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<p><span>We studied the case of the Andean </span><span>region in Colombia as example of non-mechanized small farming systems in which farmers </span><span>use handheld sprayers to spray pesticides. This is the most common </span>technique to spray <span>pesticide in developing countries. To better understand the spatial distribution of</span> airborne pesticide drift deposits<span> on the soil surface using that spray technique, nine different spatial interpolation </span><span>methods were tested using a surrogate tracer substance (Uranine) i.e. classical approaches </span><span>like the linear interpolation and kriging, and some advanced methods like spatial vine </span><span>copulas, the Karhunen-Loève expansion of the underlying random field, the integrated </span><span>nested Laplace approximation and the Empirical Bayesian Kriging used in ArcMap (GIS). </span><span>This study contributes to</span><span> future </span><span>studies on mass balance and risk assessment related to </span>environmental <span>drift pollution in developing </span><span>countries.</span></p>

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At the heart of Industry 4.0. lies the automation of manufacturing processes and interoperability of corresponding applications, in most cases utilizing Cyber-Physical Systems and Internet of Things. Used within diverse industrial areas across the world, these concepts gained more importance in recent years. In the semiconductor industry, continuous improvement of all involved processes is achieved by utilizing these concepts in many different stages of the manufacturing process. This work proposes a machine learning framework that on one hand enhances wafer map classification in the testing stage of semiconductor devices, and on the other hand fulfills the requirements and demands of all involved stakeholders in adopted engineering processes. The core of the proposed system is the Wafer Health Factor, a machine learning framework that detects process deviations in analog wafer test data through pattern recognition. Additional integration of Radon-based features, as well as the use of an ensemble classification framework, boosts model performances in the presented real-world application scenario significantly. Moreover, we also show that the proposed framework performs well on two benchmark data sets from literature, even on small training data sets. We conclude, that the presented framework improves the performance of pattern recognition tasks in realworld applications and thus, enables the automatic and early detection of deviations within semiconductor manufacturing processes.