Recovery of Sparse Probability Measures via Convex Programming

We consider the problem of cardinality penalized optimization of a convex function over the probability simplex with additional convex constraints. The classical ℓ_1 regularizer fails to promote sparsity on the probability simplex since ℓ_1 norm on the probability simplex is trivially constant. We propose a direct relaxation of the minimum cardinality problem and show that it can be efficiently solved using convex programming. As a first application we consider recovering a sparse probability measure given moment constraints, in which our formulation becomes linear programming, hence can be solved very efficiently. A sufficient condition for exact recovery of the minimum cardinality solution is derived for arbitrary affine constraints. We then develop a penalized version for the noisy setting which can be solved using second order cone programs. The proposed method outperforms known rescaling heuristics based on ℓ_1 norm. As a second application we consider convex clustering using a sparse Gaussian mixture and compare our results with the well known soft k-means algorithm

Evaluation of High-Rate GNSS-PPP for Monitoring Structural Health and Seismogeodesy Applications

This study evaluates the usability of the GNSS-PPP method for structural health monitoring and seismogeodesy applications. Two test scenarios were considered. The first test scenario included monitoring hormonic oscillations in amplitude of 5 mm to 20 mm with the frequency range of 0.2 Hz to 2.5 Hz that were generated using a shaking table, which has the ability to move in one direction in a horizontal plane. The second test scenario was carried out by simulating the El-Centro Earthquake as a seismogeodesy application. The used GNSS data comprised dual-frequency observations with a 10 Hz sampling rate. GNSS-derived positioning time series were obtained by processing the data using a post-mission kinematic PPP method and results were compared, in both the frequency domain and time domain, with LVDT (Linear Variable Differential Transformer) data, taking as a reference. Results show that the high-rate GNSS PPP method can capture the frequencies of harmonic movements comparable to the LVDT. The observed amplitudes of the harmonic oscillations are slightly different from the LVDT data at the order of mm level. These results demonstrate the ability of the high-rate GNSS PPP method to reliably monitor structural and earthquake-induced vibration frequencies and amplitudes for both the structural health and seismogeodesy applications

Rheological behavior of cement paste with nano-Fe3O4 under magnetic field : magneto-rheological responses and conceptual calculations

The magneto-rheological responses of cement paste with nano-Fe3O4 particles are experimentally investigated. The estimated magneto-dynamic force between two neighboring nanoparticles and equilibrium movement velocity of the nanoparticles in cement-based suspensions are calculated. Results show that the nanoparticles have a potential to move to form magnetic clusters when a magnetic field is applied, which creates a sort of agitation effect breaking down the early C-S-H links between cement particles, and thus the corresponding suspensions exhibit liquid-like behavior immediately after applying the magnetic field. The solid-like property of the studied suspensions becomes more dominant with magnetizing time due to the formation of magnetic clusters and the reconstruction of C-S-H bridges. The rheological properties of paste medium exert significant influences on the magneto-rheological responses of cement paste containing nano-Fe3O4 particles. It is revealed that the calculated magnetic yield parameter and nanoparticle movement velocity are useful relevant indicators to evaluate the magneto-rheological effect of cementitious paste

Quantitative assessment of the influence of external magnetic field on clustering of nano-Fe3O4 particles in cementitious paste

In view of active rheology control of cementitious materials, nano-Fe3O4 can be added as responsive particles. Following the concept of magnetorheological fluids, it is assumed that magnetic nanoparticles will form chains or clusters in cementitious paste following magnetic field lines. A quantitative experimental validation of this assumption is presented herein. The clustering of nano-Fe3O4 particles under magnetic fields is studied by mapping iron (Fe) element distribution in cementitious paste using energy dispersive X-ray spectroscopy. By means of image analysis, the Fe-element patterns are quantified by the deviation of Fe-elements in a unit area from the mean value expected in case of a uniform distribution, as expressed by coefficient of variation (COV). The magneto-rheological responses of cementitious pastes are evaluated using small amplitude oscillatory shear technique. Results show that the magneto-rheological effect exhibits a linear relationship with the relative change of COV, providing a quantitative validation of magnetic clustering in cementitious paste

Rheological properties of cement paste with nano-Fe3O4 under magnetic field : flow curve and nanoparticle agglomeration

Understanding the influence of magnetic fields on the rheological behavior of flowing cement paste is of great importance to achieve active rheology control during concrete pumping. In this study, the rheological properties of cementitious paste with water-to-cement (w/c) ratio of 0.4 and nano-Fe3O4 content of 3% are first measured under magnetic field. Experimental results show that the shear stress of the cementitious paste under an external magnetic field of 0.5 T is lower than that obtained without magnetic field. After the rheological test, obvious nanoparticle agglomeration and bleeding are observed on the interface between the cementitious paste and the upper rotating plate, and results indicate that this behavior is induced by the high magnetic field strength and high-rate shearing. Subsequently, the hypothesis about the underlying mechanisms of nanoparticles migration in cementitious paste is illustrated. The distribution of the nanoparticles in the cementitious paste between parallel plates is examined by the magnetic properties of the powder as determined by a vibrating sample magnetometer. It is revealed that the magnetization of cementitious powders at different sections and layers provides a solid verification of the hypothesis

Stable, Ductile and Strong Ultrafine HT-9 Steels via Large Strain Machining

Beyond the current commercial materials, refining the grain size is among the proposed strategies to manufacture resilient materials for industrial applications demanding high resistance to severe environments. Here, large strain machining (LSM) was used to manufacture nanostructured HT-9 steel with enhanced thermal stability, mechanical properties, and ductility. Nanocrystalline HT-9 steels with different aspect rations are achieved. In-situ transmission electron microscopy annealing experiments demonstrated that the nanocrystalline grains have excellent thermal stability up to 700 & DEG;C with no additional elemental segregation on the grain boundaries other than the initial carbides, attributing the thermal stability of the LSM materials to the low dislocation densities and strains in the final microstructure. Nano-indentation and micro-tensile testing performed on the LSM material pre- and post-annealing demonstrated the possibility of tuning the material's strength and ductility. The results expound on the possibility of manufacturing controlled nanocrystalline materials via a scalable and cost-effective method, albeit with additional fundamental understanding of the resultant morphology dependence on the LSM conditions

Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations

Nowadays, the high rate GNSS (Global Navigation Satellite Systems) positioning methods are widely used as a complementary tool to other geotechnical sensors, such as accelerometers, seismometers, and inertial measurement units (IMU), to evaluate dynamic displacement responses of engineering structures. However, the most common problem in structural health monitoring (SHM) using GNSS is the presence of surrounding structures that cause multipath errors in GNSS observations. Skyscrapers and high-rise buildings in metropolitan cities are generally close to each other, and long-span bridges have towers, main cable, and suspender cables. Therefore, multipath error in GNSS observations, which is typically added to the measurement noise, is inevitable while monitoring such flexible engineering structures. Unlike other errors like atmospheric errors, which are mostly reduced or modeled out, multipath errors are the largest remaining unmanaged error sources. The high noise levels of high-rate GNSS solutions limit their structural monitoring application for detecting load-induced semi-static and dynamic displacements. This study investigates the estimation of accurate dynamic characteristics (frequency and amplitude) of structural or seismic motions derived from multipath-affected high-rate GNSS observations. To this end, a novel hybrid model using both wavelet-based multiscale principal component analysis (MSPCA) and wavelet transform (MSPCAW) is designed to extract the amplitude and frequency of both GNSS relative- and PPP- (Precise Point Positioning) derived displacement motions. To evaluate the method, a shaking table with a GNSS receiver attached to it, collecting 10 Hz data, was set up close to a building. The table was used to generate various amplitudes and frequencies of harmonic motions. In addition, 50-Hz linear variable differential transformer (LVDT) observations were collected to verify the MSMPCAW model by comparing their results. The results showed that the MSPCAW could be efficiently used to extract the dynamic characteristics of noisy dynamic movements under seismic loads. Furthermore, the dynamic behavior of seismic motions can be extracted accurately using GNSS-PPP, and its dominant frequency equals that extracted by LVDT and relative GNSS positioning method. Its accuracy in determining the amplitude approaches 91.5% relative to the LVDT observations

The global spread of misinformation on spiders

In the internet era, the digital architecture that keeps us connected and informed may also amplify the spread of misinformation. This problem is gaining global attention, as evidence accumulates that misinformation may interfere with democratic processes and undermine collective responses to environmental and health crises1,2. In an increasingly polluted information ecosystem, understanding the factors underlying the generation and spread of misinformation is becoming a pressing scientific and societal challenge3. Here, we studied the global spread of (mis-)information on spiders using a high-resolution global database of online newspaper articles on spider–human interactions, covering stories of spider–human encounters and biting events published from 2010–20204. We found that 47% of articles contained errors and 43% were sensationalist. Moreover, we show that the flow of spider-related news occurs within a highly interconnected global network and provide evidence that sensationalism is a key factor underlying the spread of misinformation

Decomposers and root feeders interactively affect plant defence in Sinapis alba

Aboveground herbivory is well known to change plant growth and defence. In contrast, effects of soil organisms, acting alone or in concert, on allocation patterns are less well understood. We investigated separate and combined effects of the endogeic earthworm species Aporrectodea caliginosa and the root feeding nematode species Pratylenchus penetrans and Meloidogyne incognita on plant responses including growth and defence metabolite concentrations in leaves of white mustard, Sinapis alba. Soil biota had a strong impact on plant traits, with the intensity varying due to species combinations. Nematode infestation reduced shoot biomass and nitrogen concentration but only in the absence of earthworms. Earthworms likely counteracted the negative effects of nematodes. Infestation with the migratory lesion-nematode P. penetrans combined with earthworms led to increased root length. Earthworm biomass increased in the presence of this species, indicating that these nematodes increased the food resources of earthworms—presumably dead and decaying roots. Nitrogen-based defence compounds, i.e. glucosinolates, did not correlate with nitrogen levels. In the presence of earthworms, concentrations of aromatic glucosinolates in leaves were significantly increased. In contrast, infection with P. penetrans strongly decreased concentrations of glucosinolates (up to 81%). Infestation with the sedentary nematode M. incognita induced aromatic glucosinolates by more than 50% but only when earthworms were also present. Myrosinase activities, glucosinolate-hydrolysing enzymes, were unaffected by nematodes but reduced in the presence of earthworms. Our results document that root-feeding nematodes elicit systemic plant responses in defence metabolites, with the responses varying drastically with nematode species of different functional groups. Furthermore, systemic plant responses are also altered by decomposer animals, such as earthworms, challenging the assumption that induction of plant responses including defence traits is restricted to herbivores. Soil animals even interact and modulate the individual effects on plant growth and plant defence, thereby likely also influencing shoot herbivore attack