Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.

Active site labeling of cysteine cathepsins by a straightforward diazomethylketone probe derived from the N-terminus of human cystatin C

We designed a straightforward biotinylated probe using the N-terminal substrate-like region of the inhibitory site of human cystatin C as a scaffold, linked to the thiol-specific reagent diazomethylketone group as a covalent warhead (i.e. Biot-(PEG)2-Ahx-LeuValGly-DMK). The irreversible activity-based probe bound readily to cysteine cathepsins B, L, S and K. Moreover affinity labeling is sensitive since active cathepsins were detected in the nM range using an ExtrAvidin®-peroxidase conjugate for disclosure. Biot-(PEG)2-Ahx-LeuValGly-DMK allowed a slightly more pronounced labeling for cathepsin S with a compelling second-order rate constant for association (kass = 2,320,000 M−1 s−1). Labeling of the active site is dose-dependent as observed using 6-cyclohexylamine-4-piperazinyl-1,3,5-triazine-2-carbonitrile, as competitive inhibitor of cathepsins. Finally we showed that Biot-(PEG)2-Ahx-LeuValGly-DMK may be a simple and convenient tool to label secreted and intracellular active cathepsins using a myelomonocytic cell line (THP-1 cells) as model.

Learning about probability from text and tables: Do color coding and labeling through an interactive-user interface help?

Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly assigned to learn about probabilistic reasoning from one of 4 computer-based lessons generated from a 2 (color coding/no color coding) by 2 (labeling/no labeling) between-subjects design. Learners added the labels or color coding at their own pace by clicking buttons in a computer-based lesson. Participants' eye movements were recorded while viewing the lesson. Labeling was beneficial for learning, but color coding was not. In addition, labeling, but not color coding, increased attention to important information in the table and time with the lesson. Both labeling and color coding increased looks between the text and corresponding information in the table. The findings provide support for the multimedia principle, and they suggest that providing labeling enhances learning about probabilistic reasoning from text and tables