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This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.

Multi-Label Learning (MLL) is a classification task in which each instance may be associated with two or more class labels simultaneously. With the arrival of information explosion era, MLL is confronted with the curse of high-dimensional data. Feature selection is an effective technique that has been used to reduce dimensionality. Previous Multi-Label Feature Selection (MLFS) approaches only lay emphasis on the fitting relations between the feature space and the corresponding label space while neglecting the instance correlations. To remedy the deficiency, we present a novel MLFS method by Exploring Instance Correlations with Local Discriminant model (EICLD). More specifically, we first construct a local set for each instance, and use a local discriminant model for each local set to explore instance correlations. Then, we further integrate the local models of all instances to guarantee the global instance correlations. Finally, l2,1-norm is introduced into loss function and the regularization of feature weight matrix respectively to facilitate feature selection process. An optimization algorithm is designed for handling the proposed objective function. The paper is compared with seven representative approaches on twelve data sets. The results validate the superiority of the proposed approach.

The Laplacian eigenmaps (LE) is one of the most commonly used nonlinear dimensionality reduction methods and aims to find a low-dimensional representation to preserve the topological relationship between sample points in the original data. However, the l2-norm based loss function makes LE unable to preserve the relationship in many cases. Additionally, the topological relationship does not represent the real intrinsic structure of data. For example, the overemphasis of the topological relationship by LE easily breaks the manifold structure into multiple local areas in the embedding space, which makes the spectral clustering analysis of multi-manifold data more difficult to carry out. To solve this problem, we propose the soft adaptive loss based LE (SALE). With the soft adaptive loss, SALE can adaptively emphasize the topological relationship between sample points and the clustering structure of data. The model is tested and validated on UCI, face and gene expression data sets, and compared with some state-of-the-art models. The experimental results show that the method is robust to noise.

Cyclophosphamide (CTX) is a widely used anticancer drug that can cause liver injury, but there is no effective treatment available at present. The antioxidant properties of Lactobacillus plantarum Lp2 in vitro and its effect on CTX-induced liver injury in mice were investigated thoroughly. The order of antioxidant capacity of the fermentate of Lp2 was as followed: fermented supernatant cell-free extract intact cell. BALB/c mice were intraperitoneally injected with 80 mg/kg BW/d CTX for 3 days to build a liver injury model, then treated with Lp2 fermented supernatant (Lp2-s) and Lp2 culture broth (Lp2). After 10 days, the indicators of oxidative stress and liver injury were measured. Both Lp2-s and Lp2 restored the levels of T-SOD, CAT, GSH-Px, MDA, GSH, ALT, and AST. The western blotting results showed that Lp2-s and Lp2 ameliorated CTX-induced oxidative damage and hepatocyte apoptosis via inhibiting MAPKs pathway and strengthening Nrf2/HO-1/NQO1 antioxidant defense system, thus inhibiting the mitochondrial-mediated apoptosis pathway. Therefore, both Lp2-s and Lp2 had similar protective effects on CTX-induced liver injury.

Review question / Objective: To study on the efficacy and safety of Kunxian Capsule in treating patients with lupus nephritis( LN). Condition being studied: The study showed that Kunxian capsule significantly improved immune status and renal function and suppressed IL-10 and TNF in serum of lupus mice- α Of secretion, works better in combination with and reduces hormone dosage. At present, there are few studies in which Kunxian capsules are used in the clinic, and the conclusions of the studies are inconsistent, so this study was conducted in order to comprehensively evaluate the efficacy and safety of Kunxian capsules for the treatment of lupus nephritis.

In the literature, a lot of work focused on studying intermittent control problems via feedback control strategy. No study on the intermittent control problems via sliding mode control method has been reported so far. This paper studies the problem of fast synchronization between two complex dynamical networks via aperiodically intermittent control and sliding mode control. In order to achieve fast synchronization of complex dynamical networks by using aperiodically intermittent sliding mode controller, new differential inequalities are derived firstly. After that, some sufficient finite-time synchronization criteria and finite-time achieving slide mode surface are obtained based on finite-time stability theory, aperiodically intermittent sliding mode control technique and constructing Lyapunov function. Finally, an example is provided to verify the effectiveness of the proposed theoretical methods.

Determining a systematic whole-building retrofit plan for envelope components and indoor appliances to achieve targets such as cost savings and policy compliance is a challenging task. To be specific, the systematic whole-building retrofit problem, when solved by an optimization approach, is highly complicated. It is sometimes even impossible to find a solution with given computation resources and algorithms. In addition, a costly comprehensive bottom-up audit is required to establish the parameters of the problem. This study presents two models to reduce the complexity of the systematic whole-building retrofit optimization problem. Firstly, the proposed models use the grouping concept to optimize the retrofit of subsystems in a building instead of individual components/appliances, which reduces the dimension of the problem effectively. Secondly, the models use so-called ‘notch test’ data, which are sampled and verified savings of an intervention, to eliminate the need for bottom-up energy audits. This further simplifies the retrofit optimization problem and reduces the retrofit cost. The models are based on our previous work and aim at energy savings maximization and payback period minimization, considering the green building policy and tax incentive initiatives. A case study shows that about 2530 MWh energy savings and an A rating from the energy performance certificate standard can be obtained with a payback period of 59 months, which verifies the feasibility and effectiveness of the models proposed.

The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%.

Retrofitting existing buildings with energy-efficient facilities is an effective method to improve their energy efficiency, especially for old buildings. A multi-objective optimization model for building envelope retrofitting is presented. Envelope components including windows, external walls and roofs are considered to be retrofitted. Installation of a rooftop solar panel system is also taken into consideration in this study. Rooftop solar panels are modeled with their degradation and a maintenance scheme is studied for sustainability of energy and its long-term effect on the retrofitting plan. The purpose is to make the best use of financial investment to maximize energy savings and economic benefits. In particular, net present value, the payback period and energy savings are taken as the main performance indicators of the retrofitting plan. The multi-objective optimization problem is formulated as a non-linear integer programming problem and solved by a weighted sum method. Results of applying the designed retrofitting plan to a 50-year-old building consisting of 66 apartments demonstrated the effectiveness of the proposed model.

Electrical dispersion of a water-in-oil emulsion under an electric field has an inhibitory effect on the eletrocoalescence. To clarify the mechanism of droplet breakup at the microscopic level, molecular dynamics simulation was performed to study the deformation and breakup characteristics of a moving droplet at different field strengths. The results showed that interparticle spacings increased with the growth of the field strength, leading to significantly higher values of the electrostatic potential and van der Waals potential, and hence an increase of the deformation degree. There was a substantial rise in the non-bonded potential energy with a sharp fall in the overall velocity of the droplet when the electric field was higher than 2.1 V nm−1, causing the development of droplet breakup. Through hydrogen bond and hydration analysis, it was found that the hydrogen bond lifetime and ion distribution were responsible for the formation of different breakup patterns. These results demonstrated that droplet break-up is highly dependent on weak intermolecular interactions such as strong attraction.

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently.

The building sector is a large consumer of the world's energy and is responsible for 40% of the European Union's (EU) total final energy consumption. The most effective and efficient method to reduce energy consumption of buildings is to retrofit existing buildings with more energy efficient facilities. This paper presents a multi-objective optimization model for building envelope retrofit planning which aims at maximizing energy savings and economic benefits with given investment budget. It also takes thermal comfort and life-cycle impact into consideration. Results from application to the retrofit planning of a 70 years old family house show that energy consumption can be reduced effectively by the presented model.

Raw data of point cloud is often noisy and with\ud topological defects (such as holes), which cause problems including faulty connection and inaccurate structure. As\ud a result, the surface reconstruction of point cloud data is\ud a highly challenging problem. This work proposes a novel\ud method, which improves the surface quality compared with\ud existing methods. Our method combines both the local detailed\ud features and the global topological information during the\ud reconstruction process. To facilitate the feature refinement,\ud we first pre-process the point cloud data by relocating each\ud point, upsampling the point data, and optimizing normals to\ud enhance the features and geometric details. We then identify\ud the topological information by segmenting the geometry and\ud constructing curve skeletons for each part and guide the surface reconstruction with the skeletons by minimal user interaction. We demonstrate the effectiveness of our methods with various examples, where our reconstruction can fill out missing data and preserve sharp features.

BACKGROUND This systematic review aims to evaluate the efficacy of Gegen Qinlian Decoction (GQD) for ulcerative colitis (UC). METHODS PubMed, EMBASE, Springer LINK, Cochrane Library, the China National Knowledge Infrastructure, Chongqing Weipu Database for Chinese Technical Periodicals, Wan-fang Database, and Chinese Biomedicine Database were searched from their inception to December 2018 for randomized controlled trials comparing the use of GQD alone or in combination with western medicine (WM) with that of WM therapies for UC. Outcomes on the therapy's effectiveness rate, ulcerative colitis endoscopic index of severity (UCEIS), recurrence rate, and adverse events were extracted and analyzed by Review Manager 5.3 software. Meta-analysis was combined with fixed or random-effects model, and risk ratios (RR) and 95% confidence intervals (CI) were calculated for all outcomes. Two researchers independently reviewed each trial to determine its inclusion. The Cochrane risk of bias assessment tool was used for quality assessment. RESULTS We included 22 trials involving 2028 patients with UC. When compared with WM therapy, GQD significantly improved the clinical effectiveness (n = 591, RR = 1.21, 95% CI: 1.12-1.31, P

Phosphorylation is an important part of post-translational modifications of proteins, and is essential for many biological activities. Phosphorylation and dephosphorylation can regulate signal transduction, gene expression, and cell cycle regulation in many cellular processes. Phosphorylation is extremely important for both basic research and drug discovery to rapidly and correctly identify the attributes of a new protein phosphorylation sites. Moreover, abnormal phosphorylation can be used as a key medical feature related to a disease in some cases. The using of computational methods could improve the accuracy of detection of phosphorylation sites, which can provide predictive guidance for the prevention of the occurrence and/or the best course of treatment for certain diseases. Furthermore, this approach can effectively reduce the costs of biological experiments. In this study, a flexible neural tree (FNT), particle swarm optimization, and support vector machine algorithms were used to classify data with secondary encoding according to the physical and chemical properties of amino acids for feature extraction. Comparison of the classification results obtained from the three classifiers showed that the classification of the FNT was the best. The three classifiers were then integrated in the form of a minority subordinate to the majority vote to obtain the results. The performance of the integrated model showed improvement in sensitivity (87.41%), specificity (87.60%), and accuracy (87.50%).

As a mechanism compensating for obstructive coronary artery disease, coronary collateral circulation (CCC) has attracted cardiologists for a long time to explore its potential impact. In the present study, Chinese patients suffering from ≥95% coronary stenosis, as diagnosed by angiography, have been investigated for the correlation between CCC and lipoprotein(a) [Lp(a)] levels. A cohort of 654 patients was divided into four categories according to Rentrop grades 0, 1, 2, and 3. Lp(a) levels were divided into model 1, discretized with critical values of 33 and 66%, and model 2, discretized with a cutoff value of 30.0 mg/dL. Furthermore, we evaluated the correlation between CCC and serum Lp(a) levels. The four groups had significantly different Lp(a) levels (25.80±24.72, 18.99±17.83, 15.39±15.80, and 8.40±7.75 mg/dL; P30.0 group (OR=6.77, 95%CI=4.44-10.4) was greater than that of Lp(a)

This paper presents a novel approach for bas-relief generation and synthesis. In contrast to previous methods, we divide this problem into two parts: the selection of the best view and arrangement of the relief layout. Taking these into account, we incorporate the visual saliency and photographic composition rules into the bas-relief generation. Additionally, a nonlinear compression function is used to compress the models, and finally, we implement surface parameterization by directly manipulating the mesh triangles to generate curved surface bas-relief. We validate our approach through a variety of models. The results indicate that the proposed approach is effective to adapt different types of target surface with topology unchanged. Comparing with conventional methods, our approach is able to effectively produce bas-relief with a reasonable layout and distinct details.

Human leukocyte elastase (HLE) is a serine protease implicated in several inflammatory diseases, and represents a major target for anti-inflammatory drug development. In the present study, nordivaricatic acid (1), divarinyl divarate (2), and trivaric acid (3), three depsides isolated from the culture of a soil derived fungal strain were identified as inhibitors of HLE. Two didepsides 1 and 2 showed low inhibitory activity. In contrast, trivaric acid, a para-tridepside, exhibited highly potent inhibitory activity with an IC(50) value of 1.8 µM and a K(i) of 0.6 µM. Kinetic investigations with trivaric acid showed that this inhibition is reversible, competitive pattern. Further studies on the selectivity of three depsides toward serine proteases showed that they did not inhibit chymotrypsin, trypsin and thrombin even at 150 µM.

Three novel members of angucycline family named N05WA963A (1), B (2) and D (4), together with a new anthracycline named N05WA963C (3) were isolated from the culture broth of Streptomyces sp. N05WA963. The structures were elucidated on the basis of comprehensive spectral data analysis. All four compounds have shown antiproliferative effects on a panel of cancer cell lines such as SW620, K-562, MDA-MB-231, YES-4, T-98 and U251SP.

In order to screen the active constituents of Vaccinium vitis-idaea L., the compounds absorbed into the rat blood after oral administration of ethanol extract of the stems and leaves of V. vitis-idaea (EEV) have been analyzed. Two compounds were detected in the plasma and identified as arbutin and fraxin, which are originally existed in the crude drug. Furthermore, the pharmacological effects of the two compounds involving EEV for curing acute and chronic respiratory tract infection were tested. The results showed that both arbutin and fraxin have the anti-inflammatory, anti-coughing and phlegm-removing effects. Therefore, it was ascertained that the arbutin and fraxin would be the main active constituents of V. vitis-idaea L.