Interview mit Hans-Christian Boos zum Thema „Unternehmertum in der IT-Branche“

Ohne Zusammenfassung     

Intrinsic Motivation Systems for Autonomous Mental Development

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology     

Whole-body 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) for accurate staging of Hodgkin's disease

BACKGROUND: Staging of Hodgkin's disease (HD) is accomplished by a variety of invasive and non-invasive modalities. This prospective study was undertaken to investigate the value of whole-body positron emission tomography (PET) with 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) in defining regions involved by lymphoma compared with conventional staging methods in patients with HD. PATIENTS AND METHODS: Fourty-four newly diagnosed patients with HD underwent FDG-PET as part of their initial staging work-up. PET findings were correlated with findings of conventional staging including computed tomography, ultrasound, bone scanning, bone marrow biopsy, liver biopsy and laparotomy. When results of FDG-PET differed to those obtained by conventional methods reevaluation was performed by biopsy, if possible, or magnetic resonance imaging. RESULTS: The results of FDG-PET were compared with three hundred twenty-one conventional staging procedures performed in 44 patients. FDG-PET was positive in 38 of 44 (86%) patients at sites of documented disease. PET detected additional lesions in five cases previously not identified by conventional staging methods. In another case a nodal lesion suspect on CT was negative at FDG-PET and was settled as true negative by biopsy. As a consequence of PET findings five patients had to be upstaged and one patient had to be downstaged, resulting in changes in treatment strategy in all six cases (14%). FDG-PET failed to visualize sites of HD in four patients. In two of our patients a false positive PET result was obtained. CONCLUSIONS: Our data indicate that FDG-PET provides an imaging technique that appears to visualize involved lesions in most patients with HD and is useful in the management of these patients.     

Thermolysis of poly(diaryl diazosulfide)s

Poly(diaryl diazosulfide)s and model diaryl diazosulfides of the general structure aryl-N?N? S-aryl' are tested for their thermostability. Thermolysis in solution is followed by means of UV spectroscopy. At the beginning of the decay most model diazosulfides follow first-order kinetics, but show an acceleration of the decay rate in the further course of the thermolysis. In contrast to the models the decomposition of the corresponding polymers cannot be described by first-order kinetics, except one example ( P 1 ). Thermogravimetry studies show weight losses of 8–12% in the range between 80 and 150°C depending on the structure of the models and polymeric diazosulfides. These values correspond with the content of azo function in these compounds, therefore it is assumed that nitrogen is split off under these reaction conditions.     

Structure-Activity Relationships of Benzamides and Isoindolines Designed as SARS-CoV Protease Inhibitors Effective against SARS-CoV-2

Inhibition of coronavirus (CoV)-encoded papain-like cysteine proteases (PL^pro) represents an attractive strategy to treat infections by these important human pathogens. Herein we report on structure-activity relationships (SAR) of the noncovalent active-site directed inhibitor (R)-5-amino-2-methyl-N-(1-(naphthalen-1-yl)ethyl) benzamide ( 2 b ), which is known to bind into the S3 and S4 pockets of the SARS-CoV PL^pro. Moreover, we report the discovery of isoindolines as a new class of potent PL^pro inhibitors. The studies also provide a deeper understanding of the binding modes of this inhibitor class. Importantly, the inhibitors were also confirmed to inhibit SARS-CoV-2 replication in cell culture suggesting that, due to the high structural similarities of the target proteases, inhibitors identified against SARS-CoV PL^pro are valuable starting points for the development of new pan-coronaviral inhibitors.     

Regional mass flux balancing for controlling gentle soil remediation operations

Extraction of heavy metals by accumulating plants is a method which is currently in development for the "gentle" remediation of contaminated agricultural soils (phytoremediation, see [2]). Areal contaminant mass flux balances are basic criteria for the design of such remediations and their control. A framework for the integration of contaminant balances relating the field scale of remediation with the regional scale of soil monitoring networks is provided by the recently developed method PROTERRA [3]. The objective of the study presented in this paper was to test the suitability of PROTERRA for planning and monitoring gentle soil remediations. For this purpose we applied the PROTERRA method to the contaminated agricultural land in and around Dornach, Switzerland, to assess copper flux balances.The calculations showed that atmospheric deposition and the application of pesticides and manure are important pathways for the inputs of copper. The copper export with a special maize cultivar accumulating heavy metals would be about three times higher than the average metal export with crops. A moderate increase of sewage sludge application would lead to a substantial increase of the copper input. Decisions to remediate the soil should take the uncertainty of mass flux balances both on field scale as well as on regional scale into account. Therefore, an important need of further development of the PROTERRA method is the integration of uncertainty analysis on both scales.     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.