Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.

In this emerging area article we review recent progress in the mechanical destruction of cancer cells using laser-induced shock waves. The pure mechanical damaging and destruction of cancer cells associated with this technique possibly opens up a new route to tumor treatments and the corresponding therapies. At the same time progress in multiscale simulation techniques makes it possible to simulate mechanical properties of soft biological matter such as membranes, cytoskeletal networks and even whole cells and tissue. In this way an interdisciplinary approach to understanding key mechanisms in shock wave interactions with biological matter has become accessible. Mechanical properties of biological materials are also critical for many physiological processes and cover length scales ranging from the atomistic to the macroscopic scale. We argue that the latest developments and progress in experimentation enable the investigation of the shock wave interaction with cancer cells on multiple time- and length-scales. In this way the integrated use of experiment and simulation can address key challenges in this field. The exploration of the biological effects of laser-generated shock waves on a fundamental level constitutes an emerging multidisciplinary research area combining scientific methods from the areas of physics, biology, medicine and computer science.

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The impact of pressure waves on cells may provide several possible applications in biology and medicine including the direct killing of tumors, drug delivery or gene transfection. In this study we characterize the physical properties of mechanical pressure waves generated by a nanosecond laser pulse in a setup with well-defined cell culture conditions. To systematically characterize the system on the relevant length and time scales (micrometers and nanoseconds) we use photon Doppler velocimetry (PDV) and obtain velocity profiles of the cell culture vessel at the passage of the pressure wave. These profiles serve as input for numerical pressure wave simulations that help to further quantify the pressure conditions on the cellular length scale. On the biological level we demonstrate killing of glioblastoma cells and quantify experimentally the pressure threshold for cell destruction.

Isolation of individual cells from a heterogeneous cell population is an invaluable step in the analysis of single cell properties. The demands in molecular and cellular biology as well as molecular medicine are the selection, isolation, and monitoring of single cells and cell clusters of biopsy material. Of particular interest are methods which complement a passive optical or spectroscopic selection with a variety of active single cell processing techniques such as mechanical, biochemical, or genetic manipulation prior to isolation. Sophisticated laser-based cell processing systems are available which can perform single cell processing in a contact-free and sterile manner. Until now, however, these multipurpose turnkey systems offer only basic micromanipulation and are not easily modified or upgraded, whereas laboratory situations often demand simple but versatile and adaptable solutions. We built a flexible laser micromanipulation platform combining contact-free microdissection and catapulting capabilities using a pulsed ultraviolet (337nm) laser with simultaneous generation of optical tweezing forces using a continuous wave infrared (1064nm) laser. The potential of our platform is exemplified with techniques such as local laser-induced injection of biomolecules into individual living cells, laser surgery, isolation of single cells by laser catapulting, and control of neuronal growth using optical gradient forces. Arbitrary dynamic optical force patterns can be created by fast laser scanning with acousto-optical deflectors and galvanometer mirrors, allowing multibeam contact-free micromanipulation, a prerequisite for reliable handling of material in laboratory-on-a-chip applications. All common microscopy techniques can be used simultaneously with the offered palette of micromanipulation methods. Taken together, we show that advanced optical micromanipulation systems can be designed which combine quality, cost efficiency, and adaptability.

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Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that purpose. This paper investigates the performance of Differential Evolution for tuning hyperparameters of supervised learning algorithms for classification tasks. This empirical study involves a range of different machine learning algorithms and datasets with various characteristics to compare the performance of Differential Evolution with Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian Optimization approach. The results indicate that Differential Evolution outperforms SMAC for most datasets when tuning a given machine learning algorithm -particularly when breaking ties in a firstto-report fashion. Only for the tightest of computational budgets SMAC performs better. On small datasets, Differential Evolution outperforms SMAC by 19% (37% after tie-breaking). In a second experiment across a range of representative datasets taken from the literature, Differential Evolution scores 15% (23% after tiebreaking) more wins than SMAC.

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