Il futuro delle regioni

il futuro delle region

Trust and Distrust in Big Data Recommendation Agents

Big data technology allows for managing data from a variety of sources, in large amounts, and at a higher velocity than before, impacting several traditional systems, including recommendation agents. Along with these improvements, there are concerns about trust and distrust in RA recommendations. Much prior work on trust has been done in IS, but only a few have examined trust and distrust in the context of big data and analytics. In this vein, the purpose of this study is to study the eight antecedents of trust and distrust in recommendation agents’ cues in the context of the Big Data ecosystem using an experiment. Our study contributes to the literature by integrating big data and recommendation agent IT artifacts, expanding trust and distrust theory in the context of a big data ecosystem, and incorporating the constructs of algorithm innovativeness and process transparency

No evidence for an Eddington-ratio dependence of X-ray weakness in BALQSOs

Several works have studied the relation between X-ray, UV, and wind properties in broad absorption line quasars (BALQSOs), generally concluding that the formation of strong winds is tightly connected with the suppression of the ionizing EUV/X-ray emission. The Eddington ratio ($\lambda_{Edd}$), which measures the accretion rate, is also known to be related with outflow and emission-line properties in the general quasar population. Moreover, models describing quasar accretion depend on $\lambda_{Edd}$, which can thus possibly affect the relative production of accelerating UV and ionizing EUV/X-ray radiation. In this work, for the first time, we investigated whether BALQSO X-ray properties are related with the Eddington ratio. We selected a sample of 30 BALQSOs with accurate measurements of black-hole mass and BAL properties from the literature, and we complemented it with 4 additional BALQSOs we observed with \xmm\, to populate the low and high Eddington-ratio regimes. We did not find evidence for a strong relation between $\lambda_{Edd}$ and X-ray suppression, which however shows a significant correlation with the strength of the UV absorption features. These findings are confirmed also by considering a sample of mini-BALQSOs collected from the literature.Comment: 8 pages, 2 figures, Accepted 2018 June 29. Received 2018 June 29; in original form 2018 April 2

Piercing Through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields: I. X-ray Spectral and Long-term Variability Analyses

We present a detailed X-ray spectral analysis of 1152 AGNs selected in the Chandra Deep Fields (CDFs), in order to identify highly obscured AGNs ($N_{\rm H} > 10^{23}\ \rm cm^{-2}$). By fitting spectra with physical models, 436 (38%) sources with $L_{\rm X} > 10^{42}\ \rm erg\ s^{-1}$ are confirmed to be highly obscured, including 102 Compton-thick (CT) candidates. We propose a new hardness-ratio measure of the obscuration level which can be used to select highly obscured AGN candidates. The completeness and accuracy of applying this method to our AGNs are 88% and 80%, respectively. The observed logN-logS relation favors cosmic X-ray background models that predict moderate (i.e., between optimistic and pessimistic) CT number counts. 19% (6/31) of our highly obscured AGNs that have optical classifications are labeled as broad-line AGNs, suggesting that, at least for part of the AGN population, the heavy X-ray obscuration is largely a line-of-sight effect, i.e., some high-column-density clouds on various scales (but not necessarily a dust-enshrouded torus) along our sightline may obscure the compact X-ray emitter. After correcting for several observational biases, we obtain the intrinsic NH distribution and its evolution. The CT-to-highly-obscured fraction is roughly 52% and is consistent with no evident redshift evolution. We also perform long-term (~17 years in the observed frame) variability analyses for 31 sources with the largest number of counts available. Among them, 17 sources show flux variabilities: 31% (5/17) are caused by the change of NH, 53% (9/17) are caused by the intrinsic luminosity variability, 6% (1/17) are driven by both effects, and 2 are not classified due to large spectral fitting errors.Comment: 32 pages, 21 figures, 9 tables, accepted for publication in Ap

Crack healing utilising bacterial spores in concrete

This self repair system is based upon harmless ground borne bacteria as the self healing agent. The bacteria is activated after the concrete is cracked and the bacterial spores are exposed to moisture and air. The bacterial reproduction process creates a calcite by-product which fills the cracks in the concrete. By sealing the cracks in concrete, an effective barrier to air or liquid borne deleterious materials is formed and as a consequence of his, enhanced durability is achieved in the structure, resulting in lower life cycle costs. The concrete/mortar prisms were cracked and tested for water flow. They were then left for 56 days to heal and were subject to a test for water tightness. Healing was observed and a reduced water flow (74% and 32% healed) measured with the healed samples when compared to the specimens that were cracked and subjected to a water flow test without any healing agent. The number of samples were limited and a larger scale test is recommended for further work, however this is proof of concept of the process of healing and testing

Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery

The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.This work was funded by the National Key Research and Development Program of China (Project No. 2018YFC1505202), the National Natural Science Foundation of China (41941019), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2020Z012), the project on identification and monitoring of potential geological hazards with remote sensing in Sichuan Province (510201202076888) and the Everest Scientific Project at Chengdu University of Technology (2020ZF114103)

Ultraviolet supercontinuum generation using a differentially-pumped integrated glass chip

We investigate the generation of ultrabroadband femtosecond ultraviolet (UV) radiation via third-order harmonic generation in highly confined gas media. A dual-stage differential-pumping scheme integrated into a glass microfluidic chip provides an exceptional gas confinement up to several bar and allows the apparatus to be operated under high-vacuum environment. UV pulses are generated both in argon and neon with up to ∼0.8 μJ energy and 0.2% conversion efficiency for spectra that cover the UVB and UVC regions between 200 and 325 nm. Numerical simulations based on the unidirectional pulse propagation equation reveal that ionization plays a critical role for extending the spectral bandwidth of the generated third-harmonic pulse beyond the tripled 800 nm driving laser pulse bandwidth. By delivering UV supercontinua supporting Fourier transform limits below 2 fs, as well as comparable pulse energies with respect to capillary-based techniques that typically provide high spectral tunability but produce narrower bandwidths, our compact device makes a step forward towards the production and application of sub-fs UV pulses for the investigation of electron dynamics in neutral molecules

Prediction of compost organic matter via color sensor

Composted materials serve as an effective soil nutrient amendment. Organic matter in compost plays an important role in quantifying composted materials overall quality and nutrient content. Measuring organic matter content traditionally takes considerable time, resources, and various laboratory equipment (e.g., oven, muffle furnace, crucibles, precision balance). Much like the quantitative color indices (e.g., sRGB R, sRGB G, sRGB B, CIEL*a* b*) derived from the low-cost NixPro2 color sensor have proven adept at predicting soil organic matter in-situ, the NixPro2 color sensor has the potential to be effective for predicting organic matter in composted materials without the need for traditional laboratory methods. In this study, a total of 200 compost samples (13 different compost types) were measured for organic matter content via traditional loss-on-ignition (LOI) and via the NixPro2 color sensor. The NixPro2 color sensor showed promising results with an LOI-prediction model utilizing the CIEL*a* b* color model through the application of the Generalized Additive Model (GAM) algorithm yielding an excellent prediction accuracy (validation R2 = 0.87, validation RMSE = 4.66 %). Moreover, the PCA scoreplot differentiated the three lowest organic matter compost types from the remaining 10 compost types. These results have valuable practical significance for the compost industry by predicting compost organic matter in real time without the need for laborious, time-consuming methods