Search

Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states.

Serial protein crystallography has emerged as a powerful method of data collection on small crystals from challenging targets, such as membrane proteins. Multiple microcrystals need to be located on large and often flat mounts while exposing them to an X-ray dose that is as low as possible. A crystal-prelocation method is demonstrated here using low-dose 2D full-field propagation-based X-ray phase-contrast imaging at the X-ray imaging beamline TOMCAT at the Swiss Light Source (SLS). This imaging step provides microcrystal coordinates for automated serial data collection at a microfocus macromolecular crystallography beamline on samples with an essentially flat geometry. This prelocation method was applied to microcrystals of a soluble protein and a membrane protein, grown in a commonly used double-sandwich in situ crystallization plate. The inner sandwiches of thin plastic film enclosing the microcrystals in lipid cubic phase were flash cooled and imaged at TOMCAT. Based on the obtained crystal coordinates, both still and rotation wedge serial data were collected automatically at the SLS PXI beamline, yielding in both cases a high indexing rate. This workflow can be easily implemented at many synchrotron facilities using existing equipment, or potentially integrated as an online technique in the next-generation macromolecular crystallography beamline, and thus benefit a number of dose-sensitive challenging protein targets., IUCrJ, 7 (6), ISSN:2052-2525

Cryocrystallography, which is commonly used in macromolecular crystallography, may sometimes reduce the quality of diffraction data and the visibility of crystals owing to frost adhesion. A device has been developed to remove frost by drizzling liquid nitrogen over the crystals, which enabled noise reduction of diffraction images and the centering of crystals with low visibility owing to frost adhesion., Cryocrystallography is a technique that is used more often than room-temperature data collection in macromolecular crystallography. One of its advantages is the significant reduction in radiation damage, which is especially useful in synchrotron experiments. Another advantage is that cryopreservation provides simple storage of crystals and easy transportation to a synchrotron. However, this technique sometimes results in the undesirable adhesion of frost to mounted crystals. The frost produces noisy diffraction images and reduces the optical visibility of crystals, which is crucial for aligning the crystal position with the incident X-ray position. To resolve these issues, a computer-controlled device has been developed that drizzles liquid nitrogen over a crystal to remove frost. It was confirmed that the device works properly, reduces noise from ice rings in diffraction images and enables the centering of crystals with low visibility owing to frost adhesion.

An automated data-collection system named ZOO has been developed. This system enabled faster data collection, facilitated advanced data-collection and data-processing techniques, and permitted the collection of higher quality data., Owing to the development of brilliant microfocus beamlines, rapid-readout detectors and sample changers, protein microcrystallography is rapidly becoming a popular technique for accessing structural information from complex biological samples. However, the method is time-consuming and labor-intensive and requires technical expertise to obtain high-resolution protein crystal structures. At SPring-8, an automated data-collection system named ZOO has been developed. This system enables faster data collection, facilitates advanced data-collection and data-processing techniques, and permits the collection of higher quality data. In this paper, the key features of the functionality put in place on the SPring-8 microbeam beamline BL32XU are described and the major advantages of this system are outlined. The ZOO system will be a major driving force in the evolution of the macromolecular crystallography beamlines at SPring-8.

A DigitalMicrograph script named InsteaDMatic has been developed for automated continuous rotation electron diffraction (cRED) data acquisition. InsteaDMatic coordinates functions of the transmission electron microscope goniometer and camera in order to tune up diffraction-frame recording simultaneously with crystal rotation. Exploiting fast and automated data collection, the influence of the electron dose rate on the quality of cRED data was studied, and structural models obtained for two different parallel-beam illumination modes (aperture selection and nanoprobe) were compared., A DigitalMicrograph script, InsteaDMatic, has been developed to facilitate rapid automated 3D electron diffraction/microcrystal electron diffraction data acquisition by continuous rotation of a crystal with a constant speed, denoted as continuous rotation electron diffraction. The script coordinates microscope functions, such as stage rotation, and camera functions relevant for data collection, and stores the experiment metadata. The script is compatible with any microscope that can be controlled by DigitalMicrograph and has been tested on both JEOL and Thermo Fisher Scientific microscopes. A proof of concept has been performed through employing InsteaDMatic for data collection and structure determination of a ZSM-5 zeolite. The influence of illumination settings and electron dose rate on the quality of diffraction data, unit-cell determination and structure solution has been investigated in order to optimize the data acquisition procedure.

We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the Kilobot and compatible with existing hardware, the Kilogrid is a modular system composed of a grid of computing nodes, or modules that provides a bidirectional communication channel between the Kilobots and a remote workstation. In this paper, we describe the hardware and software architecture of the Kilogrid system as well as its functioning to accompany its release as a new open hardware tool for the swarm robotics community. We demonstrate the capabilities of the Kilogrid using a 200-module Kilogrid, swarms of up to 100 Kilobots, and four different case studies: exploration and obstacle avoidance, site selection based on multiple gradients, plant watering, and pheromone-based foraging. Through this set of case studies, we show how the Kilogrid allows the experimenter to virtualize sensors and actuators not available to the Kilobot and to automatize the collection of data essential for the analysis of the experiments.

Though a wealth of data exists for the characterization of pedestrian movement, a majority of it originates from experimental settings owing to the current state of trackers for real-world scenarios. While these trackers are steadily improving, they remain insufficiently reliable for the accurate, microscopic tracking of individuals. We propose the application of evolutionary algorithms to the calibration of parameters of existing trackers in order to further optimize their performance in complex cases, with an initial focus on feature-based tracking methods. Preliminary results demonstrate a two-fold improvement of tracking accuracy and as a strong correlation in performance between cases.