Construction,structure and photocatalysis of janus nanofiber modified by g-C3N4 nanosheets heterostructure photocatalysts

The construction of photocatalyst with gradient band structure is guided by the principle of band gap engineering. Rational structural design is advanced and applied to construct a new-typed peculiarly structural and functional carbon-based [TiO₂/C]//[Bi₂WO₆/C] Janus nanofiber modified by g-C₃N₄ nanosheets heterostructure photocatalyst (denoted as TB-JgHP). The flexible carbon-based [TiO₂/C]//[Bi₂WO₆/C] Janus nanofiber with one side responding to ultraviolet light and the other capturing visible light is fabricated by conjugate electrospinning, and then g-C₃N₄ nanosheets are uniformly grown in-situ on the surface of the Janus nanofibers by using gas-solid reaction via gasification of urea. The optimized TB-JgHP possesses remarkable hydrogen evolution efficiency (17.48 mmol h⁻¹ g⁻¹) and methylene blue degradation rate (99.2%) under simulated sunlight illumination for 100 min, demonstrating prominent dual-functional characteristics. The enhanced photocatalytic performance benefits from the unique Janus structure as well as the synergistic effects among the triple heterostructures of TiO₂ and Bi₂WO₆, g-C₃N₄ and TiO₂, g-C₃N₄ and Bi₂WO₆. The formation of gradient band structure among heterostructures is more conducive to the multi-step separation of photo-induced electron-hole pairs and more effective absorption of light. Further, flexible self-standing carbon-based photocatalysts not only have outstanding electron transport performance, but also are easy to separate from solution with preeminent recyclable stability. Based on a series of characterization techniques, it is further proved that TB-JgHP has higher carrier separation efficiency than the counterpart contrast samples. The formation mechanism of TB-JgHP is proposed, and the construction technique is established. The design philosophy and construction technique presented in this work pave a new avenue for research and development of other heterostructure photocatalysts.     

A comprehensive survey on computer vision based concepts,methodologies, analysis and applications for automatic gun/knife detection

The ability to detect gun and gun held in hand or other body parts is a typical human skill. The same problem presents an imperative task for computer vision system. Automatic observer independent detection of hand held gun or gun held in the other body part, whether it is visible or concealed, provides enhance security in vulnerable places and initiates appropriate action there. Compare to the automatic object detection systems, automatic detection of gun has very few successful attempts. In the present scope of this paper, we present an extensive survey on automatic detection of gun and define a taxonomy for this particular detection system. We also describe the inherent difficulties related with this problem. In this survey of published papers, we examine different approaches used in state-of-the-art attempts and compare performances of these approaches. Finally, this paper concludes pointing to the possible research gaps in related fields.     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

Adaptive finite-time tracking control for parameterized nonlinear systems with full state constraints

In this article, the issue of adaptive finite-time dynamic surface control (DSC) is discussed for a class of parameterized nonlinear systems with full state constraints. Using the property of logarithmic function, a one-to-one nonlinear mapping is constructed to transform a constrained system into an unconstrained system with the same structure. The nonlinear filter is constructed to replace the first-order linear filter in the traditional DSC, and the demand on the filter time constant is reduced. Based on finite-time stable theory and using modified DSC, the finite-time controller is designed via DSC. Theoretical analysis shows that all the signals in the closed-loop system are semiglobal practical finite-time stable. Furthermore, none of the states are outside the defined open set. In the end, simulation results are presented to demonstrate the effectiveness of the proposed control schemes with both linear filters and nonlinear filters.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

Strategic environmental assessment in Romania: between benefits and constraints

This paper offers an overall picture of the importance of Strategic Environmental Assessment (SEA) in Romania and its opportunities and constraints. The role, scope and effectiveness of SEA in Romania are presented in two good practice examples and examined in order to highlight the strengths and weaknesses of the procedure at national and local scale. In practice, the number of SEA procedures is very high due to the legislation in force, and it is increasingly effective in decision-making about programmes and plans. SEA can be viewed as a great opportunity for the implementation of sustainable development in practice. It also continues to be an important procedural and decisional instrument for management and monitoring of socioeconomic development, spatial planning and environmental protection in Romania.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

基于时间延迟的多类型维修与经济生产批量联合优化研究

针对目前未能较好的综合考虑多维修类型与经济生产批量联合优化的问题，首先考虑多类型维修关系，基于时间延迟理论求出故障和缺陷次数的表达式；其次，在此基础上，综合考虑生产费用和维修费用的基础上，构建了多类型维修和经济生产批量联合优化模型，以单位时间内总费用最小为优化目标，获得最优检查间隔期和经济生产批量；最后，通过算例分析验证了模型的有效性，说明了第一类缺陷检查次数的多少对费用和经济生产批量影响不大。     

Adaptive fuzzy control for nontriangular form systems with time-varying full-state constraints

This article investigates an adaptive fuzzy tracking control problem for a class of nontriangular form systems with asymmetric time-varying full state constraints. Unknown functions are approximated by the fuzzy logic systems. A domination approach is employed to tackle the nontriangular form structure. Time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure full-state constraints satisfaction. Based on the backstepping technique and time-varying ABLFs, an adaptive controller is proposed and guarantees that all the signals in the closed-loop system are ultimately bounded and the time-varying full state constraints are met. Simulation examples are presented to further demonstrate the effectiveness of the proposed approach.