From the social learning theory to a social learning algorithm for global optimization

Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization approach that mimics the social learning process of humans. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Experimental studies in this paper showed the appealing search behavior of this human intelligence-inspired approach, which can reach the global optimum even in ill conditions. The effectiveness and high efficiency of the proposed algorithm has further been verified by comparing to some representative EC algorithms and variants on a set of benchmarks

How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results. Our theoretical analysis and experiment prove that the proposed low-rank solution does not require massive inputs to guarantee the performance, and thereby simplifying the design of two learning methods for the solution. Intensive experiments show the proposed solution improves the single learning method in both qualitative and quantitative assessments. Surprisingly, it shows more superior capability on noisy images and outperforms state-of-the-art methods

安全型糖尿病足趾甲修剪钳的研制与应用*

Objective: To evaluate the clinical application effects of new type secure toenail clipper for diabetes patients. Methods: Combining advantages and abandoning shortcomings of common nail clippers and scissors, designed a new type toenail clipper for diabetes patients with abnormal and (or) hard toenails, and applied it in 450 diabetes patients. Results: Easy application with safety and effective pruning. After pruning, the pain and oppression feeling caused by abnormal and (or) hard toenails relieved, and no case had diabetic foot ulcer. Conclusions: The new type secure toenail clipper can effectively reduce the sense of pain and oppression, and decrease the incidence of diabetic foot ulcer.目的  评价安全型糖尿病足趾甲修剪钳的临床应用效果。方法  结合普通指甲钳及剪刀的优点,摒弃缺点，针对糖尿病足顽固趾甲设计一款新型的趾甲修剪钳，应用于450例糖尿病足顽固趾甲患者。结果  操作者施力方便，修剪安全、有效。修剪后患者疼痛感与压迫感减轻，无1例发生糖尿病足溃疡。结论  新型糖尿病足趾甲修剪钳能够有效减轻患者顽固趾甲所致的疼痛感与压迫感，降低糖尿病足溃疡发生率

Wolf is Coming—Dynamic Classification Prediction Model of Vespa Mandarinia

Given the threat of Vespa mandarinia invasion to ecological balance, according to the data and information provided, the dynamic reproduction model of Vespa mandarinia is established by using natural domain interpolation, and the variation law of total bumblebee with time, latitude, and longitude is obtained. At the same time, we established the classification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.We meshed the area provided by the title, assigned values to the location of Vespa mandarinia (VM), and established a VM diffusion model with natural neighborhood interpolation. Its propagation process is simulated by cellular automata. It is determined that VM spreads in a circular shape centered at (122.93174°W, 48.93457°N) and (122.57376°W, 49.07848°N) in the Washington area, with the farthest distance being 1184.4 km and 985 km respectively.We set up a classification prediction model for better classification. According to the image upload time and location, SVM and neural network are used for classification prediction, and the classification accuracy is 74.26% and 97.60%, respectively, and the neural network has higher classification accuracy. So we choose the neural network

Evidence that fructose 1,6-bisphosphate specifically protects the α-subunit of pyrophosphate-dependent 6-phosphofructo-1-phosphotransferase against proteolytic degradation

AbstractPyrophosphate-dependent 6-phosphofructo-1-phosphotransferase (PFP) consists of α (regulatory) and β (catalytic) subunits. The α-subunit was previously reported to be much more susceptible to tryptic digestion than the β-subunit. In this study, ligand-induced protection of PFP subunits against proteolysis by subtilisin was investigated in vitro and the data obtained demonstrated that fructose 1,6-bisphosphate (Fru-1,6-P2), while exerting negligible effect on the β-subunit, remarkably protected the α-subunit against proteolytic degradation. Western blot analysis revealed a good correlation between the Fru-1,6-P2 concentration and the degree of corresponding protection on the α-subunit against proteolysis. In contrast, none of other examined ligands including fructose 2,6-bisphosphate, fructose 6-phosphate and pyrophosphate had such protection on the α-subunit. This finding (1) indicates that the stability of the α-subunit can be selectively increased by Fru-1,6-P2, and (2) suggests that Fru-1,6-P2 is likely a special effector of the α-subunit