PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale

Physically-based hydrological-geotechnical modeling at large scales is difficult, especially due to the time-consuming nature of flow routing and 3D soil stability models. Although parallelization techniques are commonly used for each model individually, there is currently no concurrent parallelization strategy for both. This study proposed an open-source, Parallelized, and modular modeling software for regional Hydrologic processes and Landslides simulation and prediction (PHyL v1.0). It offers parallel computation in both hydrological and 3D slope stability modules, cross-scale modeling ability via a soil moisture downscaling method, and advanced input/output (I/O) and post-processing visualization. Additionally, PHyL v1.0 is flexible and extensible, making it compatible with all mainstream operating systems. We applied PHyL v1.0 in the Yuehe River Basin, where the computational efficiencies, parallel performance, parameter sensitivity analysis, and predictive capabilities were evaluated. The PHyL v1.0 is therefore appropriately used as an advanced software for high-resolution and complex simulations of regional floods and landslides.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Water ResourcesSanitary Engineerin

Fast Download but Eternal Seeding: The Reward and Punishment of Sharing Ratio Enforcement

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute their upload resources. It has been demonstrated that communities that use SRE are greatly oversupplied, i.e., they have much higher seeder-to-leecher ratios than communities in which SRE is not employed. The first order effect of oversupply under SRE is a positive increase in the average downloading speed. However, users are forced to seed for extremely long times to maintain adequate sharing ratios to be able to start new downloads. In this paper, we propose a fluid model to study the effects of oversupply under SRE, which predicts the average downloading speed, the average seeding time, and the average upload capacity utilization for users in communities that employ SRE. We notice that the phenomenon of oversupply has two undesired negative effects: a) Peers are forced to seed for long times, even though their seeding efforts are often not very productive (in terms of low upload capacity utilization); and b) SRE discriminates against peers with low bandwidth capacities and forces them to seed for longer durations than peers with high capacities. To alleviate these problems, we propose four different strategies for SRE, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations. Our results indicate that these new strategies release users from needlessly long seeding durations, while also being fair towards peers with low capacities and maintaining high system-wide downloading speeds.Accepted Author ManuscriptDistributed System

Systemic risk and user-level performance in private P2P communities

Many peer-to-peer communities, including private BitTorrent Communities that serve hundreds of thousands of users, utilizecredit-based or sharing ratio enforcement schemes to incentivize their members to contribute. In this paper, we analyze the performanceof such communities from both the system-level and the user-level perspectives. We show that both credit-based and sharing ratioenforcement policies can lead to system-wide “crunches” or “crashes” where the system seizes completely due to too little or to toomuch credit, respectively. We explore the conditions that lead to these system pathologies and present a theoretical model that predictsif a community will eventually crunch or crash. We apply this analysis to design an adaptive credit system that automatically adjustscredit policies to maintain sustainability. Given private communities that are sustainable, it has been demonstrated that they are greatlyoversupplied in terms of excessively high seeder-to-leecher ratios. We further analyze the user-level performance by studying theeffects of oversupply. We show that although achieving an increase in the average downloading speed, the phenomenon of oversupplyhas three undesired effects: long seeding times, low upload capacity utilizations, and an unfair playing field for late entrants into swarms.To alleviate these problems, we propose four different strategies, which have been inspired by ideas in social sciences and economics.We evaluate these strategies through simulations and demonstrate their positive effects.Accepted Author Manuscript Published online 5-12-2012Distributed System

Advancing deep learning-based detection of floating litter using a novel open dataset

Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labeled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA). However, there is no study currently reporting a rigorous evaluation of the effectiveness of these approaches for floating litter detection and their effects on the models' generalization capability. To overcome the problem of limited data availability, this work introduces the “TU Delft—Green Village” dataset, a novel labeled dataset of 9,473 camera and phone images of floating macroplastic litter and other litter items, captured using experiments in a drainage canal of TU Delft. We use the new dataset to conduct a thorough evaluation of the detection performance of five DL architectures for multi-class image classification. We focus the analysis on a systematic evaluation of the benefits of TL and DA on model performances. Moreover, we evaluate the generalization capability of these models for unseen litter items and new device settings, such as increasing the cameras' height and tilting them to 45°. The results obtained show that, for the specific problem of floating litter detection, fine-tuning all layers is more effective than the common approach of fine-tuning the classifier alone. Among the tested DA techniques, we find that simple image flipping boosts model accuracy the most, while other methods have little impact on the performance. The SqueezeNet and DenseNet121 architectures perform the best, achieving an overall accuracy of 89.6 and 91.7%, respectively. We also observe that both models retain good generalization capability which drops significantly only for the most complex scenario tested, but the overall accuracy raises significantly to around 75% when adding a limited amount of images to training data, combined with flipping augmentation. The detailed analyses conducted here and the released open source dataset offer valuable insights and serve as a precious resource for future research.Sanitary Engineerin

Deep learning for detecting macroplastic litter in water bodies: A review

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.Sanitary Engineerin

Damage Curves Derived from Hurricane Ike in the West of Galveston Bay Based on Insurance Claims and Hydrodynamic Simulations

Hurricane Ike, which struck the United States in September 2008, was the ninth most expensive hurricane in terms of damages. It caused nearly USD 30 billion in damage after making landfall on the Bolivar Peninsula, Texas. We used the Delft3d-FM/SWAN hydrodynamic and spectral wave model to simulate the storm surge inundation around Galveston Bay during Hurricane Ike. Damage curves were established through the relationship between eight hydrodynamic parameters (water depth, flow velocity, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force) simulated by the model and National Flood Insurance Program (NFIP) insurance damage data. The NFIP insurance database contains a large amount of building damage data, building stories, and elevation, as well as other information from the Ike event. We found that the damage curves are sensitive to the model grid resolution, building elevation, and the number of stories. We also found that the resulting damage functions are steeper than those developed for residential structures in many other locations.Hydraulic Structures and Flood Ris

Nonreciprocal coherent coupling of nanomagnets by exchange spin waves

Nanomagnets are widely used to store information in non-volatile spintronic devices. Spin waves can transfer information with low-power consumption as their propagations are independent of charge transport. However, to dynamically couple two distant nanomagnets via spin waves remains a major challenge for magnonics. Here we experimentally demonstrate coherent coupling of two distant Co nanowires by fast propagating spin waves in an yttrium iron garnet thin film with sub-50 nm wavelengths. Magnons in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave propagation. The coupled system is finally formulated by an analytical theory in terms of an effective non-Hermitian Hamiltonian. Our results are attractive for analog neuromorphic computing that requires unidirectional information transmission. [Figure not available: see fulltext.]Accepted Author ManuscriptQN/Bauer Grou

Nanomechanical probing and strain tuning of the Curie temperature in suspended Cr₂Ge₂Te₆-based heterostructures

Two-dimensional magnetic materials with strong magnetostriction are attractive systems for realizing strain-tuning of the magnetization in spintronic and nanomagnetic devices. This requires an understanding of the magneto-mechanical coupling in these materials. In this work, we suspend thin Cr2Ge2Te6 layers and their heterostructures, creating ferromagnetic nanomechanical membrane resonators. We probe their mechanical and magnetic properties as a function of temperature and strain by observing magneto-elastic signatures in the temperature-dependent resonance frequency near the Curie temperature, TC. We compensate for the negative thermal expansion coefficient of Cr2Ge2Te6 by fabricating heterostructures with thin layers of WSe2 and antiferromagnetic FePS3, which have positive thermal expansion coefficients. Thus we demonstrate the possibility of probing multiple magnetic phase transitions in a single heterostructure. Finally, we demonstrate a strain-induced enhancement of TC in a suspended Cr2Ge2Te6-based heterostructure by 2.5 ± 0.6 K by applying a strain of 0.026% via electrostatic force.QN/Steeneken LabQN/vanderSarlabQN/van der Zant LabDynamics of Micro and Nano System

Determination of the Maximum Effective Burning Velocity of Dust-Air Mixtures in Constant Volume Combustion

The reactivity of a combustible dust cloud is traditionally characterized by the so-called K-St value, defined as the maximum rate of pressure rise measured in constant volume explosion vessels, multiplied with the cube root of the vessel volume. The present paper explores the use of an alternative parameter, called the maximum effective burning velocity (u(eff,max)), which also is derived from pressure-time histories obtained in constant volume explosion experiments. The proposed parameter describes the reactivity of fuel-air mixtures as a function of the dispersion-induced turbulence intensity. Procedures for estimating u(eff,max) from tests in both spherical and cylindrical explosion vessels are outlined, and examples of calculated values for various fuel-air mixtures in closed vessels of different sizes and shapes are presented. Tested fuels include a mixture of 7.5% methane in air, and suspensions of 500 g/m(3) cornstarch in air and 500 g/m(3) coal dust in air. Three different test vessels have been used: a 20-1 spherical vessel and two cylindrical vessels, 7 and 221. The results show that the estimated maximum effective burning velocities are less apparatus dependent than the corresponding K-St values. (C) 2007 Elsevier Ltd. All rights reserved

A step-wise reduction of nonracemic alpha-ketoamides to alpha-methine amides under mild conditions

A step-wise reductive method that converts nonracemic gamma-chiral alpha-ketoamides into amide derivatives was developed under mild conditions. The method involves a highly efficient three-step transformation including NaBH4-reduction, bromination and Pd/C hydrogenation reactions. As a consequence, a variety of nonracemic gamma-diaryl amides products were obtained in high overall yields with excellent enantiomeric specificity. The utility of the method is demonstrated through the concise formal synthesis of (+)-sertraline in good overall yield. (C) 2016 Elsevier Ltd. All rights reserved