An energy harvesting device for obtaining energy from drops without needing of moving the drops along the device, in a reduced scale and combinable with othertypes of harvesting devices, the energy harvesting device comprising one or more triboelectric generators comprising a bottom electrode, a friction or triboelectric element placed over the bottom electrode, and at least two top electrodes placed over the triboelectric element and defining at least one gap between them, exposing the triboelectric element to the external environment so that on contacting a drop of liquid makes an electrical connection between the top electrodes varying the capacitance of the triboelectric generators and alternatively for functioning as a power unit for a sensor or as a self-powered sensor producing an electrical signal generated by the contact of the liquid with the electrodes.
Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System (MES) for scheduling a Stochastic Job Shop Scheduling Problem (SJSSP). ELBSO is embedded within Ordinal Optimization (OO), where in the first phase it uses a meta model, which previously was trained by a Discrete Event Simulation model of a SJSSP. The meta model used within ELBSO uses Genetic Programming (GP)-based Machine Learning (ML). Therefore, instead of using the DES model to train and test the meta model, this article uses historical data from a front-end fab to train and test. The results were statistically evaluated for the quality of the fit generated by the meta-model.
The use of Augmented Reality (AR) in industry is growing rapidly, driven by benefits such as efficiency gains and ability to overcome physical boundaries. Existing studies stress the need to take stakeholder values into account in the design process. In this study the impact of AR on stakeholders' values is investigated by conducting focus groups and interviews, using value sensitive design as a framework. Significant impacts were found on the values of safety, accuracy, privacy, helpfulness and autonomy. Twenty practical design choices to mitigate potential negative impact emerged from the study.
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