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Einleitung und Fragestellung:
Abusive Supervision wird mit willentlicher Leistungszurückhaltung, verringerter Motivation, erhöhtem Stresserleben, psychosomatischen Beschwerden und Burnout bei Mitarbeitenden assoziiert. Angesichts der hohen Prävalenz destruktiver Führung bleibt bislang die Frage offen, welche
protektiven Ressourcen die genannten Zusammenhänge abpuffern.
Theoretischer Hintergrund:
Abusive Supervision bezieht sich auf das Ausmaß der feindseligen verbalen und nonverbalen Verhaltensweisen einer Führungskraft. Basierend auf dem Anforderungs- Ressourcen- Modell gehen wir davon aus, dass sich personale Ressourcen, die Mitarbeitende in der arbeitsfreien Zeit aufbauen, positiv auf den negativen Effekt zwischen destruktiver Führung und Mitarbeitergesundheit auswirken. Wir fokussieren hier die generalisierte Selbstwirksamkeitserwartung, die sich im Sinne der sozialkognitiven Theorie und zahlreichen empirischen Befunden als gesundheitsrelevante Ressource im
Umgang mit domänenübergreifenden Belastungen herausgestellt hat. Diese sollte durch Bewältigungserfahrung in der arbeitsfreien Zeit gefördert werden. Bewältigungserfahrung in der Freizeit bedeutet die Gelegenheit des Erlebens von Kompetenz und Fachwissen.
Methode:
Die Moderatoranalyse wurde im Rahmen einer Querschnittsbefragung einer anfallenden Stichprobe mit N = 305 Personen getestet. Die Variablen wurden mit der Abusive Supervision Scale (Tepper, 2000), dem REQ (Sonnentag & Fritz, 2007), und der Subskala emotionale Erschöpfung des MBI (Büssing & Perrar, 1992) gemessen.
Ergebnisse:
In dieser Studie zeigen „Mastery Experiences“ einen hypothesenkonformen Puffereffekt, nicht jedoch die anderen Erholungsstrategien, die auch mit getestet wurden. Es zeigt sich also die Tendenz, dass sich Mitarbeitende durch das Erlernen neuer Kompetenzen und den Aufbau von Selbstwirksamkeit vor den gesundheitsschädlichen Auswirkungen destruktiver Führung schützen können. Das
Korrelationsmuster deutet aber vrmtl. auch problematische Aspekte dieser Erholungsstrategie an.
Diskussion:
Limitierend muss erwähnt werden, dass wir die vermutete vermittelnde Variable Selbstwirksamkeit nicht explizit gemessen haben, und dass zukünftige Untersuchungen den Effekt in Form einer mediierten Moderation replizieren müssen.
An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLSAA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.
An Augmented Multiphase Rail Launcher With a Modular Design: Extended Setup and Muzzle Fed Operation
(2024)
Inspired by the super-human performance of deep learning models in playing the game of Go after being presented with virtually unlimited training data, we looked into areas in chemistry where similar situations could be achieved. Encountering large amounts of training data in chemistry is still rare, so we turned to two areas where realistic training data can be fabricated in large quantities, namely a) the recognition of machine-readable structures from images of chemical diagrams and b) the conversion of IUPAC(-like) names into structures and vice versa. In this talk, we outline the challenges, technical implementation and results of this study.
Optical Chemical Structure Recognition (OCSR): Vast amounts of chemical information remain hidden in the primary literature and have yet to be curated into open-access databases. To automate the process of extracting chemical structures from scientific papers, we developed the DECIMER.ai project. This open-source platform provides an integrated solution for identifying, segmenting, and recognising chemical structure depictions in scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilises a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string.
DECIMER.ai is data-driven, relying solely on the training data to make accurate predictions without hand-coded rules or assumptions. The latest model was trained with 127 million structures and 483 million depictions (4 different per structure) on Google TPU-V4 VMs
Name to Structure Conversion: The conversion of structures to IUPAC(-like) or systematic names has been solved algorithmically or rule-based in satisfying ways. This fact, on the other side, provided us with an opportunity to generate a name-structure training pair at a very large scale to train a proof-of-concept transformer network and evaluate its performance.
In this work, the largest model was trained using almost one billion SMILES strings. The Lexichem software utility from OpenEye was employed to generate the IUPAC names used in the training process. STOUT V2 was trained on Google TPU-V4 VMs. The model's accuracy was validated through one-to-one string matching, BLEU scores, and Tanimoto similarity calculations. To further verify the model's reliability, every IUPAC name generated by STOUT V2 was analysed for accuracy and retranslated using OPSIN, a widely used open-source software for converting IUPAC names to SMILES. This additional validation step confirmed the high fidelity of STOUT V2's translations.
Computational methods for the accurate prediction of protein folding based on amino acid sequences have been researched for decades. The field has been significantly advanced in recent years by deep learning-based approaches, like AlphaFold, RoseTTAFold, or ColabFold. Although these can be used by the scientific community in various, mostly free and open ways, they are not yet widely used by bench scientists in relevant fields such as protein biochemistry or molecular biology, who are often not familiar with software tools such as scripting notebooks, command-line interfaces or cloud computing. In addition, visual inspection functionalities like protein structure displays, structure alignments, and specific protein hotspot analyses are required as a second step to interpret and apply the predicted structures in ongoing research studies.
PySSA (Python rich client for visual protein Sequence to Structure Analysis) is an open Graphical User Interface (GUI) application combining the protein sequence to structure prediction capabilities of ColabFold with the open-source variant of the molecular structure visualisation and analysis system PyMOL to make both available to the scientific end-user. PySSA enables the creation of managed and shareable projects with defined protein structure prediction and corresponding alignment workflows that can be conveniently performed by scientists without specialised computer skills or programming knowledge on their local computers. Thus, PySSA can help make protein structure prediction more accessible for end-users in protein chemistry and molecular biology as well as be used for educational purposes. It is openly available on GitHub, alongside a custom graphical installer executable for the Windows operating system: https://github.com/urban233/PySSA/wiki/Installation-for-Windows-Operating-System.
To demonstrate the capabilities of PySSA, its usage in a protein mutation study on the protein drug Bone Morphogenetic Protein 2 (BMP2) is described: the structure prediction results indicate that the previously reported BMP2-2Hep-7M mutant, which is intended to be less prone to aggregation, does not exhibit significant spatial rearrangements of amino acid residues interacting with the receptor.
The DECIMER.ai Project
(2024)
Over the past few decades, the number of publications describing chemical structures and their metadata has increased significantly. Chemists have published the majority of this information as bitmap images along with other important information as human-readable text in printed literature and have never been retained and preserved in publicly available databases as machine-readable formats. Manually extracting such data from printed literature is error-prone, time-consuming, and tedious. The recognition and translation of images of chemical structures from printed literature into machine-readable format is known as Optical Chemical Structure Recognition (OCSR). In recent years, deep-learning-based OCSR tools have become increasingly popular. While many of these tools claim to be highly accurate, they are either unavailable to the public or proprietary. Meanwhile, the available open-source tools are significantly time-consuming to set up. Furthermore, none of these offers an end-to-end workflow capable of detecting chemical structures, segmenting them, classifying them, and translating them into machine-readable formats.
To address this issue, we present the DECIMER.ai project, an open-source platform that provides an integrated solution for identifying, segmenting, and recognizing chemical structure depictions within the scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilizes a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string.
A key strength of DECIMER.ai is that its algorithms are data-driven, relying solely on the training data to make accurate predictions without any hand-coded rules or assumptions. By offering this comprehensive, open-source, and transparent pipeline, DECIMER.ai enables automated extraction and representation of chemical data from unstructured publications, facilitating applications in chemoinformatics and drug discovery.
Unsupervised physics-informed deep learning can be used to solve computational physics problems by training neural networks to satisfy the underlying equations and boundary conditions without labeled data. Parameters such as network architecture and training method determine the training success. However, the best choice is unknown a priori as it is case specific. Here, we investigated network shapes, sizes, and types for unsupervised physics-informed deep learning of the two-dimensional Reynolds averaged flow around cylinders. We trained mixed-variable networks and compared them to traditional models. Several network architectures with different shape factors and sizes were evaluated. The models were trained to solve the Reynolds averaged Navier-Stokes equations incorporating Prandtl’s mixing length turbulence model. No training data were deployed to train the models. The superiority of the mixed-variable approach was confirmed for the investigated high Reynolds number flow. The mixed-variable models were sensitive to the network shape. For the two cylinders, differently deep networks showed superior performance. The best fitting models were able to capture important flow phenomena such as stagnation regions, boundary layers, flow separation, and recirculation. We also encountered difficulties when predicting high Reynolds number flows without training data.
Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture
(2024)
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture
(2024)
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. To evaluate the model's performance, a benchmark was performed using a real-world dataset of hand-drawn chemical structures. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.
From https://github.com/zielesny/Jdpd:
Jdpd - An open Java Simulation Kernel for Molecular Fragment Dissipative Particle Dynamics (DPD)
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems like MFsim.
Since Jdpd version 1.6.1.0 Jdpd is available in a (basic) double-precision version and a (derived) single-precision version (= JdpdSP) for all numerical calculations, where the single precision version needs about half the memory of the double precision version.
Jdpd uses the Apache Commons Math and Apache Commons RNG libraries and is published as open source under the GNU General Public License version 3. This repository comprises the Java bytecode libraries (including the Apache Commons Math and RNG libraries), the Javadoc HTML documentation and the Netbeans source code packages including Unit tests.
Jdpd has been described in the scientific literature (the final manuscript 2018 - van den Broek - Jdpd - Final Manucsript.pdf is added to the repository) and used for DPD studies (see references below).
See text file JdpdVersionHistory.txt for a version history with more detailed information.
From https://github.com/zielesny/MFsim:
MFsim - An open Java all-in-one rich-client simulation environment for mesoscopic simulation
MFsim is an open Java all-in-one rich-client computing environment for mesoscopic simulation with Jdpd as its default simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD). The environment integrates and supports the complete preparation-simulation-evaluation triad of a mesoscopic simulation task. Productive highlights are a SPICES molecular structure editor, a PDB-to-SPICES parser for particle-based peptide/protein representations, a support of polymer definitions, a compartment editor for complex simulation box start configurations, interactive and flexible simulation box views including analytics, simulation movie generation or animated diagrams. As an open project, MFsim enables customized extensions for different fields of research.
MFsim uses several open libraries (see MFSimVersionHistory.txt for details and references below) and is published as open source under the GNU General Public License version 3 (see LICENSE).
MFsim has been described in the scientific literature and used for DPD studies (see references below).
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).
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.
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 disruptive nature of the changing media landscape and technology-driven advances in communication have led to innovative ways of organizing work in the information and communication industry. This reorganization of work is reflected in the concept of New Work, which rethinks working concepts, styles, and employee behavior. Based on a survey among staff in the information and communication industry (n = 380), this study investigates the status quo of the implementation of New Work measures and their effectiveness in helping companies reach organizational goals. The results show that New Work measures are widely adopted although there is still unused potential. Moreover, the study demonstrates that the implementation of New Work measures supports companies in achieving New Work goals as well as overall organizational goals in the contexts of agile management, change management, internal communication, and evaluation.
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.
This chapter is a commentary on Principle 20 of the United Nations Guiding Principles on Business and Human Rights (UNGPs). The UNGPs, endorsed by the United Nations Human Rights Council in 2011, are the first universally accepted framework for addressing business responsibilities for human rights. They outline State obligations to protect human rights, businesses’ responsibility to respect human rights, and the importance of both States and businesses offering adequate remedies for human rights breaches.
This chapter is a commentary on Principle 21 of the United Nations Guiding Principles on Business and Human Rights (UNGPs). The UNGPs, endorsed by the United Nations Human Rights Council in 2011, are the first universally accepted framework for addressing business responsibilities for human rights. They outline State obligations to protect human rights, businesses’ responsibility to respect human rights, and the importance of both States and businesses offering adequate remedies for human rights breaches.
Dephasing in quantum systems is typically the result of their interaction with environmental degrees of freedom. We investigate within a spin-boson model the influence of a super-Ohmic environment on the dynamics of a quantum two-state system. A super-Ohmic environment thereby models typical bulk phonons which are a common disturbance for solid state quantum systems as, for example, nitrogen-vacancy centers. By applying the numerically exact quasiadiabatic path-integral approach we show that for strong system-bath coupling, pseudocoherent dynamics emerges, i.e., oscillatory dynamics at short times due to slaving of the quantum system to the bath dynamics. We extend the phase diagram known for sub-Ohmic and Ohmic environments into the super-Ohmic regime and observe a pronounced nonmonotonous behavior. Super-Ohmic purely dephasing fluctuations strongly suppress the amplitude of coherent dynamics at very short times with no subsequent further decay at later times. Nevertheless, they render the dynamics overdamped. The corresponding phase separation line shows also a nonmonotonous behavior, very similar to the pseudocoherent dynamics.
We propose a quantum-mechanical model to calculate the current through a single molecular junction immersed in a solvent and surrounded by a thin shell of bound water under an applied ac voltage. The solvent plus hydration shell are captured by a dielectric continuum model for which the resulting spectral density is determined. Here the dielectric properties, e.g., the Debye relaxation time and the dielectric constant, of the bulk solvent and the hydration shell as well as the shell thickness directly enter. We determine the charge current through the molecular junction under an ac voltage in the sequential tunneling regime where we solve a quantum master equation by a real-time diagrammatic technique. Interestingly, the Fourier components of the charge current show an exponential-like decline when the hydration shell thickness increases. Finally, we apply our findings to binary solvent mixtures with varying volume fractions and find that the current is highly sensitive to both the hydration shell thickness as well as the volume fraction of the solvent mixture, giving rise to possible applications as shell and concentration sensors on the molecular scale.
Article 134 TFEU
(2023)
Article 135 TFEU
(2023)
Design and Development of a Bioreactor System for Mechanical Stimulation of Musculoskeletal Tissue
(2023)
We report on the development of a bioreactor system for mechanical stimulation of musculoskeletal tissues. The ultimate object is to improve the quality of medical treatment following injuries of the enthesis tissue. To this end, the tissue formation process through the effect of mechanical stimulation is investigated. A six-well system was designed, 3D printed and tested. An integrated actuator creates strain by applying a force. A contactless position sensor monitors the travels. An electronic circuit controls the bioreactor using a microcontroller. An IoT platform connects the microcontroller to a smartphone, enabling the user to alter variables, trigger actions and monitor the system. The system was stabilised by implementing two PID controllers and safety measures. The results show that the bioreactor design is suited to execute mechanical stimulation and to investigate the tissue formation and regeneration process …
Dem Thema Nachhaltigkeit kommt zunehmend eine größere Bedeutung zu. Dies liegt nicht zuletzt daran, dass die Pflicht, einen Nachhaltigkeitsbericht zu erstellen, mit dem Jahr 2024 auch auf viele kleine und mittlere Unternehmen ausgeweitet wird. Bislang trifft dies überwiegend auf große Unternehmen zu, welche in der Regel strukturell und hinsichtlich Software sehr gut für die Bewältigung dieser Aufgabe aufgestellt sind. Anders verhält es sich jedoch bei KMU, denn in diesen fehlen meist personelle und finanzielle Ressourcen sowie geeignete softwaretechnische Unterstützungswerkzeuge. In diesem Beitrag werden die Ergebnisse einer Studie der Westfälischen Hochschule vorgestellt, die sich auf das Nachhaltigkeitsreporting von KMU fokussiert. Darüber hinaus werden Herausforderungen aus Informationssicht erläutert und mögliche Unterstützungsbedarfe für KMU diskutiert. Ein Überblick über zukünftige Ansatzpunkte und eine abschließende Diskussion runden den Artikel ab.
In this paper, we investigate the influence of different disease groups on the size of different 1 anatomical structures. To this end, we first modify and improve an existing anatomical segmentation 2 model. Then, we use this model to segment 104 anatomical structures from computed tomography 3 (CT) scans and compute their volumes from the segmentation. After correlating the results with each 4 other, we find no new significant correlations. After correlating the volume data with known diseases 5 for each case, we find two weak correlations, one of which has not been described before and for 6 which we present a possible explanation.
Theoretischer Hintergrund: Mut ist ein bislang wenig erforschtes Konstrukt. In der Forschung existieren unterschiedliche Betrachtungsweisen und Annahmen, u.a. hinsichtlich der Art des Konstruktes oder der Einflussfaktoren. Es existieren zudem, insbesondere im deutschsprachigen Raum, nur wenige Instrumente zur Messung von Mut. Diese zeigen zudem oftmals verbesserungsfähige oder unzureichende psychometrische Gütekriterien.
Fragestellung: Auf Basis einer umfangreichen Literaturrecherche ist unser Ziel, neben einem wissenschaftlichen Beitrag zur Klärung des Konstruktes, einen Selbstbeschreibungsfragebogen zur Messung von Mut im Arbeitskontext zu konstruieren, welcher den gängigen psychometrischen Gütekriterien entspricht und perspektivisch im Rahmen der Personalauswahl und Personalentwicklung eingesetzt werden könnte.
Methodik: Der Erstentwurf des Selbstbeschreibungsfragebogens zu Mut im Arbeitskontext besteht aus den Dimensionen sozialer Mut und persönlicher Mut. Zur psychometrischen Überprüfung des Fragebogenentwurfs haben wir eine Querschnittstudie in Form einer Online-Befragung durchgeführt (N = 253). Der Fokus lag auf der Itemanalyse, sowie auf der Überprüfung der Reliabilität und der Validität.
Ergebnisse: Die Reliabilität beträgt α = .92 und α = .91. Die exploratorische Faktorenanalyse stützt das 2-Faktoren-Modell. Es existieren erwartungsgemäß signifikante positive Korrelationen mit inhaltsähnlichen Konstrukten, u.a. arbeitsplatzbezogene Selbstwirksamkeit oder Extraversion und negative signifikante Korrelationen zu Neurotizismus und Psychopathie. Zusätzlich zeigen Mittelwertsvergleiche für
Geschlecht und Führungsverantwortung Ergebnisse gemäß dem aktuellen Stand der Forschung.
Diskussion Der Selbstbeschreibungsfragebogen zeigt klares Potenzial für die Nutzung im Rahmen der Personalauswahl und Personalentwicklung. Im Rahmen der Fragebogenkonstruktion ist es entscheidend das Konstrukt so eng wie möglich einzugrenzen. Die Fokussierung auf eine spezifische Form von
Mut scheint der Schlüssel zu sein, um ein den gängigen Anforderungen an psychometrische Gütekriterien entsprechendes Instrument zu entwickeln.
Eine der ersten Informationen, die man von seinem Gegenüber wahrnehmen kann, ist meist das äußere Erscheinungsbild. Wird dies als attraktiv bewertet, wirkt es sich in vielen Lebensbereichen, wie auch im beruflichen Umfeld, vorteilhaft aus (Willis & Todorov, 2006; Marlowe et al., 1996; Langlois et al., 2000; Frieze et al., 1991). Im Rahmen der Bachelor-Thesis wurde der Einfluss physischer Attraktivität in Bezug
auf das Fehlverhalten von Mitarbeitenden in Form einer Vignettenstudie untersucht. Es wurden die folgenden Forschungsfragen formuliert: Werden attraktive Mitarbeitende trotz eines gezeigten Fehlverhaltens als vertrauenswürdiger eingeschätzt als unattraktive Mitarbeitende? Wird eine Bestrafung in Form einer Abmahnung und einer Kündigung bei unattraktiven Mitarbeitenden für angemessener gehalten als bei attraktiven Mitarbeitenden? Es wurde vermutet, dass sich auch hier die physische Attraktivität positiv auswirken kann.
Die postulierten Hypothesen wurden mit einem Stichprobenumfang von N = 679 im Between-Subjects Design eines Online-Experiments untersucht. Insgesamt gab es vier Vignetten, die sich in der Attraktivität einer dargestellten Mitarbeiterin und der Art des kontraproduktiven Arbeitsverhaltens unterschieden. Die Datenanalyse zeigte eine signifikante Interaktion zwischen der physischen Attraktivität und der Art des kontraproduktiven Arbeitsverhaltens auf, F(1,675) = 4.02, p = .046, η² = .01. Im Falle eines interpersonal schädigenden Arbeitsverhaltens wurde eine Kündigung bei der attraktiven Mitarbeiterin als angemessener bewertet. Im Falle eines organisationalschädigenden Arbeitsverhaltens hingegen wurde eine Kündigung bei der unattraktiven Mitarbeiterin als angemessener bewertet. Aus diesen Forschungsergebnissen wurden praktische Implikationen, wie zum Beispiel die Sensibilisierung für derartige Einflüsse durch Schulungen, abgeleitet. Auch Ansätze für zukünftige Forschungen, wie die Variation im Geschlecht der Stimulusperson, wurden vorgeschlagen.
Theoretischer Hintergrund: In der psychologischen Führungsforschung zeigt sich ein Shift von traditionellem Management hin zu progressiveren Führungsmodellen, in denen das Gemeinwohl und die nachhaltige Führung von Mitarbeitenden anstelle des Selbstinteresses von Führungskräften treten.
Diese Modelle bewegen sich allerdings weiter im traditionellen Paradigma, dass effektive Führung komplexe Systeme gezielt beeinflussen und auf erwünschte Zielzustände hin ausrichten kann.
Fragestellung: Folgt man dem systemischen Ansatz, so können Führungskräfte das organisationale System nicht beeinflussen, sondern lediglich die Relationen seiner Bestandteile und Rahmenbedingungen für Emergenz schaffen. So lässt es sich beispielsweise aus der Theorie komplexer adaptiver Systeme und dem darauf basierenden Complexity Leadership Ansatz ableiten. Wenngleich viele Wissenschaftler*innen hierin Potential effektiver Führung sehen, mangelt es doch an konzeptionellen und psychometrischen Grundlagen sowie empirischer Evidenz für die Effektivität systemischer Führung.
Methodik: Wir stellen einen Fremdbeschreibungsfragebogen zur Messung systemischer Führung vor (N ges = 8770) sowie die mit diesem Instrument gewonnenen Ergebnisse verschiedener Feldstudien (k = 28) zu Antezedenzien, Auswirkungen und Randbedingungen systemischer Führung. Wir berücksichtigen auch die inkrementelle Varianzaufklärung über transformationale Führung.
Ergebnisse: Das Systemic Leadership Inventory ermöglicht die Einschätzung systemischer Kompetenzen
von Führungskräften.
Diskussion: Zukünftige Forschung sollte sich mit der Entwickelbarkeit systemischer Führung beschäftigen. Limitationen unseres Forschungsprojekts werden diskutiert.
Der sozioanalytischen Theorie folgend argumentieren wir, dass Machiavellismus nur im Falle einer hohen emotionalen Einflusskompetenz zuträglich für den objektiven Karriereerfolg ist.
In den Daten unserer fragebogenbasierten Querschnittsstudie zum jährlichen Bruttoeinkommen mit N = 149 Mitarbeitenden aus der Privatwirtschaft zeigen sich unter Kontrolle von Alter, Geschlecht und Führungsspanne weder signifikante Haupteffekte für Machiavellismus, noch für emotionale Intelligenz, dafür aber ein hypothesenkonformer Interaktionseffekt.
Unter Berücksichtigung methodischer Limitationen, die vorrangig an die Messung der beiden die Studie konstituierenden Konstrukte geknüpft sind, werden wissenschaftliche und praktische Implikationen dieses Befunds diskutiert.
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.
Die neue Aufgabe der internen Kommunikation: schwierige Unternehmenspersönlichkeiten erkennen
(2023)
Measurement studies are essential for research and industry alike to understand the Web’s inner workings better and help quantify specific phenomena. Performing such studies is demanding due to the dynamic nature and size of the Web. An experiment’s careful design and setup are complex, and many factors might affect the results. However, while several works have independently observed differences in
the outcome of an experiment (e.g., the number of observed trackers) based on the measurement setup, it is unclear what causes such deviations. This work investigates the reasons for these differences by visiting 1.7M webpages with five different measurement setups. Based on this, we build ‘dependency trees’ for each page and cross-compare the nodes in the trees. The results show that the measured trees differ considerably, that the cause of differences can be attributed to specific nodes, and that even identical measurement setups can produce different results.
In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.
n-type silicon modules
(2023)
The photovoltaic industry is facing an exponential growth in the recent years fostered by a dramatic decrease in installation prices. This cost reduction is achieved by means of several mechanisms. First, because of the optimization of the design and installation process of current PV projects, and second, by the optimization, in terms of performance, in the manufacturing techniques and material combinations within the modules, which also has an impact on both, the installation process, and the levelized cost of electricity (LCOE).
One popular trend is to increase the power delivered by photovoltaic modules, either by using larger wafer sizes or by combining more cells within the module unit. This solution means a significant increase in the size of these devices, but it implies an optimization in the design of photovoltaic plants. This results in an installation cost reduction which turns into a decrease in the LCOE.
However, this solution does not represent a breakthrough in addressing the real challenge of the technology which affects the module requirements. The innovation efforts must be focused on improving the modules capability to produce energy without enlarging the harvesting area. This challenge can be faced by approaching some of the module characteristics which are summarized in this chapter.
Advanced Determination of Temperature Coefficients of Photovoltaic Modules by Field Measurements
(2023)
In this work data from outdoor measurements, acquired over the course of up to three years on commercially available solar panels, is used to determine the temperature coefficients and compare these to the information as stated by the producer in the data sheets. A program developed in MatLab App Designer allows to import the electrical and ambient measurement data. Filter algorithms for solar irradiance narrow the irradiance level down to ~1000 W/m2 before linear regression methods are applied to obtain the temperature coefficients. A repeatability investigation proves the accuracy of the determined temperature coefficients which are in good agreement to the supplier specification if the specified values for power are not larger than -0.3%/K. Further optimization is achieved by applying wind filter techniques and days with clear sky condition. With the big (measurement) data on hand it was possible to determine the change of the temperature coefficients for varying irradiance. As stated in literature we see an increase of the temperature coefficient of voltage and a decline for the temperature coefficient of power with increasing irradiance.
Nachhaltigkeit von intelligenten Gebäuden - Ein Blick auf die Gesetzgebungen und Praxismöglichkeiten
(2023)
Gebäude sind durch ihre Herstellung und den Betrieb für einen erheblichen Teil der CO2-Emissionen in Europa verantwortlich. Die EU und Deutschland wollen durch milliardenschwere Maßnahmenpakete diese Emissionen bis zum Jahr 2045 (Deutschland) bzw. 2050 (EU) auf null reduzieren. Neben der Gebäudehülle als maßgeblicher Faktor für die Wärmebilanz zum Heizen und Kühlen spielt die Gebäudeautomation eine wichtige Rolle. Wie Gebäude intelligenter und smarter werden und wie sich das auf die Energieeffizienz auswirkt, soll im Folgenden betrachtet werden.
Aufgrund der Energiewende und den steigenden Anforderungen an die technische Gebäudeausrüstung gewinnt der Betrieb von Wärmepumpen in Gebäuden immer mehr an Bedeutung. Inzwischen existiert eine Vielzahl an Wärmepumpen-Systemen, die unterschiedliche Vor- und Nachteile sowie Einsatzmöglichkeiten aufweisen. Sofern die Installation einer Wärmepumpe für den Wohngebäudesektor in Betracht gezogen wird, muss eruiert werden, welches System sowohl ökologisch als auch ökonomisch für das Bauvorhaben am sinnvollsten ist. Hierfür wurde eine Bewertungstool entwickelt, das den Einsatz der unterschiedlichen Wärmepumpensysteme bewertet und auch Nutzern mit wenig Expertise eine Entscheidungshilfe ermöglicht. Für eine möglichst ganzheitliche Betrachtung können verschiedene Szenarien mit Hilfe des Bewertungstools überprüft werden. Hierzu können Indikatoren wie Standortdaten, Gebäudedaten, Parameter für die Trinkwassererwärmung, die Systemtemperaturen der Heizung und die Betriebsweise der Wärmepumpe im Tool variiert werden. Die Ergebnisse des Bewertungstools zeigen, wie die unterschiedlichen Nutzungsanforderungen sich auf die Jahresarbeitszahl und den Energiebedarf auswirken. Zusätzlich werden Investitions- und Verbrauchskosten für die unterschiedlichen Szenarien abgeschätzt und berechnet. Bei der ökologischen Bewertung wird der Fokus der Betrachtung auf den TEWI-Wert gelegt, um den Einfluss von verschiedener Kältemittel im Lebenszyklus der Wärmepumpe zu berücksichtigen.
Jdpd - An open Java Simulation Kernel for Molecular Fragment Dissipative Particle Dynamics (DPD)
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems like MFsim.
Since Jdpd version 1.6.1.0 Jdpd is available in a (basic) double-precision version and a (derived) single-precision version (= JdpdSP) for all numerical calculations, where the single precision version needs about half the memory of the double precision version.
Jdpd uses the Apache Commons Math and Apache Commons RNG libraries and is published as open source under the GNU General Public License version 3. This repository comprises the Java bytecode libraries (including the Apache Commons Math and RNG libraries), the Javadoc HTML documentation and the Netbeans source code packages including Unit tests.
Jdpd has been described in the scientific literature (the final manuscript 2018 - van den Broek - Jdpd - Final Manucsript.pdf is added to the repository) and used for DPD studies (see references below).
See text file JdpdVersionHistory.txt for a version history with more detailed information.
MFsim - An open Java all-in-one rich-client simulation environment for mesoscopic simulation
MFsim is an open Java all-in-one rich-client computing environment for mesoscopic simulation with Jdpd as its default simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD). The environment integrates and supports the complete preparation-simulation-evaluation triad of a mesoscopic simulation task. Productive highlights are a SPICES molecular structure editor, a PDB-to-SPICES parser for particle-based peptide/protein representations, a support of polymer definitions, a compartment editor for complex simulation box start configurations, interactive and flexible simulation box views including analytics, simulation movie generation or animated diagrams. As an open project, MFsim enables customized extensions for different fields of research.
MFsim uses several open libraries (see MFSimVersionHistory.txt for details and references below) and is published as open source under the GNU General Public License version 3 (see LICENSE).
MFsim has been described in the scientific literature and used for DPD studies.
Fälle zum Europarecht
(2023)
Die Beschaffung von IT-Sicherheitslösungen ist für Unternehmen oft eine Herausforderung. So führt die Komplexität der Systeme dazu, dass die für eine Kaufentscheidung erforderlichen Kompetenzen und Informationen nicht immer vorhanden sind. Grundvoraussetzung für eine erfolgreiche Geschäftsbeziehung ist deswegen ein valides Vertrauensverhältnis zwischen Anwender- und Herstellerunternehmen. Das setzt jedoch voraus, dass die Herstellerunternehmen vertrauenswürdig auftreten und im Interesse ihrer Kunden handeln. Eine Studie der Westfälischen Hochschule Gelsenkirchen hat untersucht, welche Vertrauenskriterien Kunden bei Herstellern und deren Produkten wichtig sind. So ist zum Beispiel ein Hersteller bei den Kunden unten durch, wenn er zu viele Buzzwords nutzt.
Wie Datenräume helfen, neue Geschäftsmodelle zu entwickeln : sicher, vertrauenswürdig und dezentral
(2023)
In der heutigen Zeit werden sehr große Datenmengen generiert und verwaltet, dennoch wird der Wert der Daten in Deutschland und Europa noch nicht voll ausgeschöpft. Die gemeinsame Nutzung von Daten kann und soll datengetriebene Anwendungen noch weiter vorantreiben, bei der Erfüllung regulatorischer Anforderungen helfen sowie einen finanziellen Mehrwert für Firmen schaffen. Viele kleine bis mittelständische Unternehmen zögern derzeit jedoch, Daten untereinander auszutauschen, weil sie befürchten, die Hoheit über ihre Daten zu verlieren und nicht wissen, wer Zugriff auf die Daten hat und wofür die Daten verwendet werden.
Künstliche Intelligenz (KI) ermöglicht es, komplexe Zusammenhänge und Muster aus großen Datenmengen zu extrahieren und in einem statistischen Modell zu erfassen. Dieses KI-Modell kann anschließend Aussagen über zukünftig auftretende Daten treffen. Mit dem zunehmenden Einsatz von Künstlicher Intelligenz rücken solche Systeme auch immer mehr ins Visier von Cyberkriminellen. Der Artikel beschreibt umfassend Angriffsszenarien und mögliche Abwehrmaßnahmen.
Vor vier Jahren betrat die Datenschutz-Grundverordnung (DS-GVO) die Bühne und brachte für Unternehmen und Nutzer gleichermaßen Veränderungen mit sich. Doch gerade im dynamischen Umfeld des Online-Marketings tauchen ständig neue und oft knifflige Fragen auf – Fragen, die nun im Rahmen einer wissenschaftlichen Studie etwas genauer unter die Lupe genommen wurden.
Viele Unternehmen beginnen damit, Standards "wild" umzusetzen oder verschiedene Sicherheitsprodukte zu kaufen. Cybersicherheit muss aber auf einem soliden Fundament stehen. Dazu sollten die Verantwortlichen die eigenen Organisationsstrukturen kennen und die drei Schlüsselfaktoren Menschen, Prozesse und Technologie sorgfältig ausbalancieren.
Ventilanordnung und Verfahren zum Kühlen eines Wärmetauschers eines Fahrzeugs [Offenlegungsschrift]
(2023)
Die Erfindung betrifft eine Ventilanordnung umfassend wenigstens ein durch Gas, insbesondere Luft, durchströmbares Ventilelement (1, 1') mit einer Öffnung, wobei der Öffnung ein Dichtelement zugeordnet ist, mit dem die Öffnung verschließbar und/oder öffenbar ist, wobei das Dichtelement durch einen Dichtflächenbereich eines Plattenelements ausgebildet ist, vorzugsweise eines in zumindest einem möglichen Betriebszustand planen Plattenelements ausgebildet ist, wobei das Dichtelement der Öffnung des wenigstens einen Ventilelements gegenüberliegt und der das Dichtelement bildende Dichtflächenbereich des Plattenelements mittels wenigstens eines Spiralarmes mit einem Randflächenbereich des Plattenelements einstückig und relativ zum Randflächenbereich beweglich verbunden ist und der wenigstens eine Spiralarm von wenigstens einer um den Dichtflächenbereich verlaufenden spiralförmigen und durch Gas durchströmbare Ausnehmung in dem Plattenelement zumindest bereichsweise umgeben ist. Die Erfindung betrifft auch ein Verfahren zum Kühlen eines Wärmetauschers in
einem Fahrzeug.