Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes. © 2017 Elsevier Ltd.PKK and SL acknowledge a grant (16AWMP- B066761-04) from the AWMP Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government and the support from Future Research Program (2E27030) funded by the Korea Institute of Science and Technology (KIST). PKK and RJ acknowledge a MISTI Global Seed Funds award. MD acknowledges the support of the European Research Council (ERC) through the project MHetScale (617511). TLB acknowledges the support of European Research Council (ERC) through the project Re- activeFronts (648377). RJ acknowledges the support of the US Department of Energy through a DOE Early Career Award (grant DE-SC0009286). The data to reproduce the work can be obtained from the corresponding author.N

Robust multimodal fusion network using adversarial learning for brain tumor grading

© 2022Background and Objective: Gliomas are graded using multimodal magnetic resonance imaging, which provides important information for treatment and prognosis. When modalities are missing, the grading is degraded. We propose a robust brain tumor grading model that can handle missing modalities. Methods: Our method was developed and tested on Brain Tumor Segmentation Challenge 2017 dataset (n = 285) via nested five-fold cross-validation. Our method adopts adversarial learning to generate the features of missing modalities relative to the features obtained from a full set of modalities in the latent space. An attention-based fusion block across modalities fuses the features of each available modality into a shared representation. Our method's results are compared to those of two other models where 15 missing-modality scenarios are explicitly considered and a joint training approach with random dropouts is used. Results: Our method outperforms the two competing methods in classifying high-grade gliomas (HGGs) and low-grade gliomas (LGGs), achieving an area under the curve of 87.76% on average for all missing-modality scenarios. The activation maps derived with our method confirm that it focuses on the enhancing portion of the tumor in HGGs and on the edema and non-enhancing portions of the tumor in LGGs, which is consistent with prior expertise. An ablation study shows the added benefits of a fusion block and adversarial learning for handling missing modalities. Conclusion: Our method shows robust grading of gliomas in all cases of missing modalities. Our proposed network might have positive implications in glioma care by learning features robust to missing modalities.11Nsciescopu

NoHammer: Preventing Row Hammer with Last-Level Cache Management

Row Hammer (RH) is a circuit-level phenomenon where repetitive activation of a DRAM row causes bit-flips in adjacent rows. Prior studies that rely on extra refreshes to mitigate RH vulnerability demonstrate that bit-flips can be prevented effectively. However, its implementation is challenging due to the significant performance degradation and energy overhead caused by the additional extra refresh for the RH mitigation. To overcome challenges, some studies propose techniques to mitigate the RH attack without relying on extra refresh. These techniques include delaying the activation of an aggressor row for a certain amount of time or swapping an aggressor row with another row to isolate it from victim rows. Although such techniques do not require extra refreshes to mitigate RH, the activation delaying technique may result in high-performance degradation in false-positive cases, and the swapping technique requires high storage overheads to track swap information. We propose NoHammer, an efficient RH mitigation technique to prevent the bit-flips caused by the RH attack by utilizing Last-Level Cache (LLC) management. NoHammer temporarily extends the associativity of the cache set that is being targeted by utilizing another cache set as the extended set and keeps the cache lines of aggressor rows on the extended set under the eviction-based RH attack. Along with the modification of the LLC replacement policy, NoHammer ensures that the aggressor row's cache lines are not evicted from the LLC under the RH attack. In our evaluation, we demonstrate that NoHammer gives 6% higher performance than a baseline without any RH mitigation technique by replacing excessive cache misses caused by the RH attack with LLC hits through sophisticated LLC management, while requiring 45% less storage than prior proposals. © 2023 IEEE.FALS

Visibly transparent amorphous silicon via passivation of dangling bonds enabling low-loss photonic platforms at the visible frequencies

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Tailored refractive index of hydrogenated amorphous silicon for dielectric metasurface

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GreenDIMM: OS-Assisted DRAM Power Management for DRAM with a Sub-Array Granularity Power-Down State

Power and energy consumed byDRAMcomprising main memory of data-center servers have increased substantially as the capacity and bandwidth of memory increase. Especially, the fraction of DRAM background power in DRAM total power is already high, and it will continue to increase with the decelerating DRAM technology scaling as we will have to plug more DRAM modules in servers or stack more DRAM dies in a DRAM package to provide necessary DRAM capacity in the future. To reduce the background power, we may exploit low average utilization of the DRAM capacity in data-center servers (i.e., 40 C60%) for DRAM power management. Nonetheless, the current DRAM power management supports lowpower states only at the rank granularity, which becomes ineffective with memory interleaving techniques devised to disperse memory requests across ranks. That is, ranks need to be frequently woken up from low-power states with aggressive power management, which can significantly degrade system performance, or they do not get a chance to enter low-power states with conservative power management. To tackle such limitations of the current DRAM power management, we propose GreenDIMM, OS-assisted DRAM power management. Specifically, GreenDIMM first takes a memory block in physical address space mapped to a group of DRAM sub-arrays across every channel, rank, and bank as a unit of DRAM power management. This facilitates fine-grained DRAM power management while keeping the benefit of memory interleaving techniques. Second, GreenDIMM exploits memory on-/off-lining operations of the modern OS to dynamically remove/add memory blocks from/to the physical address space, depending on the utilization of memory capacity at run-time. Third, GreenDIMM implements a deep powerdown state at the sub-array granularity to reduce the background power of the off-lined memory blocks. As the off-lined memory blocks are removed from the physical address space, the sub-arrays will not receive any memory request and stay in the power-down state until the memory blocks are explicitly on-lined by the OS. Our evaluation with a commercial server running diverse workloads shows that GreenDIMM can reduce DRAM and system power by 36% and 20%, respectively, with ~1% performance degradation. © 2021 Association for Computing Machinery

Effects of inorganic oxidants on kinetics and mechanisms of WO3-mediated photocatalytic degradation

This study evaluates the capacity of various inorganic oxidants (IO4 -, HSO5 -, S2O8 2-, H2O2, and BrO3 -) to act as alternative electron acceptors for WO3-mediated photocatalytic oxidation. Combination with IO4 - drastically increased the rate of photocatalytic degradation of 4-chlorophenol by WO3, while the other oxyanions only negligibly improved the photocatalytic activity. The extent of the photocatalytic performance enhancement in the presence of inorganic oxidants correlated well with the efficiencies for: (1) hydroxylation of benzoic acid as an OH probe, (2) dechlorination of dichloroaceate as a hole scavenger, and (3) water oxidation with O2 evolution. The results suggest that the promoted charge separation primarily causes kinetic enhancement in photocatalytic degradation using the WO3/IO4 - system. In marked contrast to the substrate-dependent activity of the photochemically activated IO4 - (generating selective IO3), the efficiency of the WO3/IO4 - system for photocatalytic degradation did not sensitively depend on the type of target organic compound, which implies the existence of a minor contribution of the photocatalytic reduction pathway associated with the production of IO3 as a secondary oxidant. On the other hand, the insignificant inhibitory effect of methanol as an OH quencher may reveal the possible involvement of SO4 - in the improved photocatalytic activity of the WO3/HSO5 - system. The alternative use of platinized WO3, where the interfacial electron transfer occurs in a concerted step, achieved a highly accelerated photocatalytic oxidation in the presence of HSO5 - and polyoxometalates as electron scavengers. In particular, the surface loading of nanoscale platinum appeared to retard the reaction route for SO4 - generation associated with a one-electron transfer.close0

Enhanced cross-polarization efficiency of a-Si:H metasurfaces via chemical equilibrium in PE-CVD

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Evaluation of response to immune checkpoint inhibitors using a radiomics, lesion-level approach

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hundred and ninety-six patients with advanced lung cancer, treated with ICI monotherapy, who underwent at least three CT scans, were retrospectively enrolled. For all 621 measurable lesions, HPDv was determined from baseline CT scans using the tumor growth kinetics (TGK) ratio, and radiomics features were extracted. Multivariable logistic regression analysis of radiomics features was performed to discriminate DR. Radiomics features that significantly discriminated HPDv on baseline CT differed according to organ. Of the 196 patients, 54 (27.6%) had a DR and 142 (72.4%) did not have a DR. Overall survival in the group with a DR was significantly inferior to that in the group without a DR (log rank test, p = 0.04). Our study shows that lesion-level analysis using radiomics features has great potential for discriminating HPDv and understanding heterogeneous tumor progression, including a DR, after ICI treatment.11Nsciescopu

Review on Quality Control Methods in Metal Additive Manufacturing

Metal additive manufacturing (AM) has several similarities to conventional metal manufacturing, such as welding and cladding. During the manufacturing process, both metal AM and welding experience repeated partial melting and cooling, referred to as deposition. Owing to deposition, metal AM and welded products often share common product quality issues, such as layer misalignment, dimensional errors, and residual stress generation. This paper comprehensively reviews the similarities in quality monitoring methods between metal AM and conventional metal manufacturing. It was observed that a number of quality monitoring methods applied to metal AM and welding are interrelated; therefore, they can be used complementarily with each other