Fate and effects of uncoated ZnO nanoparticles on nine crops exposed in two agricultural soils, a calcareous and an acidic soil.

The nanotechnology has a wide range of applications including those that deliberately release nanoparticles (NPs) into the environment. The use of nanoformulations containing agrochemicals to soils provides an efficient way to apply pesticides and fertilizers in a controlled mode. The zinc oxide nanoparticles (ZnO NPs) are of particular interest due to their increasing incorporation into agricultural products

Soil pH effects on the Toxicity of zinc oxide nanoparticles to soil bacterial communities.

The environmental levels of ZnO nanoparticles (nZnO) are increasing continually given the widespread and expanding applications of this material. Soil pH appears to be one of the key factors affecting the behavior and toxicity of metal nanoparticles in soi

Influence of soil pH in the effects of ZnONPs on the antioxidant activities and Zn uptake in three plant species (T aestivum, R. sativus and Z. mays)

In recent years, the study of phytotoxicity of NPs has made rapid progress, but important issues remain to be solved, among them, the role of soil and the importance of the physicochemical soil characteristics for their toxicity and accumulation potential

Robust Machine Learning for Malware Detection over Time

The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more effective detectors that can block the majority of these malicious programs. However, these algorithms have not been developed to pursue the natural evolution of malware, and their performances significantly degrade over time because of such concept-drift. Currently, state-of-the-art techniques only focus on detecting the presence of such drift, or they address it by relying on frequent updates of models. Hence, there is a lack of knowledge regarding the cause of the concept drift, and ad-hoc solutions that can counter the passing of time are still under-investigated. In this work, we commence to address these issues as we propose (i) a drift-analysis framework to identify which characteristics of data are causing the drift, and (ii) SVM-CB, a time-aware classifier that leverages the drift-analysis information to slow down the performance drop. We highlight the efficacy of our contribution by comparing its degradation over time with a state-of-the-art classifier, and we show that SVM-CB better withstand the distribution changes that naturally characterizes the malware domain. We conclude by discussing the limitations of our approach and how our contribution can be taken as a first step towards more time-resistant classifiers that not only tackle, but also understand the concept drift that affect data

The AEgIS experiment at CERN: Measuring antihydrogen free-fall in earth's gravitational field to test WEP with antimatter

The AEgIS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) experiment is designed with the objective to test the weak equivalence principle with antimatter by studying the free fall of antihydrogen in the Earth's gravitational field. A pulsed cold beam of antihydrogen will be produced by charge exchange between cold Ps excited in Rydberg state and cold antiprotons. Finally the free fall will be measured by a classical moir\ue9 deflectometer. The apparatus being assembled at the Antiproton Decelerator at CERN will be described, then the advancements of the experiment will be reported: positrons and antiprotons trapping measurements, Ps two-step excitation and a test-measurement of antiprotons deflection with a small scale moir\ue9 deflectometer

Laser excitation of the n=3 level of positronium for antihydrogen production

We demonstrate the laser excitation of the n = 3 state of positronium (Ps) in vacuum. A combination of a specially designed pulsed slow positron beam and a high-efficiency converter target was used to produce Ps. Its annihilation was recorded by single-shot positronium annihilation lifetime spectroscopy. Pulsed laser excitation of the n = 3 level at a wavelength lambda approximate to 205 nm was monitored via Ps photoionization induced by a second intense laser pulse at lambda = 1064 nm. About 15% of the overall positronium emitted into vacuum was excited to n = 3 and photoionized. Saturation of both the n = 3 excitation and the following photoionization was observed and explained by a simple rate equation model. The positronium's transverse temperature was extracted by measuring the width of the Doppler-broadened absorption line. Moreover, excitation to Rydberg states n = 15 and 16 using n = 3 as the intermediate level was observed, giving an independent confirmation of excitation to the 3 P-3 state

Optimal Sampling for the Population Coverage Survey of the New Italian Register Based Census

Abstract For the first time in 2018 the Italian Institute of Statistics (Istat) implemented the annual Permanent Population Census which relies on the Population Base Register (PBR) and the Population Coverage Survey (PCS). This article provides a general overview of the PCS sampling design, which makes use of the PBR to correct population counts with the extended dual system estimator (Nirel and Glickman 2009). The sample allocation, proven optimal under a set of precision constraints, is based on preliminary estimates of individual probabilities of over-coverage and under-coverage. It defines the expected sample size in terms of individuals, and it oversamples the sub-populations subject to the risk of under/over coverage. Finally, the article introduces a sample selection method, which to the greatest extent possible satisfies the planned allocation of persons in terms of socio-demographic characteristics. Under acceptable assumptions, the article also shows that the sampling strategy enhances the precision of the estimates

ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. The dataset is built by first optimizing a set of adversarial patches against an ensemble of models, using a state-of-the-art attack that creates transferable patches. The corresponding patches are then randomly rotated and translated, and finally applied to the ImageNet data. We use ImageNet-Patch to benchmark the robustness of 127 models against patch attacks, and also validate the effectiveness of the given patches in the physical domain (i.e., by printing and applying them to real-world objects). We conclude by discussing how our dataset could be used as a benchmark for robustness, and how our methodology can be generalized to other domains. We open source our dataset and evaluation code at https://github.com/pralab/ImageNet-Patch