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Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities
No full textLithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-the-art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization
Incorporation of Low Concentrations of Gold Nanoparticles: Complex Effects on Radiation Response and Fate of Cancer Cells
No full text(1) Background: In oncology research, a long-standing discussion exists about pros and cons of metal nanoparticle-enhanced radiotherapy and real mechanisms behind the tumor cell response to irradiation (IR) in presence of gold nanoparticles (GNPs). A better understanding of this response is, however, necessary to develop more efficient and safety nanoparticle (NP) types designed to disturb specific processes in tumor cells. (2) Aims and Methods: We combined 3D confocal microscopy and super-resolution single molecule localization microscopy (SMLM) to analyze, at the multiscale, the early and late effects of 10 nm-GNPs on DNA double strand break (DSB) induction and repair in tumor cells exposed to different doses of photonic low-LET (linear energy transfer) radiation. The results were correlated to different aspects of short and long-term cell viability. SkBr3 breast cancer cells (selected for the highest incidence of this cancer type among all cancers in women, and because most breast tumors are treated with IR) were incubated with low concentrations of GNPs and irradiated with 60Co γ-rays or 6 MV X-rays. In numerous post-irradiation (PI) times, ranging from 0.5 to 24 h PI, the cells were spatially (3D) fixed and labeled with specific antibodies against γH2AX, 53BP1 and H3K9me3. The extent of DSB induction, multi-parametric micro- and nano-morphology of γH2AX and 53BP1 repair foci, DSB repair kinetics, persistence of unrepaired DSBs, nanoscale clustering of γH2AX and nanoscale (hetero)chromatin re-organization were measured by means of the mentioned microscopy techniques in dependence of radiation dose and GNP concentration. (3) Results: The number of γH2AX/53BP1 signals increased after IR and an additional increase was observed in GNP-treated (GNP(+)) cells compared to untreated controls. However, this phenomenon reflected slight expansion of the G2-phase cell subpopulation in irradiated GNP(+) specimens instead of enhanced DNA damage induction by GNPs. This statement is further supported by some micro- and nano-morphological parameters of γH2AX/53BP1 foci, which slightly differed for cells irradiated in absence or presence of GNPs. At the nanoscale, Ripley’s distance frequency analysis of SMLM signal coordinate matrices also revealed relaxation of heterochromatin (H3K9me3) clusters upon IR. These changes were more prominent in presence of GNPs. The slight expansion of radiosensitive G2 cells correlated with mostly insignificant but systematic decrease in post-irradiation survival of GNP(+) cells. Interestingly, low GNP concentrations accelerated DSB repair kinetics; however, the numbers of persistent γH2AX/53BP1 repair foci were slightly increased in GNP(+) cells. (4) Conclusions: Low concentrations of 10-nm GNPs enhanced the G2/M cell cycle arrest and the proportion of radiosensitive G2 cells, but not the extent of DNA damage induction. GNPs also accelerated DSB repair kinetics and slightly increased presence of unrepaired γH2AX/53BP1 foci at 24 h PI. GNP-mediated cell effects correlated with slight radiosensitization of GNP(+) specimens, significant only for the highest radiation dose tested (4 Gy)
Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic?
No full textIn this study, we empirically show the dynamics of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) to consist of an almost equally divided combination of stochastic and deterministic chaos, rendering the series unpredictable after expiration of the Lyapunov time, resulting in futile forecasting attempts. We achieve a clear distinction of the true nature of the underlying time series dynamics by applying a novel and combinatory chaos analysis framework comparing the wavelet-filtered S&P 500 returns with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. Furthermore, we are the first to show the strange attractor of especially the daily-frequented S&P 500 return system graphically via Takens´ embedding and by spectral embedding in combination with Laplacian Eigenmaps. Finally, we critically discuss implications and future prospects in terms of financial forecasting
Optical, structural, and functional properties of highly reflective and stable iridium mirror coatings for infrared applications
No full textMetallic coatings are essential for numerous optical systems due to their high and broadband refl ectivity in the infrared spectral range. In contrast to well-established (protected)silver and gold mirror coatings, iridium is environmentally durable, referring to ISO 9211-3 and thermally stable up to 600 °C even without protective layers, as demonstrated. Additionally,the optical and related structural properties of atomic layer deposited (ALD) and magnetron sputtered (MS) Ir coatings were investigated using spectrophotometry, FTIR, ellipsometry, WLI,AFM, XRR, XRD, SEM, and electrical resistivity measurements. The properties of Ir ALD and Ir MS coatings diff er due to their topography and microstructure
CO2-Bindung durch Hydrokulturpflanzen und Cyanobakterien - ein Vergleich im Büro Innenraum
No full textIn dem vorliegenden Bericht wird die Veränderung der Innenraumluft durch den Einsatz einer Hydrokultur Pflanzenwand und einem Cyanobakterien Bild im Büroraum verglichen
Tool Wear and Milling Characteristics for Hybrid Additive Manufacturing Combining Laser Powder Bed Fusion and In Situ High-Speed Milling
No full textWe report on milling and tool wear characteristics of hybrid additive manufacturing comprising laser powder bed fusion and in situ high-speed milling, a particular process in which the cutter mills inside the powder bed without any cooling lubricant being applicable. Flank wear is found to be the dominant wear characteristic with its temporal evolution over utilization period revealing the typical s-shaped dependence. The flank wear land width is measured by microscopy and correlated to the achievable surface roughness of milled 3D-printed parts, showing that for flank wear levels up to 100 μm a superior surface roughness below 3 μm is accessible for hybrid additive manufacturing. Further, based on this correlation recommended tool, life scenarios can be deduced. In addition, by optimizing the finishing tool start position and the number of afore-built layers, the milling process is improved with respect to the maximum millable angle for undercut surfaces of 3D-printed parts to 30° for the roughing process and to 40° for the entire machining process including finishin
Maschinelle Lernverfahren zur videobasierten Intentionserkennung von Radfahrern mit stationären Kameras
No full textAutomatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers
No full textThis paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different
industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting
the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using
parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of
the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a
RL control concept, not considering time-variant parameters
Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments
No full textAyahuasca is a blend of Amazonian plants that has been used for traditional medicine by
the inhabitants of this region for hundreds of years. Furthermore, this plant has been demon� strated to be a viable therapy for a variety of neurological and mental diseases. EEG experi� ments have found specific brain regions that changed significantly due to ayahuasca. Here,
we used an EEG dataset to investigate the ability to automatically detect changes in brain
activity using machine learning and complex networks. Machine learning was applied at
three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of
the EEG time series, and (C) the complex network measures calculated from (B). Further, at
the abstraction level of (C), we developed new measures of complex networks relating to
community detection. As a result, the machine learning method was able to automatically
detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed
by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are
more important for the detection of ayahuasca. The most activated areas were the frontal
and temporal lobe, which is consistent with the literature. F3 and PO4 were the most impor� tant brain connections, a significant new discovery for psychedelic literature. This connec� tion may point to a cognitive process akin to face recognition in individuals during
ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assorta� tivity were the most important complex network measures. These two measures are also
associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic
mechanism. Moreover, the new measures were crucial to the predictive model and sug� gested larger brain communities associated with the use of ayahuasca. This suggests that
the dissemination of information in functional brain networks is slower when this drug is
present. Overall, our methodology was able to automatically detect changes in brain activity
during ayahuasca consumption and interpret how these psychedelics alter brain networks,
as well as provide insights into their mechanisms of actio
UV-Femtosecond-Laser Structuring of Cyclic Olefin Copolymer
No full textWe report on the laser ablation of cyclic olefin copolymer using an amplified ultrashort pulsed laser in the ultraviolet spectral range. In addition to a high ablation depth per laser-structured layer up to 74 μm at a fluence of 22 J cm−2, an excellent mean roughness
of laser-patterned surfaces down to 0.5 μm is demonstrated. Furthermore, with increasing fluence, increasing ablation efficiencies up to 2.5 mm3 W−1 min−1 are determined. Regarding the quality of the ablation, we observed steep ablation flanks and low debris formation, though for fluences above 10.5 J cm−2 the formation of troughs was observed, being attributed to multiple reflections on the ablation flanks. For comparison, laser ablation was performed under identical conditions with an infrared laser wavelength. The results highlight that UV ablation exhibits significant advantages in terms of ablation efficiency, surface roughness and quality. Moreover, our results show that a larger UV focus spot accelerates the ablation process with comparable quality, paving the way for high-power UV ultrashort pulsed lasers towards an efficient and qualitative tool for the laser machining of cyclic olefin copolymer. The production of complex microfluidics further underlines the suitability of this type of laser