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Towards Understanding Prolate 4 Monomers: Numerical Predictions and Experimental Validation of Electronic Properties and Slow Relaxation in a Muffin-shaped Er Complex
No full textWe report the synthesis, crystal structure and magnetic properties of the muffin-shaped complex [Er(PPTMP)(HO)][OTf] (PPTMP = (4-(6-(1,10-phenanthrolin-2-yl)pyridin-2-yl)-1H-1,2,3-triazol-1-yl)methyl pivalate) (\one). Complex \one\ is shown to exhibit field-induced slow relaxation of the magnetisation at ~T via two distinct relaxation paths. Using tunable high-frequency/high-field electron paramagnetic resonance spectroscopy we experimentally determine the effective -factors and zero field splittings of the two energetically lowest Kramers doublets (KD). Our data reveal that the distorted muffin-shaped ligand field favours an magnetic ground state, while the main contribution to the first excited KD at ~GHz is suggested to be . The ground state -tensor has generally axial form but hosts significant transversal components, which we conclude to be the source of SMM-silent behaviour in zero field. Our findings are backed up by \textit{ab-initio} spin-orbit configuration interaction calculations showing excellent agreement with the experimental data and in particular highlight that the counterions should be included in the numerical modelling of the crystalline structure
Methodology for the Electromagnetic Design of Superconducting Accelerator Magnets based on the Integrated Product Engineering Model - iPeM = Methodik für den elektromagnetischen Entwurf von supraleitenden Beschleunigermagneten auf der Grundlage des integrierten Produktentstehungsmodells - iPeM
No full textIn dieser Arbeit wird eine Methodik für den Entwurf der elektromagnetischen Aspekte von supraleitenden Beschleunigermagneten vorgestellt. Diese Methodik basiert auf dem integrated Product engineering Model (iPeM) und beschreibt die formalisierten Entwurfsschritte während des Simulationsprozesses eines solchen Magneten. Zur Implementierung der Methodik in aktuelle Simulationsabläufe wird ein Software-Tool, der sogenannte Magnet Model Management Layer (3ML), konzipiert und implementiert. Das Dissertationsprojekt wird am CERN durchgeführt, welches im Zentrum der Entwicklung supraleitender Beschleunigermagnete steht, um die Infrastruktur für internationale HEP-Experimente bereitzustellen. Die Dissertation hat das Ziel, die Lücke der fehlenden organisationsweiten, ganzheitlichen Methodiken Entwicklungsunterstützung in diesem Bereich im Allgemeinen und am CERN im Besonderen zu schließen.
Entwicklungsherausforderungen in der Domäne, dem CERN als Organisation und in der CERN TE-MSC Gruppe werden identifiziert und quantifiziert. Diese Herausforderungen und ihr Einfluss aufeinander werden visualisiert und Erfolgskriterien für zukünftige Entwicklungsprojekte in diesem Bereich abgeleitet. Wissensmanagement und -transfer werden als kritische Herausforderung im Entwicklungsprozess für supraleitende Magnete identifiziert. Basierend auf diesen Erkenntnissen werden die Methodik, der Tool-Support und die Integration durchgeführt. Eine Methodik für den Entwurf elektromagnetischer Magnete wird vorgestellt. Diese Methodik steht im Einklang mit den bestehenden Systems Engineering (SE) und Product Management (PM) Standards am CERN. Ein Tool-Support wird entwickelt und im Rahmen eines Validierungstests eingeführt, um den die Methodik den Anwendern zu vermitteln. Mit dem Fokus auf Wissensmanagement wurde der Magnet Model Management Layer (3ML) mit einer Webschnittstelle implementiert, die es allen Benutzern ermöglicht Simulationsmodelle zu organisieren und Referenzsystemelemente zu identifizieren, ohne ein spezielles Simulationswerkzeug öffnen zu müssen. Das Routine for the Optimization of magnet X-sections, Inverse field calculation and coil End design (ROXIE) Programm wird als Beispiel verwendet, um die Integration von 3ML in reale Simulations-Arbeitsabläufe zu demonstrieren. Es werden fortgeschrittene Modellierungsanwendungsfälle erläutert, die durch die neue Software ermöglicht werden.
Der Ausblick zeigt das Verbesserungspotenzial von 3ML auf und motiviert zu weiteren Tests und Validierungsstudien
Review of weighting methods for life cycle impact assessment under GLAM
No full textPurpose
Weighting is the process of assigning relative importance to life cycle inventory results or indicator results across impact categories, using weighting factors based on value choices. It is an optional step within Life Cycle Assessment (LCA) but plays an important role in interpreting and communicating the relative importance of different environmental impacts. As part of the Global LCIA Guidance (GLAM) project under the UN Life Cycle Initiative, a comprehensive review of weighting methods was conducted to better understand which approaches are most appropriate for different applications in LCA.
Methods
Members of the GLAM weighting subtask identified and reviewed twenty-seven weighting methods. These methods were grouped into four categories: Multiple Criteria Decision Analysis (MCDA), monetary, data-driven and distance-to-target methods. Classifiers based on inherent features of the weighting methods were applied to support their inclusion or exclusion from further considerations. Each method then was assessed against a set of evaluation criteria defined by the subtask members. A color-code system (green, yellow or red) was applied to indicate the degree to which each method met each criterion to facilitate comparison and communication.
Results and discussion
Each method was briefly described with appropriate references, including examples of usage in LCA studies where available. The review results are summarized in a table that highlights the performance of each method against the evaluation criteria. All monetary methods are classified as trade-off rates, whereas there are MCDA methods and data-driven methods that can be either trade-off rates or importance coefficients. All distance-to-target methods are classified as importance coefficients. The ability of each method to incorporate temporal discounting or cultural differentiation varies, depending on the data availability and study design. None of the methods reviewed fully met all evaluation criteria, especially within the scope of the GLAM project. Some criteria (like Scientific validity) are sufficiently met by almost all of these methods.
Conclusions
Existing weighting methods based on different approaches have both advantages and limitations. No single method is universally sufficient, and their validity depends on context. This comprehensive overview of available weighting methods provides a valuable starting point for practitioners seeking to identify suitable weighting method for specific LCA applications. To facilitate easy use, a software was also developed based on this review to support the selection of the most appropriate weighting method for LCA studies
Optimizing evacuation via budget constrained maximum dynamic flow with speed variation and intermediate storage
No full textDuring any type of disaster, managing the evacuation of people at risk and planning humanitarian support constitute a critical challenge due to the presence of heavy traffic congestion in urban areas. Among them, flow maximization and time minimization models in bi-directional contraflow network have been emerging in addressing these issues. Resource limitation is one of the critical issues in such scenarios. The main objective of this work is to maximize the number of evacuees by best utilizing budget allocation and improving speed adjustment, which minimize congestion during evacuation. Since a limited budget is available, a set of bottleneck arcs is first identified, and then the budget is optimally allocated to some of these arcs to increase their capacities within given space bounds. The remaining arcs are then updated with new speed adjustment, where the travel time should be reduced. In this model, the flow is increased by settling evacuees in intermediate shelters, intended for those who may not reach the final destination due to network capacity or permissible time window constraints. The presented algorithms are polynomial, and their validity is proved. This novel approach could be a milestone in saving lives and reducing traffic congestion, as it offers both theoretical and practical value and contributes to more effective traffic management during emergencies, special events, and rush hour periods. To demonstrate their efficacy, the proposed models are applied to a real-world network case study of the Kathmandu Valley
Operational Convection‐Permitting COSMO/ICON Ensemble Predictions at Observation Sites ( CIENS )
No full textWe present the CIENS dataset, which contains ensemble weather forecasts from the operational convection-permitting numerical weather prediction model of the German Weather Service. It comprises forecasts for 55 meteorological variables mapped to the locations of synoptic stations, as well as additional spatially aggregated forecasts from surrounding grid points, available for a subset of these variables. Forecasts are available at hourly lead times from 0 to 21 h for two daily model runs initialised at 00 and 12 UTC, covering the period from December 2010 to June 2023. Additionally, the dataset provides station observations for six key variables at 170 locations across Germany: pressure, temperature, hourly precipitation accumulation, wind speed, wind direction, and wind gusts. Since the forecasts are mapped to the observed locations, the data is delivered in a convenient format for analysis. The CIENS dataset complements the growing collection of benchmark datasets for weather and climate modelling. A key distinguishing feature is its long temporal extent, which encompasses multiple updates to the underlying numerical weather prediction model and thus supports investigations into how forecasting methods can account for such changes. In addition to detailing the design and contents of the CIENS dataset, we outline potential applications in ensemble post-processing, forecast verification, and related research areas. A use case focused on ensemble post-processing illustrates the benefits of incorporating the rich set of available model predictors into machine learning-based forecasting models
A PAC-Bayes Oracle Inequality for Sparse Neural Networks
No full textWe study the Gibbs posterior distribution for sparse deep neural nets in a nonparametric regression setting. The posterior can be accessed via Metropolis-adjusted Langevin algorithms. Using a mixture over uniform priors on sparse sets of network weights, we prove an oracle inequality which shows that the method adapts to the unknown regularity and hierarchical structure of the regression function. The estimator achieves the minimax-optimal rate of convergence (up to a logarithmic factor)
RelExt: A new dark matter tool for the exploration of dark matter models
No full textWe present the C++ program RelExt for Standard Model (SM) extensions that feature a Dark Matter (DM) candidate. The tool allows to efficiently scan the parameter spaces of these models to find parameter combinations that lead to relic density values which are compatible with the measured value within the uncertainty specified by the user. The code computes the relic density for freeze-out (co-)annihilation processes. The user can choose between several pre-installed models or any arbitrary other model featuring a discrete symmetry, by solely providing the corresponding FeynRules model files. The code automatically generates the required (co-)annihilation amplitudes and thermally averaged cross sections, including the total widths in the s-channel mediators, and solves the Boltzmann equation to determine the relic density. It can easily be linked to other tools like e.g. ScannerS to check for the relevant theoretical and experimental constraints, or to BSMPT to investigate the phase history of the model and possibly related gravitational waves signals
Towards a Comprehensive Virtual Metrology Framework: Integrating AutoML, Data Integration, Uncertainty Quantification & Model Maintenance
No full textDespite the potential of Virtual Metrology (VM), integrated frameworks combining standardized data handling, automated model development, and uncertainty quantification remain rare. This paper presents a scalable VM architecture that leverages Asset Administration Shells (AAS) for data integration, AutoML for modeling, and a practical UQ approach. We propose a novel but practically applicable method that connects GUM principles with ML-based uncertainty, aiming to support informed architectural decisions and foster robust, interpretable, and scalable VM deployment in industrial environments
