Filtern
Erscheinungsjahr
- 2017 (117)
- 2018 (81)
- 2022 (67)
- 2016 (64)
- 2021 (59)
- 2020 (52)
- 2019 (50)
- 2015 (48)
- 2023 (46)
- 2013 (40)
- 2006 (39)
- 2007 (33)
- 2008 (31)
- 2014 (31)
- 2010 (29)
- 2005 (27)
- 2012 (25)
- 2003 (22)
- 2011 (22)
- 2002 (19)
- 2004 (18)
- 2009 (18)
- 2001 (17)
- 2000 (15)
- 1996 (9)
- 1999 (9)
- 1998 (7)
- 1995 (6)
- 2024 (5)
- 1997 (4)
- 1983 (1)
- 1987 (1)
- 1988 (1)
- 1992 (1)
- 1994 (1)
Dokumenttyp
- Wissenschaftlicher Artikel (1015) (entfernen)
Sprache
- Deutsch (790)
- Englisch (223)
- Französisch (1)
- Spanisch (1)
Volltext vorhanden
- nein (1015) (entfernen)
Schlagworte
- Geldpolitik (6)
- Building Information Modeling (4)
- Kühllastberechnung (4)
- Qualitätsplan (3)
- Reinraumtechnik (3)
- VDI 2078 (3)
- CDK (2)
- Deutschlandwetter (2)
- Energiepolitik (2)
- Industry Foundation Classes (2)
Institut
- Wirtschaftsrecht (382)
- Institut für Internetsicherheit (160)
- Wirtschaft und Informationstechnik Bocholt (68)
- Institut für Innovationsforschung und -management (55)
- Westfälisches Energieinstitut (55)
- Westfälisches Institut für Gesundheit (47)
- Wirtschaft Gelsenkirchen (35)
- Elektrotechnik und angewandte Naturwissenschaften (33)
- Wirtschaftsingenieurwesen (23)
- Informatik und Kommunikation (20)
The German supply chain law ( Lieferkettensorgfaltspflichtengesetz, abbreviated: LkSG) which enters into force on 1 January 2023 is part of the developing legal framework for human rights in global supply chains. Like the French vigilance law, it represents a new generation of supply chain laws which impose mandatory human rights due diligence obligations. The LkSG requires enterprises to exercise a number of due diligence obligations – from conducting risk analysis to undertaking preventive measures or remedial actions. The law is based on public enforcement via a competent authority, the Federal Office for Economic Affairs and Export Control (BAFA). The BAFA monitors and enforces compliance with the due diligence obligations. Non-compliant enterprises can be fined with up to 800,000 Euros and, in some cases, up to 2% of the annual turnover. Whilst the LkSG is an important step towards achieving greater corporate sustainability, it also has limitations. It was a political compromise and, as such, it does not include a new civil liability for non-compliance. Moreover, by default, it only applies to the enterprise’s own business area and its direct suppliers, whereas indirect suppliers are only included where the enterprise has substantiated knowledge that an obligation has been violated.
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A “bottom-up” decomposition of C10E4 into the smallest fragment molecules (particles) that satisfy chemical intuition leads to convincing simulation results which agree with experimental findings for bilayer formation and thickness. For integration of the equations of motion Shardlow’s S1 scheme proves to be a favorable choice with best overall performance. Increasing the integration time steps above the common setting of 0.04 DPD units leads to increasingly unphysical temperature drifts, but also to increasingly rapid formation of bilayer superstructures without significantly distorted particle distributions up to an integration time step of 0.12. A scaling of the mutual particle–particle repulsions that guide the dynamics has negligible influence within a considerable range of values but exhibits apparent lower thresholds beyond which a simulation fails. Repulsion parameter scaling and molecular particle decomposition show a mutual dependence. For mapping of concentrations to molecule numbers in the simulation box particle volume scaling should be taken into account. A repulsion parameter morphing investigation suggests to not overstretch repulsion parameter accuracy considerations.
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the rules. This requires a computational workflow with data import, fragmentation algorithm integration, and result visualisation. The described workflow is normally unavailable for a new algorithm and must be set up individually. This work presents an open Java rich client Graphical User Interface (GUI) application to support the development of new in silico molecule fragmentation algorithms and make them readily available upon release. The MORTAR (MOlecule fRagmenTAtion fRamework) application visualises fragmentation results of a set of molecules in various ways and provides basic analysis features. Fragmentation algorithms can be integrated and developed within MORTAR by using a specific wrapper class. In addition, fragmentation pipelines with any combination of the available fragmentation methods can be executed. Upon release, three fragmentation algorithms are already integrated: ErtlFunctionalGroupsFinder, Sugar Removal Utility, and Scaffold Generator. These algorithms, as well as all cheminformatics functionalities in MORTAR, are implemented based on the Chemistry Development Kit (CDK).
Zentrale Raumlufttechnische Anlagen (RLT-Anlagen) sind für Betriebszeiten von fünfzehn und mehr Jahren konzipiert. Nicht selten werden die Geräte auch nach 25 Jahren Dank Retrofit weiterbetrieben. Unberücksichtigt bleibt dabei, ob die zukünftigen, klimatischen Bedingungen noch der Auslegung entsprechen. Zur Überprüfung der klimatischen Änderungen können sogenannte Testreferenzjahre (TRY – Test Reference Year) genutzt werden. Diese basieren für die heutige Auslegung auf den lokalen, stündlichen Wetterbedingungen im Bezugsjahr 2012 und zusätzlich auf modellbasierten Wetterdaten für das Bezugsjahr 2045.
Das Zentralluftgerät einer Krankenhaus-Intensivstation wurde für die 15 Wetter¬stationen der VDI 4710, Blatt 3 in Deutschland auf die Leistungsanforderungen von heute und für das Jahr 2045 untersucht. Zusätzlich wurden für den Standort Berlin die aktuellen Wetteraufzeichnungen im Sommer 2020 betrachtet. Daraus lassen sich Rückschlüsse ziehen, wie sich städtische Wärmeinseln (UHI – Urban Heat Islands) zukünftig auf den Energie- und Leistungsbedarf zur Gebäudeklimatisierung auswirken werden.
Die Auswirkungen auf die Wärme- und Kältespitzenleistung sowie der kumulierte Energiebedarf werden genauso analysiert wie der Befeuchtungsbedarf. Hieraus lassen sich die potenziellen Leistungsreserven abschätzen und die Klimaresilienz der Anlagentechnik bewerten.
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
Keine Landesgesetzgebungskompetenz für ausnahmsloses Verbot von Windenergieanlagen in Waldgebieten
(2022)
Short Selling
(2022)
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
Die Ukrainekrise und coronabedingte Lieferkettenprobleme treiben derzeitdie Rohstoff-, Material- und Lebensmittelpreise hoch. Auch die Inflationser-wartungen steigen; es drohen Zweitrundeneffekte imGefolge höhererLohnforderungen und Lohnabschlüsse. Langfristig könnten in der Eurozoneweitere Faktoren die Inflation treiben, z.B. angebotsseitig der Fachkräfte-mangel sowie globale Nahrungsmittelknappheiten und politikseitig diegewollten Effekte der Klimapolitik. Der Beitrag diskutiert vor diesemHinter-grund geldpolitische Implikationen.
Planung bzw. Budgetierung bilden ein zentrales Element des Controlling. Aussagekräftige Ergebnisse der Budgetierung sind unverzichtbar für die Steuerung der im Unternehmen verfügbaren Ressourcen. Entsprechend der hohen Bedeutung der Budgetierung ist eine intensive methodische Innovationsbereitschaft bei den Planungsinstrumenten in den letzten Jahren zu beobachten. Zwischenzeitlich werden auch die Einsatzbereiche des Zero-Based-Budgeting wieder intensiver diskutiert (= reloaded), wobei hier die Besonderheit darin besteht, dass dieses Instrument bereits in den 80er Jahren für einige Jahre eine stärkere Beachtung in der betriebswirtschaftlichen Theorie und Praxis gefunden hatte.
The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public.
Ein größerer Anteil der in den letzten Jahren vorgenommenen Unternehmensakquisitionen wurde maßgeblich mit attraktiven Synergieerwartungen begründet. Bei näherer Betrachtung können diese Synergien oft nur wenig präzise quantifiziert und der Zeitpunkt ihrer Realisierung nur ungenau eingeordnet werden. Der vorliegende Beitrag zeigt die Bedeutung von Synergien in Verbindung mit dem Goodwill, grenzt die Kosten- und Umsatzsynergien inhaltlich ab und befasst sich auf der Basis zahlreicher Studien mit dem aktuellen Erkenntnisstand in Verbindung mit der Vorbereitung und Realisierung von Kosten- und Umsatzsynergien.
Das Phänomen des Shareholder Activismbzw. der aktivistischen Investorenwar bis vor wenigen Jahren primär aus demangloamerikanischen Raumbekannt. Seit einiger Zeit sind verstärkt auch europäische und deutscheUnternehmen das Ziel von aktivistischen Aktionären. Der vorliegendeBeitrag zeigt die Zielsetzungen dieser Investorengruppe und die hierbeiverfolgten Strategien bzw. eingesetzten Maßnahmen auf, womit paralleleine Beschreibung des Geschäftsmodells des finanziell geprägten Share-holder Activismvorgelegt wird.
The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER — Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.
Different charge treatment approaches are examined for cyclotide-induced plasma membrane disruption by lipid extraction studied with dissipative particle dynamics. A pure Coulomb approach with truncated forces tuned to avoid individual strong ion pairing still reveals hidden statistical pairing effects that may lead to artificial membrane stabilization or distortion of cyclotide activity depending on the cyclotide’s charge state. While qualitative behavior is not affected in an apparent manner, more sensitive quantitative evaluations can be systematically biased. The findings suggest a charge smearing of point charges by an adequate charge distribution. For large mesoscopic simulation boxes, approximations for the Ewald sum to account for mirror charges due to periodic boundary conditions are of negligible influence.