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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. [ABSTRACT FROM AUTHOR]

The distribution of child sexual exploitation (CE) material has been aided by the growth of the Internet. The graphic nature and prevalence of the material has made researching and combating difficult. Although used to study online CE distribution, automated data collection tools (e.g., webcrawlers) have yet to be shown effective at targeting only relevant data. Using CE-related image and keyword criteria, we compare networks starting from CE websites to those from similar non-CE sexuality websites and dissimilar sports websites. Our results provide evidence that (a) webcrawlers have the potential to provide valid CE data, if the appropriate criterion is selected; (b) CE distribution is still heavily image-based suggesting images as an effective criterion; (c) CE-seeded networks are more hub-based and differ from non-CE-seeded networks on several website characteristics. Recommendations for improvements to reliable criteria selection are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Detection of earthmoving equipment in construction images and videos can increase the automation level of many construction management tasks such as productivity measurement, locating of machines, work-zone safety, and semantic image and video indexing. Some of the earthmoving plants, such as hydraulic excavator, have articulated shapes making them a difficult target for even state of the art object recognition algorithms. The goal of this paper is to develop a model for non-rigid equipment detection and pose estimation in construction images and videos. In this paper, we describe an object recognition system based on mixture of appearances of deformable body parts of the hydraulic excavator and compare its results with general Histogram of Oriented Gradient detectors in both images and videos. Then a spatial–temporal reasoning model is presented which uses time and space constraints of the excavators'' moving patterns to improve the detection results in videos. [Copyright &y& Elsevier]

Abstract: Automated action classification of construction workers and equipment from videos is a challenging problem that has a wide range of potential applications in construction. These applications include, but are not limited to, enabling rapid construction operation analysis and ergonomic studies. This research explores the potential of an emerging action analysis framework, Bag-of-Video-Feature-Words, in learning and classifying worker and heavy equipment actions in challenging construction environments. We developed a test bed that integrates the Bag-of-Video-Feature-Words model with Bayesian learning methods for evaluating the performance of this action analysis approach and tuning the model parameters. Video data sets were created for experimental evaluations. For each video data set, a number of action models were learned from training video segments and applied to testing video segments. Compared to previous studies on construction worker and equipment action classification, this new approach can achieve good performance in recognizing multiple action categories while robustly coping with the issues of partial occlusion, view point, and scale changes. [Copyright &y& Elsevier]

Abstract: Most semiconductor companies follow the SEMI E10 and E79 guidelines to measure equipment availability and efficiency via OEE (Overall Equipment Effectiveness). However, several problems need to be conquered before implementing OEE. For example, the time intervals of OEE losses are critical to the improvement studies, but it is very hard to collect reliable and accurate data. Therefore, people will find the unknown differences between the recorded losses from their operational system and OEE losses from SEMI E79 definition once people implement OEE. Furthermore, how to obtain the theoretical unit throughput or standard UPH (unit-per-hour machine rates) to determine the average processing rates of an equipment is another issue to be conquered. In this study, we develop an IT integrated system to record the time intervals of OEE losses for the bottleneck equipment “wire bonder” in the semiconductor assembly industry. We integrate a communication system (that is developed by the wire bonder supplier) and MES (Manufacturing Execution System) into an ADC (Automated Data Collection) system for collecting useful data. The data quality is further guaranteed by the real time detection of equipment status from the communication system. The application of the ADC system has eliminated the unknown OEE losses from original 6% of manual recording system to near zero percent. Therefore, the UPH data can be collected automatically once a production lot (partition of a specific batch in an order) is finished. This resolves the accuracy and timeliness problem associated with traditional stopwatch sampling. [ABSTRACT FROM AUTHOR]

The factors which affect the behaviour of the customer's computing environment, which is undergoing a revolution away from a server or timeshare centric model to a client/server or distributed model, can no longer be identified solely through using traditional methods of data collection. Digital Equipment Corporation has developed an automated data collection process, collecting on-system data logging information from customer sites that has yielded consistent, quantitative, high integrity information. This information has been used to proactively focus on direct product and process improvements. This paper describes the on-system data logging process and analysis methodology used by Digital to measure system, product and operating system reliability with examples of the application of the techniques that provide insight into the causes of failures. [ABSTRACT FROM AUTHOR]