Hysteresis and phase transition in many-particle storage systems

We study the behavior of systems consisting of ensembles of interconnected storage particles. Our examples concern the storage of lithium in many-particle electrodes of rechargeable lithium-ion batteries and the storage of air in a system of interconnected rubber balloons. We are particularly interested in those storage systems whose constituents exhibit non-monotone material behavior leading to transitions between two coexisting phases and to hysteresis. In the current study we consider the case that the time to approach equilibrium of a single storage particle is much smaller than the time for full charging of the ensemble. In this regime the evolution of the probability to find a particle of the ensemble in a certain state, may be described by a nonlocal conservation law of Fokker-Planck type. Two constant parameter control whether the ensemble transits the 2-phase region along a Maxwell line or along a hysteresis path or if the ensemble shows the same non-monotone behavior as its constituents

Using Reference Models for Data Warehouse Metadata Management

Based on experience from applying the Common Warehouse Metamodel to metadata management at a large Swiss bank, the applicability of metadata reference models in complex application domains is analyzed. In order to explain the context of the case, the state-of-the-art of the Common Warehouse Metamodel and its application for data warehouse management are summarized. Based on the project experience documented in this paper, benefits of the reference model approach are described and recommendations for future developments of the Common Warehouse Metamodel are proposed

Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing

The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC

Hysteresis and phase transition in many-particle storage systems

We study the behavior of systems consisting of ensembles of interconnected storage particles. Our examples concern the storage of lithium in many-particle electrodes of rechargeable lithium-ion batteries and the storage of air in a system of interconnected rubber balloons. We are particularly interested in those storage systems whose constituents exhibit non-monotone material behavior leading to transitions between two coexisting phases and to hysteresis. In the current study we consider the case that the time to approach equilibrium of a single storage particle is much smaller than the time for full charging of the ensemble. In this regime the evolution of the probability to find a particle of the ensemble in a certain state, may be described by a nonlocal conservation law of Fokker-Planck type. Two constant parameter control whether the ensemble transits the 2-phase region along a Maxwell line or along a hysteresis path or if the ensemble shows the same non-monotone behavior as its constituents

Plasma-borne indicators of inflammasome activity in Parkinson’s disease patients

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons of the substantia nigra. Inflammation and cell death are recognized aspects of PD suggesting that strategies to monitor and modify these processes may improve the management of the disease. Inflammasomes are pro-inflammatory intracellular pattern recognition complexes that couple these processes. The NLRP3 inflammasome responds to sterile triggers to initiate pro-inflammatory processes characterized by maturation of inflammatory cytokines, cytoplasmic membrane pore formation, vesicular shedding, and if unresolved, pyroptotic cell death. Histologic analysis of tissues from PD patients and individuals with nigral cell loss but no diagnosis of PD identified elevated expression of inflammasome-related proteins and activation-related “speck” formation in degenerating mesencephalic tissues compared with controls. Based on previous reports of circulating inflammasome proteins in patients suffering from heritable syndromes caused by hyper-activation of the NLRP3 inflammasome, we evaluated PD patient plasma for evidence of inflammasome activity. Multiple circulating inflammasome proteins were detected almost exclusively in extracellular vesicles indicative of ongoing inflammasome activation and pyroptosis. Analysis of plasma obtained from a multi-center cohort identified elevated plasma-borne NLRP3 associated with PD status. Our findings are consistent with others indicating inflammasome activity in neurodegenerative disorders. Findings suggest mesencephalic inflammasome protein expression as a histopathologic marker of early-stage nigral degeneration and suggest plasma-borne inflammasome-related proteins as a potentially useful class of biomarkers for patient stratification and the detection and monitoring of inflammation in PD

Digitalization Platform for Sustainable Battery Cell Production: Coupling of Process, Production, and Product Models

Lithium-ion batteries are used in a wide range of applications, with the electromobility sector being the main contributor to the increasing demand predicted for the next decade. Although batteries play an important role in decarbonizing the transportation sector, their production includes energy-intensive processes that hinder a more sustainable production. Moreover, the production processes are characterized by a manifold of parameters leading to complex cause–effect relations along the process chain which influences the battery cell quality. Therefore, a sustainable future for battery production and the electromobility sector depends on the environmentally and economically efficient production of high-performance batteries. Against this background, this work presents a digitalization platform based on the coupling of mechanistic models to digitally reproduce the battery cell production and provide a deeper understanding of the interdependencies on the process, production, and product levels. In addition to a description of the individual models contained in the platform, this work demonstrates their coupling on a use case to study the effects of different solids contents of the coating suspension. Besides providing a multilevel assessment of the parameter interdependencies, considering quality, environmental and economic aspects, the presented framework contributes to knowledge-based decision support and improvement of production and battery cell performance

High Gene Flow on a Continental Scale in the Polyandrous Kentish Plover Charadrius alexandrinus

Gene flow promotes genetic homogeneity of species in time and space. Gene flow can be modulated by sex-biased dispersal that links population genetics to mating systems. We investigated the phylogeography of the widely distributed Kentish plover Charadrius alexandrinus. This small shorebird has a large breeding range spanning from Western Europe to Japan and exhibits an unusually flexible mating system with high female breeding dispersal. We analysed genetic structure and gene flow using a 427-bp fragment of the mitochondrial (mtDNA) control region, 21 autosomal microsatellite markers and a Z microsatellite marker in 397 unrelated individuals from 21 locations. We found no structure or isolation-by-distance over the continental range. However, island populations had low genetic diversity and were moderately differentiated from mainland locations. Genetic differentiation based on autosomal markers was positively correlated with distance between mainland and each island. Comparisons of uniparentally and biparentally inherited markers were consistent with female-biased gene flow. Maternally inherited mtDNA was less structured, whereas the Z-chromosomal marker was more structured than autosomal microsatellites. Adult males were more related than females within genetic clusters. Taken together, our results suggest a prominent role for polyandrous females in maintaining genetic homogeneity across large geographic distances.Organismic and Evolutionary Biolog

The acetylation of RelA in Lys310 dictates the NF-κB-dependent response in post-ischemic injury

The activation of nuclear factor kappa B (NF-κB) p50/RelA is a key event in ischemic neuronal injury, as well as in brain ischemic tolerance. We tested whether epigenetic mechanisms affecting the acetylation state of RelA might discriminate between neuroprotective and neurotoxic activation of NF-κB during ischemia. NF-κB activation and RelA acetylation were investigated in cortices of mice subjected to preconditioning brain ischemia or lethal middle cerebral artery occlusion (MCAO) and primary cortical neurons exposed to preconditioning or lethal oxygen-glucose deprivation (OGD). In mice subjected to MCAO and in cortical neurons exposed to lethal OGD, activated RelA displayed a high level of Lys310 acetylation in spite of reduced total acetylation. Also, acetylated RelA on Lys310 interacted strongly with the CREB-binding protein (CBP). Conversely, RelA activated during preconditioning ischemia appeared deacetylated on Lys310. Overexpressing RelA increased Bim promoter activity and neuronal cell death both induced by lethal OGD, whereas overexpressing the acetylation-resistant RelA-K310R, carrying a mutation from Lys310 to arginine, prevented both responses. Pharmacological manipulation of Lys310 acetylation by the sirtuin 1 activator resveratrol repressed the activity of the Bim promoter and reduced the neuronal cell loss. We conclude that the acetylation of RelA in Lys310 dictates NF-κB-dependent pro-apoptotic responses and represents a suitable target to dissect pathological from neuroprotective NF-κB activation in brain ischemia

Stochastic many-particle model for LFP electrodes

In the framework of non-equilibrium thermodynamics, we derive a new model for many-particle electrodes. The model is applied to LiFePO4 (LFP) electrodes consisting of many LFP particles of nanometer size. The phase transition from a lithium-poor to a lithium-rich phase within LFP electrodes is controlled by both different particle sizes and surface fluctuations leading to a system of stochastic differential equations. An explicit relation between battery voltage and current controlled by the thermodynamic state variables is derived. This voltage–current relation reveals that in thin LFP electrodes lithium intercalation from the particle surfaces into the LFP particles is the principal rate-limiting process. There are only two constant kinetic parameters in the model describing the intercalation rate and the fluctuation strength, respectively. The model correctly predicts several features of LFP electrodes, viz. the phase transition, the observed voltage plateaus, hysteresis and the rate-limiting capacity. Moreover we study the impact of both the particle size distribution and the active surface area on the voltage–charge characteristics of the electrode. Finally we carefully discuss the phase transition for varying charging/discharging rates