Ni/Al multilayer reactions on nanostructured silicon substrates. - In: Journal of materials science, ISSN 1573-4803, Bd. 58 (2023), 31, S. 12811-12826
Fast energy release, which is a fundamental property of reactive multilayer systems, can be used in a wide field of applications. For most applications, a self-propagating reaction and adhesion between the multilayers and substrate are necessary. In this work, a distinct approach for achieving self-propagating reactions and adhesion between deposited Ni/Al reactive multilayers and silicon substrate is demonstrated. The silicon surface consists of random structures, referred to as silicon grass, which were created by deep reactive ion etching. Using the etching process, structure units of heights between 8 and 13 µm and density between 0.5 and 3.5 structures per µm^2 were formed. Ni and Al layers were alternatingly deposited in the stoichiometric ratio of 1:1 using sputtering, to achieve a total thickness of 5 µm. The analysis of the reaction and phase transformation was done with high-speed camera, high-speed pyrometer, and X-ray diffractometer. Cross-sectional analysis showed that the multilayers grew only on top of the silicon grass in the form of inversed cones, which enabled adhesion between the silicon grass and the reacted multilayers. A self-propagating reaction on silicon grass was achieved, due to the thermally isolating air pockets present around these multilayer cones. The velocity and temperature of the reaction varied according to the structure morphology. The reaction parameters decreased with increasing height and decreasing density of the structures. To analyze the exact influence of the morphology, further investigations are needed.
https://doi.org/10.1007/s10853-023-08794-9
A hybrid patch decomposition approach to compute an enclosure for multi-objective mixed-integer convex optimization problems. - In: Mathematical methods of operations research, ISSN 1432-5217, Bd. 0 (2023), 0, insges. 30 S.
In multi-objective mixed-integer convex optimization, multiple convex objective functions need to be optimized simultaneously while some of the variables are restricted to take integer values. In this paper, we present a new algorithm to compute an enclosure of the nondominated set of such optimization problems. More precisely, we decompose the multi-objective mixed-integer convex optimization problem into several multi-objective continuous convex optimization problems, which we refer to as patches. We then dynamically compute and improve coverages of the nondominated sets of those patches to finally combine them to obtain an enclosure of the nondominated set of the multi-objective mixed-integer convex optimization problem. Additionally, we introduce a mechanism to reduce the number of patches that need to be considered in total. Our new algorithm is the first of its kind and guaranteed to return an enclosure of prescribed quality within a finite number of iterations. For selected numerical test instances we compare our new criterion space based approach to other algorithms from the literature and show that much larger instances can be solved with our new algorithm.
https://doi.org/10.1007/s00186-023-00828-x
Data-informed reservoir computing for efficient time-series prediction. - In: Chaos, ISSN 1089-7682, Bd. 33 (2023), 7, 073109, S. 073109-1-073109-11
We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available. We demonstrate our approach using a delay-based RC as the machine learning component in conjunction with sparse identification of nonlinear dynamical systems for the data-driven model component. We test the performance on two example systems: the Lorenz system and the Kuramoto-Sivashinsky system. Our results indicate that our proposed technique can yield an improvement in the time-series forecasting capabilities compared with both approaches applied individually, while remaining computationally cheap. The benefit of our proposed approach, compared with pure RC, is most pronounced when the reservoir parameters are not optimized, thereby reducing the need for hyperparameter optimization.
https://doi.org/10.1063/5.0152311
3D passive microfluidic valves in silicon and glass using grayscale lithography and reactive ion etching transfer. - In: Microfluidics and nanofluidics, ISSN 1613-4990, Bd. 27 (2023), 8, 55, S. 1-12
A fabrication strategy for high-efficiency passive three-dimensional microfluidic valves with no mechanical parts fabricated in silicon and glass substrates is presented. 3D diffuser-nozzle valve structures were produced and characterized in their added value in comparison to conventional diffuser-nozzle valve designs with rectangular cross sections. A grayscale lithography approach for 3D photoresist structuring combined with a proportional transfer by reactive ion etching allowed to transfer 3D resist valve designs with high precision into the targeted substrate material. The efficiency with respect to the rectification characteristics or so-called diodicity of the studied valve designs is defined as the ratio of the pressure drops in backward and forward flow directions. The studied valve designs were characterized experimentally as well as numerically based on finite element simulations with overall matching results that demonstrate a significantly improved flow rectification of the 3D valves compared to the corresponding conventional structure. Our novel 3D valve structures show, for instance, even without systematic optimization a measured diodicity of up to 1.5 at low flow rates of only about 10 μl/s.
https://doi.org/10.1007/s10404-023-02663-2
MLflow2PROV: extracting provenance from machine learning experiments. - In: Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning (DEEM), (2023), 9, insges. 4 S.
Supporting iterative and explorative workflows for developing machine learning (ML) models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly used to simplify the structured collection and management of ML artifacts, such as ML models, metadata, and code. However, EMSs typically suffer from limited provenance capabilities. As a consequence, it is hard to analyze provenance information and gain knowledge that can be used to improve both ML models and their development workflows. We propose a W3C-PROV-compliant provenance model capturing ML experiment activities that originate from Git and MLflow usage. Moreover, we present the tool MLflow2PROV that extracts provenance graphs according to our model, enabling querying, analyzing, and further processing of collected provenance information.
https://doi.org/10.1145/3595360.3595859
Processing-in-Memory for databases: query processing and data transfer. - In: 19th International Workshop on Data Management on New Hardware, (DaMoN 2023), June 19th 2023, (2023), S. 107-111
The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and - in this way - relieving the CPU from processing. Particularly, in in-memory databases memory access becomes a performance bottleneck. Thus, PIM seems to offer an interesting solution for database processing. In this work, we investigate how commercially available PIM technology can be leveraged to accelerate query processing by offloading (parts of) query operators to memory. Furthermore, we show how to address the problem of limited PIM storage capacity by interleaving transfer and computation and present a cost model for the data placement problem.
https://doi.org/10.1145/3592980.3595323
Recommender systems in continuing professional education for public transport: challenges of a human-centered design. - In: Adjunct proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, (2023), S. 331-336
Continuous training is an essential building block to avoid workforce shortage in the public transport sector in Germany. However, the personnel requirements in this sector are highly diverse, similar to the education history of the employees. Therefore, more and more specialized continuous training offers arise, which are, on the one hand, more and more personalized but also make it more challenging to find suitable offers for the individual. Specialized recommender systems for this niche application might be a possible solution. This paper presents current work-in-progress results towards such a system and, in particular, the requirements for the recommender systems from the users’ perspective. We conducted guided interviews with industry representatives focusing on the usage-oriented expectations in recommender systems for an online platform for offerings of continuing education in the area of public transport. The resulting usage requirements form the basis for the concluding literature review of recommender systems in the special application domain. The results show that especially the challenges of small communities with limited content and multiple profiles are not sufficiently addressed in the development of recommender systems, such that existing solutions are not applicable in this niche area.
https://doi.org/10.1145/3563359.3596995
From explainable AI to explainable simulation: using machine learning and XAI to understand system robustness. - In: ACM SIGSIM-PADS 2023, (2023), S. 96-106
Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.
https://doi.org/10.1145/3573900.3591114
Boosting material utilization via direct growth of Zn2(V3O8)2 on the carbon cloth as a cathode to achieve a high-capacity aqueous zinc-ion battery. - In: Small, ISSN 1613-6829, Bd. 19 (2023), 46, 2303307, S. 1-10
Aqueous zinc-ion batteries (AZIBs) have attracted the attention of researchers because of their high theoretical capacity and safety. Among the many vanadium-based AZIB cathode materials, zinc vanadate is of great interest as a typical phase in the dis-/charge process. Here, a remarkable method to improve the utilization rate of zinc vanadate cathode materials is reported. In situ growth of Zn2(V3O8)2 on carbon cloth (CC) as the cathode material (ZVOCC) of AZIBs. Compared with the Zn2(V3O8)2 cathode material bonded on titanium foil (ZVO@Ti), the specific capacity increases from 300 to 420 mAh g−1, and the utilization rate of the material increases from 69.60% to 99.2%. After the flexible device is prepared, it shows the appropriate specific capacity (268.4 mAh g−1 at 0.1 A g−1) and high safety. The method proposed in this work improves the material utilization rate and enhances the energy density of AZIB and also has a certain reference for the other electrochemical energy storage devices.
https://doi.org/10.1002/smll.202303307
A test instance generator for multiobjective mixed-integer optimization. - In: Mathematical methods of operations research, ISSN 1432-5217, Bd. 0 (2023), 0, insges. 26 S.
Application problems can often not be solved adequately by numerical algorithms as several difficulties might arise at the same time. When developing and improving algorithms which hopefully allow to handle those difficulties in the future, good test instances are required. These can then be used to detect the strengths and weaknesses of different algorithmic approaches. In this paper we present a generator for test instances to evaluate solvers for multiobjective mixed-integer linear and nonlinear optimization problems. Based on test instances for purely continuous and purely integer problems with known efficient solutions and known nondominated points, suitable multiobjective mixed-integer test instances can be generated. The special structure allows to construct instances scalable in the number of variables and objective functions. Moreover, it allows to control the resulting efficient and nondominated sets as well as the number of efficient integer assignments.
https://doi.org/10.1007/s00186-023-00826-z