To expand on the limited size and scope of construction silica exposure studies, a silica monitoring data compilation project was initiated through the American Conference of Governmental Industrial Hygienists Construction Committee. Personal silica exposure monitoring data was collected and analyzed from 13 private, research, and regulatory groups. An effort was made to collect as much detail as possible about task, tool, and environmental and control conditions so as much information as possible could be garnered. There were considerable data gaps, particularly with regulatory agency data, that represented over half of the data set. There were 1374 personal quartz samples reported with a geometric mean of 0.13 mg/m(3) and a GSD of 5.9. Descriptive statistics are reported by trade, task, tool, and data source type. Highest exposures were for abrasive blasters, surface and tuckpoint grinders, jackhammers, and rock drills. The sample period was important, with short-term samples (up to 2 hours) having considerably higher levels than midterm (2-6 hours) or longer (over 6 hours) samples. For nearly all exposure variables, a large portion of variable categories were at or over the quartz occupational exposure limit of 0.05 mg/m(3), including 8 of 8 trade, 13 of 16 task, and 12 of 16 tool categories. The respiratory protection commonly used on construction sites is often inadequate for the exposures encountered. The data variability within task and tool was very large, with some very high exposures reported for a broad spectrum of tools. Further understanding of the conditions leading to high exposures will require more detailed documentation of the sample characteristics following database design recommendations or systematic surveys of exposure in this complex industry.

Objectives-Determine if a university based (third party) intervention can improve construction contractor organizational performance to increase use of fall prevention practices and technologies. Setting-Falls are the leading cause of worker injury and death in the construction industry. Equipment and practices that can prevent falls are often not used appropriately in the dynamic construction work environment. Methods-A contractual partnership between a university and construction contractors created management systems to ensure use of fall protection measures. Audits by university faculty provided accountability for implementing the fall prevention system. Evaluation was conducted by quasiexperimental methodology comparing changes in audit score from baseline to fifth quarter from baseline for intervention and control contractors. Results-Audit scores improvement was greater for intervention than for control contractor group. Conclusion-A third party intervention can improve contractor fall prevention performance. (Injury Prevention 2001;7(Suppl I):i64-67)

The study demonstrates that workers are more willing to attempt to change worksite conditions following training, and that their efficacy at making changes is substantially greater than before they were trained. The study confirms earlier work and strengthens these conclusions by using statistically tested comparisons of impact measures pre- and post-training.

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Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0.