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A Coordinate Measuring Machine (CMM) is a precision measuring metrology machine used in manufacturing and quality control processes to precisely measure the geometric characteristics of objects. It operates on the principle of coordinate geometry, employing a probe to collect data points from the surface of an object to determine the dimensions, shapes, and positions of features, such as holes, slots, and surfaces. CMMs are essential tools for ensuring that manufactured parts meet design specifications and quality standards.

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This simple Python software is designed to assist Civil and Geotechnical Engineers in performing site-specific seismic hazard assessments. The program calculates the seismic response spectrum based on user-provided geotechnical and seismic parameters, generating a comprehensive technical report that includes the response spectrum data and figures. The analysis adheres to Eurocode 8 and the Greek Annex, ensuring compliance with international standards for earthquake-resistant design.

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We investigated the applicability and efficiency of the MLMC approach to the Henry-like problem with uncertain porosity, permeability and recharge. These uncertain parameters were modelled by random fields with three independent random variables. Permeability is a function of porosity. Both functions are time-dependent, have multi-scale behaviour and are defined for two layers. The numerical solution for each random realisation was obtained using the well-known ug4 parallel multigrid solver. The number of random samples required at each level was estimated by calculating the decay of the variances and the computational cost for each level. The MLMC method was used to compute the expected value and variance of several QoIs, such as the solution at a few preselected points $(t,\bx)$, the solution integrated over a small subdomain, and the time evolution of the freshwater integral. We have found that some QoIs require only 2-3 mesh levels and samples from finer meshes would not significantly improve the result. Other QoIs require more grid levels.

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This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

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In this short lecture, I explain the fundamentals of electromagnetic compatibility (EMC), the basic coupling model and coupling paths via cables, electric fields, magnetic fields and wave fields. We also look at electric vehicles as an example of systems with many conducted EMC problems due to power electronic devices such as rectifiers and inverters with non-linear components such as diodes and fast switching components such as MOSFETs or IGBTs. After a brief review of circuit analysis fundamentals and an experimental investigation of the frequency-dependent impedance of resistors, capacitors and inductors, we look at a simple low-pass filter. The transfer function is derived and measured.

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