A Continuous Lipase-Catalyzed Acylation Process for the Large-Scale Production of Vitamin A Precursors

A continuous enzyme-catalyzed acylation process for the selective preparation of monoacylated Vitamin A precursors starting from a 1,6-diol on a large scale is reported. Screenings led to the selection of the commercially available immobilized lipase Chirazyme L2-C2 (lipase B from Candida antarctica) as the biocatalyst, different vinyl acylates as the acylating agents, and acetone as the co-solvent. Using a mixture of 70% (v/v) acetone and acylating agent allowed to increase the substrate concentration from 10 to 30% (w/w). Using a small fixed-bed reactor, this continuous process produced monoacylated product with >99% yield and >97% selectivity for the primary hydroxy group. The robustness of this system under different conditions was investigated. Consequently, the stability of the biocatalyst could be greatly improved by adding a protective pre-column and by adding small amounts of organic base and antioxidant to the substrate solution. This optimized laboratory process was used to selectively prepare monoacylated compounds in kilogram scales over one hundred days with only a minor decrease in conversion efficiency. The process was also implemented in an up-scaled mini plant for the continuous production on a kilogram-per-day scale, reproducing the results previously obtained on smaller laboratory scales

Open City Data Pipeline

Statistical data about cities, regions and at country level is collected for various purposes and from various institutions. Yet, while access to high quality and recent such data is crucial both for decision makers as well as for the public, all to often such collections of data remain isolated and not re-usable, let alone properly integrated. In this paper we present the Open City Data Pipeline, a focused attempt to collect, integrate, and enrich statistical data collected at city level worldwide, and republish this data in a reusable manner as Linked Data. The main feature of the Open City Data Pipeline are: (i) we integrate and cleanse data from several sources in a modular and extensible, always up-to-date fashion; (ii) we use both Machine Learning techniques as well as ontological reasoning over equational background knowledge to enrich the data by imputing missing values, (iii) we assess the estimated accuracy of such imputations per indicator. Additionally, (iv) we make the integrated and enriched data available both in a we browser interface and as machine-readable Linked Data, using standard vocabularies such as QB and PROV, and linking to e.g. DBpedia. Lastly, in an exhaustive evaluation of our approach, we compare our enrichment and cleansing techniques to a preliminary version of the Open City Data Pipeline presented at ISWC2015: firstly, we demonstrate that the combination of equational knowledge and standard machine learning techniques significantly helps to improve the quality of our missing value imputations; secondly, we arguable show that the more data we integrate, the more reliable our predictions become. Hence, over time, the Open City Data Pipeline shall provide a sustainable effort to serve Linked Data about cities in increasing quality.Series: Working Papers on Information Systems, Information Business and Operation

PROCEEDINGS OF THE IEEE SPECIAL ISSUE ON APPLICATIONS OF AUGMENTED REALITY ENVIRONMENTS 1 Augmented Reality for Construction Site Monitoring and Documentation

Abstract—Augmented Reality allows for an on-site presentation of information that is registered to the physical environment. Applications from civil engineering, which require users to process complex information, are among those which can benefit particularly highly from such a presentation. In this paper, we will describe how to use Augmented Reality (AR) to support monitoring and documentation of construction site progress. For these tasks, the staff responsible usually requires fast and comprehensible access to progress information to enable comparison to the as-built status as well as to as-planned data. Instead of tediously searching and mapping related information to the actual construction site environment, our AR system allows for the access of information right where it is needed. This is achieved by superimposing progress as well as as-planned information onto the user’s view of the physical environment. For this purpose, we present an approach that uses aerial 3D reconstruction to automatically capture progress information and a mobile AR client for on-site visualization. Within this paper, we will describe in greater detail how to capture 3D, how to register the AR system within the physical outdoor environment, how to visualize progress information in a comprehensible way in an AR overlay and how to interact with this kind of information. By implementing such an AR system, we are able to provide an overview about the possibilities and future applications of AR in the construction industry

Влияние толщины и взаимного расположения слоев в композиции железо-полиэтилен на энергетическое распределение быстрых нейтронов

Расчет производился для двух композиций: железо-полиэтилен, полиэтилен-железо. Уравнение переноса нейтронов решалось методом Монте-Карло в транспортном приближении. Полученные результаты свидетельствуют о значительном влиянии слоя легкого материала на величины потока и энергетическое распределение прошедших нейтронов, что позволяет осуществить выбор энергетического диапазона регистрации прошедшего потока для получения максимальной избирательной чувствительности к слою легкого материала

Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.Comment: Intelligent Vehicle Conference (oral presentation