The roles extracellular polymeric substances (EPS) play in mineral attachment and weathering were studied using genetically modified biofilms of the rock-inhabiting fungus Knufia petricola strain A95. Mutants deficient in melanin and/or carotenoid synthesis were grown as air-exposed biofilms. Extracted EPS were quantified and characterised using a combination of analytical techniques. The absence of melanin affected the quantity and composition of the produced EPS: mutants no longer able to form melanin synthesised more EPS containing fewer pullulan-related glycosidic linkages. Moreover, the melanin-producing strains attached more strongly to the mineral olivine and dissolved it at a higher rate. We hypothesise that the pullulan-related linkages, with their known adhesion functionality, enable fungal attachment and weathering. The released phenolic intermediates of melanin synthesis in the Δ sdh1 mutant might play a role similar to Fe-chelating siderophores, driving olivine dissolution even further. These data demonstrate the need for careful compositional and quantitative analyses of biofilm-created microenvironments.
The current research provides scientific evidence based on experimental and modeling approaches that complex softening processes characterized by dynamic recovery (DRV) and dynamic recrystallization (DRX) combined with twinning contribute efficiently to the hot deformation processing of the Al0.5CoCrFeNi alloy. An as-cast face-centered-cubic (fcc) oriented Al0.5CoCrFeNi dual-phase high entropy alloy, HEA, was deformed in uniaxial compression to a true strain 0.8 at temperatures between 900 and 1100 ºC and strain rates from 0.0013 to 0.1 s-1. A dynamic material model was applied to predict the processing windows, and the underlying deformation mechanisms were characterized using scanning electron microscopy and electron backscattered diffraction. The optimum processing window for the studied alloy is at 900–960 ºC/ 0.0013–0.002 s-1, where the eﬃciency of power dissipation and strain rate sensitivity ranges from 45 to 50% and 0.28 to 0.33, respectively. The processing map also exhibits a domain of ﬂow instability located in the lower temperature and higher strain rate regions, resulting mainly from flow localization. The studied alloy represents a dual-phase fcc + bcc/B2 microstructure in an as-cast (undeformed) state. The fcc matrix occupying 94 vol.% forms dendrites with an average diameter of 210 μm, decorated by discrete networks of bcc/B2 phase of average grain size 3.5 μm residing in interdendritic regions. The microstructural analyses corroborate the coincidence of DRX and twinning in the fcc phase in all deformed specimens. However, only DRV takes place within the bcc phase. The current study reveals a well-suited parameter range to achieve a high degree of hot deformability in dual-phase HEAs by taking advantage of twinning combined DRX to refine the microstructure significantly. Future work will have to identify possible application cases.
The European natural gas market is undergoing fundamental changes that foster uncertainty on both supply and demand sides. This uncertainty has elicited questions about the value of the Projects of Common Interest—strategic infrastructure investments supported by public funding from the European Union. This paper addresses this matter by deploying the adaptive robust optimization framework to consider long-term uncertainties in the gas infrastructure expansion planning problem. This framework confronts the drawbacks of mainstream methods of incorporating uncertainty in gas market models, such as stochastic scenario trees, in which the modeler predefines the probabilities and realization paths of unknown parameters. Our mathematical model endogenously identifies the unfortunate realizations of unknown parameters and suggests the optimal investment strategies to address them. We use this feature to assess which Projects of Common Interest are valuable in maintaining system resilience amid cold-winter demand spikes and supply shortages, while also considering budget constraints. The robust solutions indicate consistent preferences for specific projects. We find that real-world construction efforts have been focused on the most promising projects from a business perspective. However, we also find that the vast majority of projects are unlikely to be realized without financial support, even if they would serve as a hedge against stresses in the European gas system.
Team start-ups have substantial advantages over solo start-ups, but teams often do not live up to their potential due to conflicts creating unfavorable team dynamics. Based on an experiment with 665 individuals in 133 randomly composed teams participating in a new-venture simulation, we found team members’ deep-level characteristics that trigger motivations to act, i.e., achievement motivation and leadership orientations, to be particularly important sources of task conflicts. We found that diversity in leadership orientation reduces conflicts because not all team members can lead at the same time. Unlike hypothesized, we found that diversity in achievement motivation also reduces conflicts, for which, ex-post, we explore potential reasons. Furthermore, we demonstrate that the mediating task and relationship conflicts differently affect team outcomes due to their different nature of task-relatedness: while task conflicts affect task-related team efficacy and may escalate into relationship conflicts, relationship conflicts directly affect team satisfaction, but not team efficacy. Further emphasizing the importance of a motivational basis of conflicts, we found that individuals’ general self-efficacy, a more belief-related construct, affects team outcomes only through team efficacy, but not via conflicts.
The multiple traveling salespersons problem with moving targets is a generalization of the classical traveling salespersons problem, where the targets (nodes or objects) are moving over time. Additionally, for each target a visibility time window is given. The task is to find routes for several salespersons so that each target is reached exactly once within its visibility time window and the sum of all traveled distances of all salespersons is minimal. We present different modeling formulations for this TSP variant. The time requirements are modeled differently in each approach. Our goal is to examine what formulation is most suitable in terms of runtime to solve the multiple traveling salespersons problem with moving targets with exact methods. Computational experiments are carried out on randomly generated test instances to compare the different modeling approaches. The results for large-scale instances show, that the best way to model time requirements is to directly insert them into a formulation with discrete time steps.
Geothermal data collection is subject to fragmented reporting in global energy reviews. This problem arises from discussions about the future of the geothermal energy industry, its trends and how to represent it in the renewable energy transition. The aim of this study is to outline the process of global geothermal data collection and analyse the data reporting systems. A metamodel is proposed to run a comparative analysis between selected entities that report data both for geothermal power and geothermal heat. A complimentary statistical analysis is carried out on these entities. Our findings show considerable data inconsistency, a lack of standardized definitions and a misalignment of collection methodologies. This leads to incomplete geothermal data reporting. Specifically, the revision of geothermal heat data collection should be prioritized.
The beam inclination leads to a change in the laser spot size on the material surface. The higher the inclination, the larger the irradiated area and the lower the laser intensity. Moreover, if the material surface is outside of the beam focal plane, the intensity distribution profile becomes asymmetric. In this study, a heat source model, which calculates the intensity distribution on the workpiece surface as a function of beam parameters (beam waist, divergence half-angle) and process parameters (laser power, incidence angle, and distance to focal plane) was developed. The applicability of the heat source model was demonstrated by simulating 4 different laser hardening regimes. Once the heat efficiency coefficient had been calibrated the developed finite-element model allowed computation of temperatures while hardening with perpendicular laser beams as well as with inclined beams. The open-source software FEniCSx was used for the finite element computations. The mathematical formulation, required for performing temperature simulations with FEniCSx was briefly introduced.
In this study, an observer for estimating the state and unknown inputs is proposed for monitoring anaerobic digestion processes. This estimator is based on a dynamic model considering acidogenesis and methanogenesis, and consists of three sub-observers: (a) a gramian-based fixed-time convergent observer for the inlet chemical oxygen demand (COD) and the acidogenic bacteria population, (b) an asymptotic observer for the methanogenic bacteria population, and (c) a super-twisting observer for systems with time-varying parameters to estimate the inlet volatile fatty acid (VFA) concentration. These sub-observers can be designed independently, which greatly simplifies the tuning process. Proofs of convergence are developed and simulation tests show the performance of the estimation scheme as compared to classical extended Kalman filtering.
Socio-demographic characteristics of miners have been identified to play an important role in determining the distribution of costs and benefits in the mining sector. This study aims at providing an overview of the nature of the artisanal small-scale mining (ASM) sector in Ghana by focusing on the socio-economic characteristics of miners and their implications on the mining sector. Three mining communities representing the southern, middle, and northern belts of Ghana were selected. The mixed methods approach was used for the study. Questionnaires and interview guides were used to collect all necessary information. The study revealed that; ASM remains informal with the sector being dominated by the youth. Males dominate the sector and responsibilities in the industry are determined by one’s sex. There are regional differences in male–female participation and educational levels due to rural–urban differences with the existence of foreigners in artisanal mining in the country. The study recommends the incorporation of socio-demographic characteristics of miners into policy initiatives of the sector. A further study on how to make licensure procedures more attractive to ASM to help reduce the informality in the sector is also recommended.
One of the objectives fostered in medical science is the so-called precision medicine, which requires the analysis of a large amount of survival data from patients to deeply understand treatment options. Tools like machine learning (ML) and deep neural networks are becoming a de-facto standard. Nowadays, computing facilities based on the Von Neumann architecture are devoted to these tasks, yet rapidly hitting a bottleneck in performance and energy efficiency. The in-memory computing (IMC) architecture emerged as a revolutionary approach to overcome that issue. In this work, we propose an IMC architecture based on resistive switching memory (RRAM) crossbar arrays to provide a convenient primitive for matrix-vector multiplication in a single computational step. This opens massive performance improvement in the acceleration of a neural network that is frequently used in survival analysis of biomedical records, namely the DeepSurv. We explored how the synaptic weights mapping strategy and the programming algorithms developed to counter RRAM non-idealities expose a performance/energy trade-off. Finally, we discussed how this application is tailored for the IMC architecture rather than being executed on commodity systems.
The characterization of novel radiotracers toward their metabolic stability is an essential part of their development. While in vitro methods such as liver microsome assays or ex vivo blood or tissue samples provide information on overall stability, little or no information is obtained on cytochrome P450 (CYP) enzyme and isoform-specific contribution to the metabolic fate of individual radiotracers. Herein, we investigated recently established CYP-overexpressing hepatoblastoma cell lines (HepG2) for their suitability to study the metabolic stability of radiotracers in general and to gain insight into CYP isoform specificity. Wildtype HepG2 and CYP1A2-, CYP2C19-, and CYP3A4-overexpressing HepG2 cells were incubated with radiotracers, and metabolic turnover was analyzed. The optimized protocol, covering cell seeding in 96-well plates and analysis of supernatant by radio thin-layer-chromatography for higher throughput, was transferred to the evaluation of three 18F-labeled celecoxib-derived cyclooxygenase-2 inhibitors (coxibs). These investigations revealed time-dependent degradation of the intact radiotracers, as well as CYP isoform- and substrate-specific differences in their metabolic profiles. HepG2 CYP2C19 proved to be the cell line showing the highest metabolic turnover for each radiotracer studied here. Comparison with human and murine liver microsome assays showed good agreement with the human metabolite profile obtained by the HepG2 cell lines. Therefore, CYP-overexpressing HepG2 cells provide a good complement for assessing the metabolic stability of radiotracers and allow the analysis of the CYP isoform-specific contribution to the overall radiotracer metabolism.
Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.
Political economy factors are key to explain why some countries keep expanding their coal capacity. Yet, comparable cross-country evidence is scant. We consult 123 energy experts for eight major coal countries through an online survey, to assess which political economy factors affect coal-related policies. Regardless of the political or economic system, we find that the ministry for energy, the head of state and the ruling party are consistently the most important political actors, while utilities and mining companies are the most influential economic actors. Generally, other societal actors are the least influential. Economic growth, electricity system stability and low electricity costs are very relevant objectives the major arguments of pro-coal actors. The most relevant contextual factors are the influence of the power sector and structure of the power market. Actors, objectives, and contextual factors related to the environment are consistently less important. The insights of this study help identify entry points for politically feasible policies to phase-out coal.
It is well known that accurate current sharing and voltage regulation are both important, yet conflicting control objectives in multi-bus DC microgrids. In this paper a distributed control scheme is proposed, which simultaneously considers these two control objectives via a trade-off factor. This factor permits to adjust the degree of compromise between accurate voltage regulation and current sharing. At the same time, the voltage of a critical node can be precisely regulated. A sufficient condition for closed-loop stability is given and it is shown that the control parameters can always be chosen, such that stability is guaranteed. In addition, the steady state voltage and current deviations relative to their rated values are quantified via suitable metrics. For a given topology and settings of a DC microgrid, a sufficient condition for the existence of the trade-off factor is provided. The results are illustrated by simulation examples.
Wnt pathways are important for the modulation of tissue homeostasis, and their deregulation is linked to cancer development. Canonical Wnt signaling is hyperactivated in many human colorectal cancers due to genetic alterations of the negative Wnt regulator APC. However, the expression levels of Wnt-dependent targets vary between tumors, and the mechanisms of carcinogenesis concomitant with this Wnt signaling dosage have not been understood. In this study, we integrate whole-genome CRISPR/Cas9 screens with large-scale multi-omic data to delineate functional subtypes of cancer. We engineer APC loss-of-function mutations and thereby hyperactivate Wnt signaling in cells with low endogenous Wnt activity and find that the resulting engineered cells have an unfavorable metabolic equilibrium compared with cells which naturally acquired Wnt hyperactivation. We show that the dosage level of oncogenic Wnt hyperactivation impacts the metabolic equilibrium and the mitochondrial phenotype of a given cell type in a context-dependent manner. These findings illustrate the impact of context-dependent genetic interactions on cellular phenotypes of a central cancer driver mutation and expand our understanding of quantitative modulation of oncogenic signaling in tumorigenesis.
The goal of this work is to optimize five parameters of expected high impact on COSMO model performance in non-hydrostatic regime using a calibration method based on the so called Meta-Model. The domain of consideration covers the Central-Eastern Mediterranean area with a horizontal mesh of 0.03⁰ (~3.3 km). The optimization technique considers observations at twenty-two Greek and sixty-four Israeli meteorological stations of 24 h accumulated precipitation, minimum and maximum 2-m dry and dew-point temperatures at sixty dates spread over the year 2019, five for every month, in order to comply with the seasonal variability. The optimum model parameters found have been evaluated by application on an independent set of sixty days chosen from the same year-period and in the same manner. An overall model performance score improvement of order 5% was obtained for both sets of test cases. Better verification scores were found regarding almost all meteorological fields considered with respect to average values, mean absolute and standard deviation errors, as well as threat scores regarding precipitation. The use of relatively sparse local observations, instead of the gridded data sets available to places with dense meteorological station networks where the methodology has been successfully applied in numerical weather prediction and climatology, makes this approach, upon its successful realization, suitable to a significantly larger community of model users with modest observation resources. This approach is also expected to be of value for domains with a large marine area fraction, like the domain chosen, where observations are sporadic, if available, by nature as well as to regions of low population density.
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