Surrogate modeling in irreversible electroporation towards real-time treatment planning

In this paper, we develop surrogate models that can replace expensive predictive models and account for uncertainties in real-time treatment planning for irreversible electroporation of liver tumors. Standard non-intrusive surrogate modeling techniques that account for the model uncertainty and reduce the computational cost, such as polynomial chaos expansion and Gaussian process regression with conventional kernels, often do not capture the true physical behavior of the treatment outcome as required in the context of treatment planning. We improve the Gaussian process regression model by modifying the kernel function to a non-stationary Gibbs kernel with a support vector machine-based classifier in its length scale definition. This proposed model is compared with the standard surrogates in terms of their performance and accuracy. Our model is able to accurately replicate the behavior of the biophysics-based predictive model. There is a decrease of at least 81% in the overall root-mean-square error for treatment outcome when compared to the Gaussian process regression model with conventional kernels. Furthermore, we illustrate the application of the proposed surrogate model in treatment planning to address a voltage optimization problem for complete tumor ablation. Surrogate-assisted treatment planning exhibited good performance while maintaining similar levels of accuracy in comparison to treatment planning based on biophysical models. Finally, the effect of uncertainty in tissue electrical conductivities on the optimal voltage value is discussed.</p

Condition-Based Production for Stochastically Deteriorating Systems: Optimal Policies and Learning

Problem definition: Production systems deteriorate stochastically due to use and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system’s production rate. Although producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to tradeoff deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system and systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). Methodology/results: For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies, and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Numerical studies indicate that on average across a wide range of settings (i) condition-based production increases profits by 50% compared with static production, (ii) integrating condition-based production and maintenance decisions increases profits by 21% compared with the state-of-the-art sequential approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system’s production-deterioration relation. Managerial implications: Production should be adjusted dynamically based on real-time condition monitoring and the tactical maintenance planning should anticipate and integrate these operational decisions. Our proposed framework assists managers to do so optimally

Functional and biological activities of Edible Bird’s Nest (EBN) protein by proteomic and bioinformatic analyses

Edible Bird's Nest (EBN) is a traditional food in Southeast Asia that has been consumed for centuries. In addition to its high protein content, numerous researchers are now exploring the functional proteins of EBN, which have yet to be identified. The present study investigates the EBN proteome by integrating mass spectrometry with protein-based bioinformatics analysis. For protein recovery, three different precipitation techniques were employed; of these, the ammonium sulfate (AS) precipitation technique produced the highest protein yield (74.85%, p &lt; 0.05). The AS precipitation technique was effective in preserving the integrity of EBN proteins as revealed by protein electrophoresis. A total of 35 proteins were identified in the EBN-AS proteins. The predominant function of EBN-AS proteins is immunomodulation, which was further confirmed by their antioxidant [DPPH· activity (23.86%) and ABTS·+ activity (41.97%)], anti-inflammatory [inhibition of nitric oxide production (22.84%), and inhibition of albumin denaturation (19.48%)] assay. Hence, EBN-AS proteins have the potential to regulate the immune system and could be used as natural ingredients for the development of functional foods. Graphical Abstract: (Figure presented.).</p

Boosting thermochemical performance of SrBr₂·6H₂O with a secondary salt hydrate

This work systematically investigates the effect of 9 inorganic salt hydrates on the performance of strontium bromide (SrBr2) a thermochemical material (TCM). The goal is to boost the performance of this base salt by enhancing the reaction kinetics of the SrBr2 6-1 transition or by shrinking the reaction hysteresis. The study shows that the added salts that do not share a common ion with SrBr2 (LiCl, LiF, ZnF2, ZnI2, K2CO3) give limited to no benefits. The lack of improvement is due to a side reaction between SrBr2 and the added salt leading to the formation of new salt hydrate with low hygroscopicity that does not contribute to the thermochemical reaction. The addition of hygroscopic bromide salts with divalent cations (ZnBr2, CaBr2, MnBr2) gave mixed results depending on the sample history. The most likely cause is cation exchange between bromide salts occurring during exposure to high vapour pressures which promote ionic mobility. The overall best performance was achieved with the addition of LiBr, which we attribute to its high hygroscopicity.</p

Insights on Chemical Reactions and Formation Process of Electron Beam-Cured Acrylic Networks

In electron beam (EB) curing of cross-linked acrylic networks, the very high energies in play lead to several chemical phenomena unfolding sequentially and simultaneously. Hence, the formation and composition of cross-linked acrylic networks cured by EB irradiation are expected to be different compared to networks obtained by other curing methods. Herein, we characterize the distinct EB-triggered chemical reactions of a model aromatic epoxy diacrylate by analyzing the cross-linked films cured at different EB process parameters, i.e., EB curing dose and dose rate. The viscoelastic properties of the cross-linked films were evaluated by DMTA, and their respective sol/gel fractions were analyzed by SEC, 1H NMR, and MS. The cross-linked films differ in their viscoelastic properties and network composition. High EB irradiation parameters were found to trigger multiple chemical reactions, namely, the activation and cleavage of C-C bonds of isopropylidene groups and C-O bonds of hydroxyl, ether, and ester groups. Understanding the connection between EB curing conditions, reactions involved in the network creation and polymer properties can enable bottom-up approaches to material design and a more efficient use of the EB technology on multiple polymer chemistries.</p

Direct bandgap quantum wells in hexagonal Silicon Germanium

Silicon is indisputably the most advanced material for scalable electronics, but it is a poor choice as a light source for photonic applications, due to its indirect band gap. The recently developed hexagonal Si1−xGex semiconductor features a direct bandgap at least for x &gt; 0.65, and the realization of quantum heterostructures would unlock new opportunities for advanced optoelectronic devices based on the SiGe system. Here, we demonstrate the synthesis and characterization of direct bandgap quantum wells realized in the hexagonal Si1−xGex system. Photoluminescence experiments on hex-Ge/Si0.2Ge0.8 quantum wells demonstrate quantum confinement in the hex-Ge segment with type-I band alignment, showing light emission up to room temperature. Moreover, the tuning range of the quantum well emission energy can be extended using hexagonal Si1−xGex/Si1−yGey quantum wells with additional Si in the well. These experimental findings are supported with ab initio bandstructure calculations. A direct bandgap with type-I band alignment is pivotal for the development of novel low-dimensional light emitting devices based on hexagonal Si1−xGex alloys, which have been out of reach for this material system until now.</p

A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks

Simulating the evolution of the gravitational N-body problem becomes extremely computationally expensive as N increases since the problem complexity scales quadratically with the number of bodies. In order to alleviate this problem, we study the use of Artificial Neural Networks (ANNs) to replace expensive parts of the integration of planetary systems. Neural networks that include physical knowledge have rapidly grown in popularity in the last few years, although few attempts have been made to use them to speed up the simulation of the motion of celestial bodies. For this purpose, we study the advantages and limitations of using Hamiltonian Neural Networks to replace computationally expensive parts of the numerical simulation of planetary systems, focusing on realistic configurations found in astrophysics. We compare the results of the numerical integration of a planetary system with asteroids with those obtained by a Hamiltonian Neural Network and a conventional Deep Neural Network, with special attention to understanding the challenges of this specific problem. Due to the non-linear nature of the gravitational equations of motion, errors in the integration propagate, which may lead to divergence from the reference solution. To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate. Hamiltonian Neural Networks can make predictions that resemble the behavior of symplectic integrators but are challenging to train and in our case fail when the inputs differ ∼7 orders of magnitude. In contrast, Deep Neural Networks are easy to train but fail to conserve energy, leading to fast divergence from the reference solution. The hybrid integrator designed to include the neural networks increases the reliability of the method and prevents large energy errors without increasing the computing cost significantly. For the problem at hand, the use of neural networks results in faster simulations when the number of asteroids is ≳70.</p

DA-MUSIC:Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multisignal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance superresolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between the signals and the measurements and assumptions on the signals themselves (non-coherent, narrowband sources). As such, it is sensitive to model imperfections. In this work, we propose to overcome these limitations of MUSIC by augmenting the algorithm with specifically designed neural architectures. Our proposed deep augmented MUSIC (DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator, which leverages data to improve performance and robustness while preserving the interpretable flow of the classic method. DA-MUSIC is shown to learn to overcome limitations of the purely model-based method, such as its inability to successfully localize coherent sources as well as estimate the number of coherent signal sources present. We further demonstrate the superior resolution of the DA-MUSIC algorithm in synthetic narrowband and broadband scenarios as well as with real-world data of DoA estimation from seismic signals</p

Transient nucleation driven by solvent evaporation

We theoretically investigate homogeneous crystal nucleation in a solution containing a solute and a volatile solvent. The solvent evaporates from the solution, thereby continuously increasing the concentration of the solute. We view it as an idealized model for the far-out-of-equilibrium conditions present during the liquid-state manufacturing of organic electronic devices. Our model is based on classical nucleation theory, taking the solvent to be a source of the transient conditions in which the solute drops out of the solution. Other than that, the solvent is not directly involved in the nucleation process itself. We approximately solve the kinetic master equations using a combination of Laplace transforms and singular perturbation theory, providing an analytical expression for the nucleation flux. Our results predict that (i) the nucleation flux lags slightly behind a commonly used quasi-steady-state approximation. This effect is governed by two counteracting effects originating from solvent evaporation: while a faster evaporation rate results in an increasingly larger influence of the lag time on the nucleation flux, this lag time itself is found to decrease with increasing evaporation rate. Moreover, we find that (ii) the nucleation flux and the quasi-steady-state nucleation flux are never identical, except trivially in the stationary limit, and (iii) the initial induction period of the nucleation flux, which we characterize as a generalized induction time, decreases weakly with the evaporation rate. This indicates that the relevant time scale for nucleation also decreases with an increasing evaporation rate. Our analytical theory compares favorably with results from a numerical evaluation of the governing kinetic equations

A Digital Platform for Heterogeneous Fleet Management in Manufacturing Intralogistics

Brainport Industries Campus (BIC) is a unique initiative taken by the high-tech suppliers of the Eindhoven region to come together and work towards collaborative consumption of shared resources such as production machines, automated guided vehicles, clean rooms, and storage spaces in order to jointly meet their customers’ demands, reduce cost of ownership, and better manage risks during uncertain demand and supply cycles. However, the matching-demand-with-supply process in a sharing economy requires novel perspectives and tools. In this chapter, we propose a digital platform for serving multiple tenants of BIC, sharing a fleet of heterogeneous automated guided vehicles (AGVs). The proposed platform includes a user interface as an order input system, an order database, and a central cloud server that houses the necessary intelligence for communicating with all the aforementioned modules. This platform that serves multiple tenants sharing a heterogeneous AGV fleet is the first of its kind