation of As-bearing schwertmannites. • pH and As(V) speciation influenced the release of iron and sulfate significantly. • As(V) speciation affected differently the mobility of As at extreme pH conditions. [ABSTRACT FROM AUTHOR]

Objectives. To study the resorption of the herniated lumbar disc (RHLD) and its mechanism in the SD rats of lumbar intervertebral disc herniation treated with Hui medicine moxibustion (HMM). Methods. Forty SD rats were randomly divided into four groups, normal group, lumbar disc herniation (LDH) group, HMM group, and antagonist (HMM+Met12) group, with 10 rats in each group. The rat model of LDH was prepared with the method of lumbar epidural emplacement of the caudal intervertebral disc. In the HMM group and HMM+Met12 groups, 4 weeks after modeling, HMM therapy was performed in the lumbar spine for 3 months with 1 time per day and 20 min each time, the samples were collected 8 weeks after the treatment. The histological degeneration was observed through HE staining, and the neovascularization of intervertebral disc tissues was detected by the expression of CD34 and vascular endothelial growth factor (VEGF). The apoptosis of nucleus pulpous cells was detected by TUNEL assay, and the activity of caspase-3, -8, and -9 and extracellular matrix enzymes was detected by western blotting. Results. HMM treatment significantly improved the behavioral ability of rats with LDH surgery. The morphological structure was obviously destroyed in the LDH group, but disc structure was significantly repaired in the HMM group, and mild structure alterations were observed in the HMM+Met12 group. Higher levels of CD34 and VEGF were detected in the HMM group indicating that neovascularization is formed. The expression level of FasL was significantly increased in the HMM group. The protein expression levels of cleaved-caspase-3, cleaved-caspase-8, and cleaved-caspase-9 in nucleus pulposus (NP) tissues were also elevated when treated with HMM, and the TUNEL staining showed the same results. The protein expression levels of matrix metalloproteinases- (MMP-) 1, MMP-2, MMP-3, MMP-13, and ADAMTS-4 were markedly promoted in the HMM group. Met12, a small peptide antagonist of FasL, significantly reduced the effects of HMM. Conclusion. HMM can promote the formation of neovascularization of lumbar intervertebral disc, support the apoptosis of NP cells through Fas/FasL signaling, and regulate the degradation of extracellular matrix enzyme, which then accelerates the absorption of lumbar intervertebral disc herniation and the recovery of motor function in rats. [ABSTRACT FROM AUTHOR]

Image clustering is a difficult task with important application value in computer vision. The key to this task is the quality of images features. Most of current clustering methods encounter the challenge. That is, the process of feature learning and clustering operates independently. To address this problem, several researchers have been dedicated to performing feature learning and deep clustering together. However, the obtained features lack discriminability to address high-dimensional data successfully. To deal with this issue, we propose a novel model named as robust deep fuzzy K -means clustering (RD-FKC), which efficiently projects image samples into a representative embedding space and precisely learns membership degrees into a combined framework. Specifically, RD-FKC introduces Laplacian regularization technique to preserve locality properties of data. Moreover, by using an adaptive loss function, the model becomes more robust to diverse types of outliers. Furthermore, to avoid the latent space being distorted and make the extracted features retain the original information as much as possible, the model introduces reconstruction error and adds regularization to network parameters. Finally, an effective algorithm is derived to solve the optimization model. Numerous experiments have been conducted, illustrating the advantages and superiority of RD-FKC over existing clustering approaches. • Embedding clustering into deep convolutional autoencoder to learn compact and representative features. • Designing the Laplacian regularization to constrain the membership matrix for locality preserving. • Introducing the adaptive loss function to enhance the clustering robustness. • Presenting an effective algorithm to optimize the proposed model. [ABSTRACT FROM AUTHOR]

Bone screws have a mechanical advantage over bone pegs for fixation of osteochondral fractures. The purpose of this study was to investigate predrilling methods to facilitate bone screw insertion. The 162 bone screws (major diameter 3.0 mm; minor diameter 2.5 mm; length 10 mm) used were made from porcine tibial cortical bone. Metal screws of the same design were used as a control. The screws were inserted, without tapping, into predrilled holes of six different sizes (2.4–2.9 mm diameter; 80%–97% of the screw) made in cancellous bone harvested from porcine femoral condyle, and pullout strength was determined. The volumetric bone mineral densities of porcine bones were compared to those of human bones. In a clinical case, an osteochondral fracture of the patella was repaired using three autologous bone screws. Bone screws occasionally broke due to high insertion torque (108–158 N·mm); the incidences were 30% and 19% with the 80% and 83% sized holes, respectively. None of the screws inserted into holes of size ≥ 87% broke, and their insertion torques never exceeded 100 N·mm. Metal screws were inserted without breakage regardless of hole size. Bone screws inserted into 80%-93% holes had the greatest pullout strengths (range 154–165 N), whereas 97% holes had a mean strength of 84 N. Metal screws had large pullout strengths when inserted into 80% and 83% holes (164 and 169 N, respectively). The mineral density of porcine bone was lower than that of human bone (1167 and 1357 mg/cm3 for cortical bone and 193 and 269 mg/cm3 for cancellous bone, respectively). In the clinical case, bone screws were inserted smoothly into 90% holes. The optimal size of predrilled holes for bone screws (87%–93%) differs from that for metal screws. [ABSTRACT FROM AUTHOR]

In image set classification, affine hull based models have achieved good performance. However, these models only focus on holistic images and ignore the local information of images. To incorporate local information, this paper develops a cooperative operator and proposes a cooperative linear regression model (CLRM) for image set classification. Specifically, CLRM first proposes a block partitioning strategy to obtain local information. Image sets and local information are both modeled as affine hulls. Then, a linear regression model embedded with cooperative operator is proposed to incorporate different information and compute the distance between the different affine hulls. Finally, a combined distance measure is proposed to incorporate different information for classification. Extensive experiments on different recognition tasks such as face recognition on image/video based databases and object classification show that the proposed cooperative operator can improve the performance of the model and the proposed CLRM achieves competitive performance compared with the state-of-the-art classifiers. • Proposing a block partitioning strategy to extract local information. • Modeling local information as affine hull for image set classification. • Developing a cooperative operator to integrate different information as a whole. • Proposing a linear regression model embedded with the cooperative operator. • Proposing an efficient optimization algorithm to solve the model. [ABSTRACT FROM AUTHOR]

Abstract Hexavalent chromium (Cr(VI)) is one of prevalent toxic and mobile heavy metal contaminants in the environment. In this study, synthetic iron sulfide nanoparticles (FeS NPs) stabilized with carboxymethyl cellulose (CMC) were applied to remediate Cr(VI) contaminated groundwater and saturated soil. The batch test results showed that aqueous Cr(VI) was removed with a capacity as high as 1046.1 mg Cr(VI) per gram of FeS NPs. The removal of aqueous Cr(VI) mainly involves adsorption, reduction and co-precipitation. Aqueous Cr(VI) removal by using FeS NPs was a strong pH-dependent process. Dissolved oxygen (DO) would compete with Cr(VI) for Fe(II) and S(-II) and inhibit the process of Cr(VI) reduction at pH 5.6. For ionic strength and natural organic matter (NOM), there were no significant influences on the aqueous Cr(VI) removal. Column tests demonstrated that the concentrations of Cr(VI) in the effluent were lower than 0.005 mg L−1 after an elution of 45 pore volumes (PVs) of stabilized FeS NPs suspension. The leached Cr(VI) decreased from 4.58 mg L−1 of raw Cr(VI)-contaminated soil to 46.8–80.7 μg L−1 from the surface to bottom treated soil in column through Toxicity Characteristic Leaching Procedure (TCLP). Therefore, the synthesized FeS NPs hold high potential for the in-situ remediation of Cr(VI)-contaminated groundwater and saturated soil. Graphical abstract Image 1 Highlights • Synthetic FeS NPs effectively immobilized Cr(VI) in groundwater and saturated soil. • Aqueous Cr(VI) removal by FeS NPs was significantly dependent on pH. • FeS NPs removed aqueous Cr(VI) through adsorption, reduction and co-precipitation. • In-situ remediation of Cr(VI) contaminated soil by FeS NPs soliquid was simulated. [ABSTRACT FROM AUTHOR]

Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases. [ABSTRACT FROM AUTHOR]

Intestinal mucosal barrier damage is an important mechanism for the development of sepsis and multiple organ dysfunction syndrome. At present, there are no satisfactory and effective methods for the protection of the intestinal mucosal barrier. Jinzhi, the first fecal microbiota transplantation worldwide, is often used to treat critically ill patients; however, the specific mechanism involved in this process remains unclear. The aim of this study was to investigate the therapeutic effect and mechanism of Jinzhi intervention on mice with sepsis induced through treatment with lipopolysaccharide (LPS). Mice were intraperitoneally injected with LPS to simulate intestinal mucosal barrier function damage in sepsis; intervention was performed through the oral administration of Jinzhi. The effect of Jinzhi on LPS-induced sepsis was analyzed by comparing the vital signs and survival rate of mice under different treatments. Pathological staining and enzyme-linked immunosorbent assay were used to identify the effects of LPS or treatment with Jinzhi on the intestinal mucosal barrier in mice. The effect of LPS or treatment with Jinzhi on the intestinal flora was analyzed via 16S rRNA gene sequencing of ileal contents. Immunohistochemistry and enzyme-linked immunosorbent assay showed that treatment with LPS increased levels of inflammatory factors (interleukin-1α, interleukin-6, tumor necrosis factor-α), caspase-3, and caspase-8 in the serum and ileum, and destroyed the tight junction between epithelial cells. Intervention with Jinzhi reduced levels of serum LPS and tumor necrosis factor-α, and repaired the tight junction between epithelial cells. Furthermore, 16S rRNA gene sequencing analysis showed that treatment with Jinzhi improved the diversity and physiological function of the intestinal flora. These results suggest that Jinzhi may be a promising option for the treatment of sepsis caused by LPS, and emphasize that Jinzhi exerts a recovery effect on the imbalance of intestinal flora. [ABSTRACT FROM AUTHOR]

Accurate, rapid and non-destructive disease identification in the early stage of infection is essential to ensure the safe and efficient production of greenhouse cucumbers. Nevertheless, the effectiveness of most existing methods relies on the disease already exhibiting obvious symptoms in the middle to late stages of infection. Therefore, this paper presents an early identification method for cucumber diseases based on the techniques of hyperspectral imaging and machine learning, which consists of two procedures. First, reconstruction fidelity terms and graph constraints are constructed based on the decision criterion of the collaborative representation classifier and the desired spatial distribution of spectral curves (391 to 1044 nm) respectively. The former constrains the same-class and different-class reconstruction residuals while the latter constrains the weighted distances between spectral curves. They are further fused to steer the design of an offline algorithm. The algorithm aims to train a linear discriminative projection to transform the original spectral curves into a low dimensional space, where the projected spectral curves of different diseases own better separation trends. Then, the collaborative representation classifier is utilized to achieve online early diagnosis. Five experiments were performed on the hyperspectral data collected in the early infection stage of cucumber anthracnose and Corynespora cassiicola diseases. Experimental results demonstrated that the proposed method was feasible and effective, providing a maximal identification accuracy of 98.2% and an average online identification time of 0.65 ms. The proposed method has a promising future in practical production due to its high diagnostic accuracy and short diagnosis time. [ABSTRACT FROM AUTHOR]