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Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical processing units spreads the use of deep learning-based solutions to many problems, which includes the fault diagnosis on gearboxes. Those solutions usually require a significant amount of data, high computational power, and a long training process. The training of deep learning-based systems may not be feasible when GPUs are not available. This paper proposes a solution to reduce the training time of deep learning-based fault diagnosis systems without compromising their accuracy. The solution is based on the use of a decision stage to interpret all the probability outputs of a classifier whose output layer has the softmax activation function. Two classification algorithms were applied to perform the decision. We have reduced the training time by almost 80% without compromising the average accuracy of the fault diagnosis system.

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BACKGROUND Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs) but a precision public health approach is still lacking to improve health equity and account for population disparities. OBJECTIVE This study aimed (i) to develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (ii) to validate the SNI framework to increase organ donation awareness in California taking into account underlying population disparities. METHODS This study developed and tested an SNI framework which uses publicly available data at the ZCTA level to uncover demographic environments using clustering analysis which is then used to guide digital health intervention using the Meta business platform. The SNI delivered five tailored organ donation related educational contents through Facebook to four distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted. RESULTS Four main clusters with distinctive sociodemographic characteristics were identified for the State of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (beta=.2187;P<.001), with the highest effect on Cluster 1 (beta=.3683; P<.001) and the lowest effect on Cluster 4 (beta=.0936.;P=0.053). Cluster 1 is mainly composed of a population that is more likely to be rural, white, and have a higher rate of Medicare beneficiaries while Cluster 4 was more likely to be urban, Hispanic, and African-American, with high employment rate without high income and a higher proportion of Medicaid beneficiaries. CONCLUSIONS The proposed SNI framework, with its adaptive content tuning mechanism, learns and delivers, in real-time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable. CLINICALTRIAL This study was approved by the Institutional Review Board (IRB) Office of University of California, Davis, US (1596733-2). The study was registered on ClinicalTrials.gov (NTC04850287).