Articles

<b>Objective</b>: To investigate the status of the acoustic environment of a typical Chinese pediatric emergency care department in a time series and identify the relationship between noise levels and factors such as crowd density and movement. <b>Methods</b>: A descriptive study was designed based on a multi-loci time-series method. We measured three loci under three variable settings: the decibel value, observation volume, and emergency care volume. <b>Results</b>: The noise levels of the three loci were significantly higher than the internationally recommended levels, exceeding rate reached more than 86.3%. The 24-hour mean map of the three loci showed similar fluctuation patterns, all of which had two peaks at approximately 10:00 AM and 16:00 PM. <b>Conclusions</b>: The daytime and nighttime noise levels were well-fitted by cubic functions with different coefficients. It is suggested that crowd density and movement may play important roles in noise mean fluctuations, which can be optimized to ensure a satisfactory environment in a pediatric emergency care department.

The major automated plants have produced large volumes of high-quality products at low cost by introducing various technologies, including robotics and artificial intelligence. The code of many defects on the surface of products is embedded with economic loss and sometimes functionality loss because products are rarely found with defects. Therefore, most items’ production is done based on prediction and has an invisible fluctuation in production. The detection process for hidden defect images requires a lot of costs and needs to be supported for better progress and quality enhancement. Paint shop defects should be analyzed from color changes to detect defects effectively by preventing the variability of product demand over time. It is not easy to take visible light images without noise because the paint surfaces are glossy. A few parts of illumination and shadows remain in images, even in larger size and high-resolution images. The various painted surfaces are also needed to reflect both color and texture information in computer vision models to classify defects precisely. Several automated detection systems have been applied to paint shop inspections using lasers, infrared, x-ray, electrical, magnetic, and acoustic sensors. The chance of paint shop defects can be low, unnecessarily low, compared to clouds in the sky, but those chances impact defect functionalities. Thus, they are called as “lessons learned.” Lately, artificial intelligence has been introduced to the field of factory automation, and many defect detection feeds have found footsteps in machine learning and deep learning. Recent attempts at deep learning-based defect detection are proposing simple techniques using specific neural network architectures with big data. However, big data is still in its early stages, and significant challenges exist in normalizing and annotating such data. To get cost-efficient and timely solutions tailored to automotive paint shops, it might be a better consideration to combine deep learning solutions with traditional computer vision and more elaborate machine learning methods.

High levels of occupational noise and vibration remain a problem in all regions of the world. In Nigeria, 12−15% of the workforce are exposed to these hazards by WHO, 2001. This research intends to achieve the following objectives; To assess the noise emitted during the moulding of various types of blocks, to determine the level of vibration induced to workers of block moulders during activities and to determine the effect of noise and vibration on workers. The following materials and equipment were used; QT40B manual block moulding machine, LM2-45 Mobile Block moulding Machine, Lister powered block moulding machine, QTJ4-40 block moulding machine using 9 and 6 inches Plates, Vibrometer and Noise monitor. The workers were exposed to noise levels above 75dB and vibration levels above 5ms-2 set as upper limit values in the Directive 44/EC from 2002 – on the Minimum Health and safety Requirements Regarding to Exposure of Workers to the Risk Arising from Physical Agents Vibration.

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.