Modern energy infrastructure has evolved into an integrated electricity and natural gas systems (IEGS), which often encompasses multiple geographically-diverse energy areas. This paper focuses on the decentralized adjustable robust operation problem for multi-area IEGS. Existing distributed algorithms usually require synchronization of all area subproblems, which is hard to scale and could result in under-utilization of computation resources due to the heterogeneity of local areas. To address those limitations, this paper proposes an asynchronous alternating direction method of multipliers (ADMM) based decentralized model for multi-area IEGS. This asynchronous decentralized structure only requires local communications and allows each area to perform local updates with information from a subset of but not all neighbors, where the individual areas’ subproblems are solved independently and asynchronously. Meanwhile, the linear decision rules (LDRs)-based adjustable robust operation model is tailored to combine with the automatic generation control (AGC) systems to fully exploit its potential in dealing with renewable energy uncertainty. Numerical results illustrate the effectiveness of the proposed method.
In this article, the manufacturing and toughening effects on the material properties of epoxy adhesives used in wind turbine rotor blades are presented. Different adhesive materials are developed by combining SPABOND™ 820HTA (non-toughened) and SPABOND™ 840HTA (toughened) adhesives with the machine and manual mixing methods. Firstly, the manufacturing quality are compared between the two methods, in terms of void percentage and void volume using micro-computed tomography. Dynamical Mechanical analysis, uniaxial tensile testing, V-notch shear testing and single-edge-notch beam testing are carried out to evaluate the manufacturing and toughening effects. In these experiments, the digital image correlation technique is exploited to obtain the displacement and strain data. Origin ProⓇ and MATLAB R2021bⓇ are utilized for technical data analysis, plotting, smoothing, filtering, and averaging. The obtained data could be used to select the adhesive material based on the strength and stiffness requirements, develop failure criteria, and predict the thick adhesive joint behavior by finite element modeling.
We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dimensions. We adopt a Galerkin formulation-based method, in polygonal domains with a finite number of straight sides. Trial spaces used in the Galerkin discretization of the BIEs are built by using NNs that, in turn, employ the so-called Rectified Linear Units (ReLU) as the underlying activation function. The ReLU-NNs used to approximate the solutions to the BIEs depend nonlinearly on the parameters characterizing the NNs themselves. Consequently, the computation of a numerical solution to a BIE by means of ReLU-NNs boils down to a fine tuning of these parameters, in network training. We argue that ReLU-NNs of fixed depth and with a variable width allow us to recover well-known approximation rate results for the standard Galerkin Boundary Element Method (BEM). This observation hinges on existing well-known properties concerning the regularity of the solution of the BIEs on Lipschitz, polygonal boundaries, i.e. accounting for the effect of corner singularities, and the expressive power of ReLU-NNs over different classes of functions. We prove that shallow ReLU-NNs, i.e. networks having a fixed, moderate depth but with increasing width, can achieve optimal order algebraic convergence rates. We propose novel loss functions for NN training which are obtained using computable, local residual a posteriori error estimators with ReLU-NNs for the numerical approximation of BIEs. We find that weighted residual estimators, which are reliable without further assumptions on the quasi-uniformity of the underlying mesh, can be employed for the construction of computationally efficient loss functions for ReLU-NN training. The proposed framework allows us to leverage on state-of-the-art computational deep learning technologies such as TENSORFLOW and TPUs for the numerical solution of BIEs using ReLU-NNs. Exploratory numerical experiments validate our theoretical findings and indicate the viability of the proposed ReLU-NN Galerkin BEM approach.
Defective and perfect sites naturally exist within electronic semiconductors, and considerable efforts to reduce defects to improve the performance of electronic devices, especially in hybrid organic-inorganic perovskites (ABX3 ), have been undertaken. Herein, foldable hole transporting materials (HTMs) were developed, and extend the wavefunctions of A-site cations of perovskite which as hybridized electronic states link the trap states (defective site) and valance band edge (perfect site) between the naturally defective and perfect sites of perovskite surface, finally converting the discrete trap states of perovskite as the continuous valance band to reduce the trap recombination. Tailoring the foldability of the HTMs tunes the wavefunctions between defective and perfect surface sites, allowing the power conversion efficiency of a small cell to reach 23.22% and that of a mini-module (6.5×7 cm2 , active area = 30.24 cm2 ) to get as high as 21.71% with a fill factor of 81%, the highest value reported for non-spiro-OMeTAD-based perovskite solar modules. This article is protected by copyright. All rights reserved.
Background Unilateral spatial neglect (USN) is a debilitating neuropsychological syndrome that often follows brain injury, in particular a stroke affecting the right hemisphere. In current clinical practice, the assessment of neglect is based on old-fashioned paper-and-pencil and behavioral tasks, and sometimes relies on the examiner’s subjective judgment. Therefore, there is a need for more exhaustive, objective and ecological assessments of USN. Methods In this paper, we present two tasks in immersive virtual reality to assess peripersonal and extrapersonal USN. The tasks are designed with several levels of difficulty to increase sensitivity of the assessment. We then validate the feasibility of both assessments in a group of healthy adult participants. Results We report data from a study with a group of neurologically unimpaired participants (N = 39). The results yield positive feedback on comfort, usability and design of the tasks. We propose new objective scores based on participant’s performance captured by head gaze and hand position information, including, for instance, time of exploration, moving time towards left/right and time-to-reach, which could be used for the evaluation of the attentional spatial bias with neurological patients. Together with the number of omissions, the new proposed parameters can result in lateralized index ratios as a measure of asymmetry in space exploration. Conclusions We presented two innovative assessments for USN based on immersive virtual reality, evaluating the far and the near space, using ecological tasks in multimodal, realistic environments. The proposed protocols and objective scores can help distinguish neurological patients with and without USN.
The Company of Biologists' 2022 workshop on 'Cell State Transitions: Approaches, Experimental Systems and Models' brought together an international and interdisciplinary team of investigators spanning the fields of cell and developmental biology, stem cell biology, physics, mathematics and engineering to tackle the question of how cells precisely navigate between distinct identities and do so in a dynamic manner. This second edition of the workshop was organized after a successful virtual workshop on the same topic that took place in 2021.
Baryonic Acoustic Oscillations (BAOs) studies based on the clustering of voids and matter tracers provide important constraints on cosmological parameters related to the expansion of the Universe. However, modelling the void exclusion effect is an important challenge for fully exploiting the potential of this kind of analyses. We thus develop two numerical methods to describe the clustering of cosmic voids. Neither model requires additional cosmological information beyond that assumed within the galaxy de-wiggled model. The models consist in power spectra whose performance we assess in comparison to a parabolic model on Patchy cubic and light-cone mocks. Moreover, we test their robustness against systematic effects and the reconstruction technique. The void model power spectra and the parabolic model with a fixed parameter provide strongly correlated values for the Alcock-Paczynski (α) parameter, for boxes and light-cones likewise. The resulting α values – for all three models – are unbiased and their uncertainties are correctly estimated. However, the numerical models show less variation with the fitting range compared to the parabolic one. The Bayesian evidence suggests that the numerical techniques are often favoured compared to the parabolic model. Moreover, the void model power spectra computed on boxes can describe the void clustering from light-cones as well as from boxes. The same void model power spectra can be used for the study of pre- and post-reconstructed data-sets. Lastly, the two numerical techniques are resilient against the studied systematic effects. Consequently, using either of the two new void models, one can more robustly measure cosmological parameters.
One of the major challenges of coupled problems is to manage nonconforming meshes at the interface between two models and/or domains, due to different numerical schemes or domain discretizations employed. Moreover, very often complex submodels depend on (e.g., physical or geometrical) parameters, thus making the repeated solutions of the coupled problem through high-fidelity, full-order models extremely expensive, if not unaffordable. In this paper, we propose a reduced order modeling (ROM) strategy to tackle parametrized one-way coupled problems made by a first, master model and a second, slave model; this latter depends on the former through Dirichlet interface conditions. We combine a reduced basis method, applied to each subproblem, with the discrete empirical interpolation method to efficiently interpolate or project Dirichlet data across either conforming or non-conforming meshes at the domains interface, building a low-dimensional representation of the overall coupled problem. The proposed technique is numerically verified by considering a series of test cases involving both steady and unsteady problems, after deriving a posteriori error estimates on the solution of the coupled problem in both cases. This work arises from the need to solve staggered cardiac electrophysiological models and represents the first step towards the setting of ROM techniques for the more general two-way Dirichlet-Neumann coupled problems solved with domain decomposition sub-structuring methods, when interface non-conformity is involved.
Simplifying the manufacturing processes of renewable energy technologies is crucial to lowering the barriers to commercialization. In this context, to improve the manufacturability of perovskite solar cells (PSCs), we have developed a one-step solution-coating procedure in which the hole-selective contact and perovskite light absorber spontaneously form, resulting in efficient inverted PSCs. We observed that phosphonic or carboxylic acids, incorporated into perovskite precursor solutions, self-assemble on the indium tin oxide substrate during perovskite film processing. They form a robust self-assembled monolayer as an excellent hole-selective contact while the perovskite crystallizes. Our approach solves wettability issues and simplifies device fabrication, advancing the manufacturability of PSCs. Our PSC devices with positive–intrinsic–negative (p-i-n) geometry show a power conversion efficiency of 24.5% and retain >90% of their initial efficiency after 1,200 h of operating at the maximum power point under continuous illumination. The approach shows good generality as it is compatible with different self-assembled monolayer molecular systems, perovskites, solvents and processing methods.
Near-surface negatively charged nitrogen vacancy (NV) centers hold excellent promise for nanoscale magnetic imaging and quantum sensing. However, they often experience charge-state instabilities, leading to strongly reduced fluorescence and NV coherence time, which negatively impact magnetic imaging sensitivity. This occurs even more severely at 4 K and ultrahigh vacuum (UHV, p = 2 × 10-10 mbar). We demonstrate that in situ adsorption of H2O on the diamond surface allows the partial recovery of the shallow NV sensors. Combining these with band-bending calculations, we conclude that controlled surface treatments are essential for implementing NV-based quantum sensing protocols under cryogenic UHV conditions.
One key bottleneck of solid-state NMR spectroscopy is that 1H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest 1H NMR resolution available today in rigid solids. Here, using a deep-learning method, we extend the PIP approach to a second dimension, and for samples of L-tyrosine hydrochloride and ampicillin we obtain high resolution 1H-1H double-quantum/single-quantum dipolar correlation and spin-diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks.
Nitrogen limitation is the foundation of stable coral-algal symbioses. Diazotrophs, prokaryotes capable of fixing N2 into ammonia, support the productivity of corals in oligotrophic waters, but could contribute to the destabilization of holobiont functioning when overstimulated. Recent studies on reef-building corals have shown that labile dissolved organic carbon (DOC) enrichment or heat stress increases diazotroph abundance and activity, thereby increasing nitrogen availability and destabilizing the coral-algal symbiosis. However, the (a)biotic drivers of diazotrophs in octocorals are still poorly understood. We investigated diazotroph abundance (via relative quantification of nifH gene copy numbers) in two symbiotic octocorals, the more mixotrophic soft coral Xenia umbellata and the more autotrophic gorgonian Pinnigorgia flava, under (i) labile DOC enrichment for 21 days, followed by (ii) combined labile DOC enrichment and heat stress for 24 days. Without heat stress, relative diazotroph abundances in X. umbellata and P. flava were unaffected by DOC enrichment. During heat stress, DOC enrichment (20 and 40 mg glucose l⁻¹) increased the relative abundances of diazotrophs by sixfold in X. umbellata and fourfold in P. flava, compared with their counterparts without excess DOC. Our data suggest that labile DOC enrichment and concomitant heat stress could disrupt the nitrogen limitation in octocorals by stimulating diazotroph proliferation. Ultimately, the disruption of nitrogen cycling may further compromise octocoral fitness by destabilizing symbiotic nutrient cycling. Therefore, improving local wastewater facilities to reduce labile DOC input into vulnerable coastal ecosystems may help octocorals cope with ocean warming.
Symbiotic cnidarians such as corals and anemones form highly productive and biodiverse coral reef ecosystems in nutrient-poor ocean environments, a phenomenon known as Darwin's paradox. Resolving this paradox requires elucidating the molecular bases of efficient nutrient distribution and recycling in the cnidarian-dinoflagellate symbiosis. Using the sea anemone Aiptasia, we show that during symbiosis, the increased availability of glucose and the presence of the algae jointly induce the coordinated up-regulation and relocalization of glucose and ammonium transporters. These molecular responses are critical to support symbiont functioning and organism-wide nitrogen assimilation through glutamine synthetase/glutamate synthase-mediated amino acid biosynthesis. Our results reveal crucial aspects of the molecular mechanisms underlying nitrogen conservation and recycling in these organisms that allow them to thrive in the nitrogen-poor ocean environments.
Majority of the past research on application of machine learning (ML) in earthquake engineering focused on contrasting the predictive performance of different ML algorithms. In contrast, the emphasis of this paper is on the use of data to boost the predictive performance of surrogates. To that end, a novel data engineering methodology for seismic collapse risk assessment is proposed. This method, termed the automated collapse data constructor (ACDC), stems from combined understanding of the ground motion characteristics and the collapse process. In addition, the data-driven collapse classifier (D2C2) methodology is proposed which enables conversion of the collapse data from a regression format to a classification format. The D2C2 methodology can be used with any classification tool, and it allows estimation of seismic collapse capacities in a way analogous to the incremental dynamic analysis. The proposed methodologies are tested in a case study using decision trees (XGBoost) and neural network classi-fiers with an extensive dataset of collapse responses of a 4-story and an 8-story steel moment resisting frames. The results suggest that the ACDC methodology allows for dramatic improvement of the predictive performance of data-driven tools while at the same time significantly reducing data requirements. Specifically , the proposed method can reduce the number of ground motions required for collapse risk assessment from at least forty, as traditionally used, to less than twenty motions. Moreover, interpretation of feature importance conforms with the engineering understanding while revealing a novel, period-dependent measure of ground motion duration. All data and code developed in this research are made openly available. K E Y W O R D S 'data-centric' machine learning, data engineering, seismic collapse risk, surrogate modelling This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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