A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

Evaluation of atmospheric galvanic aluminum corrosion in electrical transmission towers at different sites in the Valparaíso region,Chile. Wire-on-Bolt Test (CLIMAT)

This study evaluates environmental aggressiveness and atmospheric galvanic corrosivity categories in Chile (Classification of Industrial and Marine ATmospheres test) by installing bolts in electrical transmission towers in the Valparaiso region across four exposure sites: Playa Ancha, San Sebastián, Las Vegas, and San Felipe. Classifications of marine corrosion index (MCI), industrial corrosion index (ICI), and atmospheric corrosion index (ACI) used different galvanic couples: aluminum/steel for MCI, aluminum/copper for ICI, and aluminum/polyethylene for ACI. Corrosion indices varied by season (summer, autumn, winter, and spring), for which couples were exchanged every 3 months. Intraseason variation depended mainly on the meteorochemical variables of the zone, the Cl⁻/SO₂ ratio, and the presence of general and pitting corrosion in the aluminum. The results indicate that, regardless of environmental condition, the aluminum in Al/steel (MCI) and Al/copper (ICI) couples presented a higher corrosion rate than when not forming a galvanic couple (ACI). Moreover, under higher environmental chloride, these differences increase. The Playa Ancha station presented the highest ACI.     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

Transposable Elements and Stress in Vertebrates: An Overview

Since their identification as genomic regulatory elements, Transposable Elements (TEs) were considered, at first, molecular parasites and later as an important source of genetic diversity and regulatory innovations. In vertebrates in particular, TEs have been recognized as playing an important role in major evolutionary transitions and biodiversity. Moreover, in the last decade, a significant number of papers has been published highlighting a correlation between TE activity and exposition to environmental stresses and dietary factors. In this review we present an overview of the impact of TEs in vertebrate genomes, report the silencing mechanisms adopted by host genomes to regulate TE activity, and finally we explore the effects of environmental and dietary factor exposures on TE activity in mammals, which is the most studied group among vertebrates. The studies here reported evidence that several factors can induce changes in the epigenetic status of TEs and silencing mechanisms leading to their activation with consequent effects on the host genome. The study of TE can represent a future challenge for research for developing effective markers able to detect precocious epigenetic changes and prevent human diseases.     

Environmental impact estimation of ceramic lightweight aggregates production starting from residues

Within a circular economy approach, this study investigates the environmental impact of lightweight aggregates (LWAs) produced starting from different mixes of different clays with brewery sludge and cattle bone flour ash (CBA), used as poring and fertilizing agents, respectively. The environmental impact was evaluated by means of release tests, insulation capacity, carbon footprint (CFP), and particulate matter emission during pellet firing. Release tests representative of LWAs realistic application showed very high release of phosphate and satisfactory release of potassium. The thermal insulation of the LWAs was tested by thermal imaging camera and resulted highly variable depending on the composition, with the mix containing CBA performing best. This latter composition leads also to the smallest CO₂ equivalent emission, due to the calorific power of CBA, allowing lower consumption of fossil fuels during the LWA production. Finally, total particulate emissions during the thermal treatment resulted similar in terms of mass for all mixes, while differences in terms of particle morphology and composition occurred. Samples containing residue resulted with a quite good release behavior, CFP, and insulation properties, but higher emission of particles, particularly when glass is added.     

页岩气开采对环境的影响分析及对策建议

我国页岩气资源非常丰富,但勘探开发难度巨大。分析了页岩气资源大规模开发对水资源、大气环境、地质环境及周边环境产生的影响。提出了需要健全的页岩气开发的环境监管法规规范,只有制定好产业政策,加强对技术和开采原料的研究等,才能促进页岩气产业健康、快速发展,更好地服务于中国和谐社会的建设与发展。     

投影寻踪分类模型在常见造纸纤维原料综合评价中的应用

将投影寻踪分类（PPC）模型与基于实数编码的加速遗传算法（RAGA）相结合，同时优化多个指标参数，将高维数据指标转化为低维空间上的一维投影值，建立RAGA-PPC模型，用于造纸纤维原料分类，并对造纸纤维原料进行综合评价。结果表明，基于RAGA-PPC模型的评价结果与造纸纤维原料实际分类结果一致，此方法客观可靠，精度高，具有一定的应用前景。     

Swabbing the surface: critical factors in environmental monitoring and a path towards standardization and improvement

Abstract

Cross-contamination can be broadly defined as the transfer, direct or indirect, of microorganisms from a contaminated product to a non-contaminated product. Events that may result in cross-contamination include inadequate hygiene practices, contaminated equipment surfaces, contamination via food handling personnel, further product processing, or storage abuse All of these niches require consistent environmental surveillance systems to monitor microbial harborage sites to prevent foodborne illnesses via cross-contamination. Environmental surveillance is achieved through routine surface sampling of the food contact surfaces and surrounding areas. To better understand cross-contamination, the role of environmental surface transmission during outbreaks due to the presence and persistence of pathogenic microorganisms on various food contact surfaces must be investigated. However, studies on environmental sampling techniques are rarely performed in an actual food processing environment but rather under controlled variables within a laboratory-setting. Moreover, results and conclusions of studies differ because of the considerable variability across surface sampling tools due to individual operator dependency, low recovery rates, and low reproducibility. Information is also often lacking on environmental sampling tools used within a processing facility, the characterization of these tools, and the optimization of recovery of microorganisms for surface sampling. Thus, this review aims to: (1) discuss and compare factors impacting the recovery of microorganisms and the standardization of surface sampling methods for optimal recovery of microorganisms and (2) examine how research strategies could focus more towards the development of standard methodologies for surface sampling.