Hinrichsen Hagan (flockbike45)

The quantification of microplastics is a needed task to monitor its evolution and model its behavior. However, it is a time demanding task traditionally performed using expensive equipment. In this paper, an architecture based on deep learning networks is presented with the aim of automatically count and classify microplastic particles in the range of 1-5 mm from pictures taken with a digital camera or a mobile phone with a resolution of 16 million pixels or higher. selleck products The proposed architecture comprises a first stage, implemented with the U-Net neural network, in charge of making the segmentation of the particles in the image. After the different particles have been isolated, a second stage based on the VGG16 neural network classifies them into three types fragments, pellets and lines. These three types have been selected for being the most common in the range size under consideration. The experimental evaluation was carried out using images taken with two digital cameras and one mobile phone. The particles used in experiments correspond to samples collected on the beach of Playa del Poris in Tenerife Island, Spain, (28° 09' 51″ N, 16° 25' 54″ W) in August 2018. A Jaccard index value of 0.8 is achieved in the experiments of particles segmentation and an accuracy of 98.11% is obtained in the classification of the microplastic particles. The proposed architecture is remarkable faster than a similar previously published system based on traditional computer vision techniques.Electrolytic manganese anode slag (EMAS) is the waste residue produced by electrolytic manganese metal industry. At present, no mature recycling system has been established, which causes a waste of resources and threatens the environment. Therefore, the resource utilization of EMAS has attracted increased attention. In this paper, the in-situ resource utilization of EMAS can be realized by pickling treatment was reported. Specifically, EMAS after pickling treatment (PEMAS) was first used as catalyst to activate PMS to degrade tetrachlorophenol (4-CP). Pickling could remove the inert inorganic components on EMAS and increase the specific surface area, pore volume and Mn distribution of the catalyst, thus improving the catalytic performance of the catalyst. Under the conditions of 4-CP of 40 ppm, PMS of 1 mM and PEMAS of 0.3 g L-1, 85% of 4-CP could be degraded within 50 min. Mechanism studies proved that the main active species were O2- and 1O2. Some O2- contributed to the generation of 1O2 and some O2- directly contributed to the degradation of 4-CP. During the reaction, the valence state of Mn transformed between Mn(III)/Mn(IV) and Mn(II)/Mn(III) and kept the cycle. Moreover, PEMAS/PMS system exhibited excellent independence of the solution pH, resistance to the versatile inorganic ions and background organic matters, and stability of recycling. In a word, this study has achieved the resource utilization of EMAS and the goal of treating waste with waste, which is a win-win strategy of economic and environmental benefits.In the last decades, many researchers investigated the relation between environmental pollution and the degradation phenomena on the built heritage, because of their rapid increase and growing harmfulness. Consequently, the identification of the main pollution sources has become essential to define mitigation actions against degradation and alteration phenomena of the stone materials. In this way, the present paper is focused on the study of the effect of air pollution on archaeological buildings in Historic Cairo. A multi-methodological approach was used to obtain information about the chemical composition of examined black crusts and to clarify their correlation with the air pollution, specifically the heavy metals and the carbonaceous fraction, their main sources, and their impact on the state of conservation of the studied sites. All specimens were characterized by polarized optical microscopy (POM), X-Ray Diffraction (X