Concrete crack detection using context‐aware deep semantic segmentation network

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel^® Quad‐Core? i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels.     

Characterization of nitrogen-fixing cyanobacteria in the Brazilian Amazon floodplain

The diversity of the free-living nitrogen-fixing cyanobacterial community in the floodplain sediments along the Solimões and Amazon Rivers and some of their tributaries (Japurá, Negro and Madeira) was investigated. Five cyanobacterial genera were morphologically identified, four of which (Nostoc, Calothrix, Cylindrospermum and Fischerella) have not previously been isolated from the Brazilian Amazon floodplain. Nostoc strains were the most commonly found heterocyst-forming cyanobacteria. Five strains (N. muscorum CENA18 and CENA61, N. piscinale CENA21, Cylindrospermum sp. CENA33 and Fischerella sp. CENA19) were selected for growth measurement, ability to fix N₂ and phylogenetic analysis, based on their widespread distribution and morphological distinction. Molecular analyses employing 16S rRNA sequences indicated that some of the isolates may represent novel cyanobacterial species. Dinitrogen fixed by these strains was measured indirectly as acetylene reduction activity and ranged from 11.5 to 22.2 nmol C₂H₄ μg Chl a⁻¹ h⁻¹. These results provide evidence of widespread and importance of nitrogen-fixing cyanobacteria as a source of N inputs in the Amazonian ecosystem.     

Intersections of inequality in homeownership in Sweden

Abstract

Inequalities relating to ownership of housing have become a major issue de jour in many Western societies. This article examines how the distribution of homeownership in Sweden relates to two factors widely seen as significant to such inequalities, namely parental tenure status and place of birth. We use longitudinal registry data to examine the bearing of these two factors on individual-level tenure progression since the beginning of the 1990s for persons at different stages of their housing careers. We extend existing understandings of Swedish homeownership patterns by demonstrating that inequalities relating to place of birth and parental tenure intersect with one another in ways that substantially advantage certain subgroups while disadvantaging others, and by demonstrating that experiences of entry into homeownership have in recent years been changing in markedly different ways for these different subgroups. Overall, Swedish homeownership inequalities, far from dissipating, appear to be hardening along existing lines.     

Uptake of arsenic by New Zealand watercress (Lepidium sativum)

Watercress (Lepidium sativum) is consumed as a vegetable, especially by the indigenous community in New Zealand. An investigation was carried out on the accumulation of arsenic by watercress, following earlier reports of inordinate arsenic concentrations in some aquatic macrophytes collected from the Waikato River, North Island, New Zealand. The Waikato River and some other aquatic systems in Taupo Volcanic Zone, New Zealand have elevated arsenic concentrations due to geothermal activity. Watercress, river water and sediment samples were collected from 27 sites along the Waikato river and analysed for arsenic. Greenhouse trials with watercress grown in beakers containing added arsenic were conducted to confirm the ability of this species to accumulate arsenic. At a number of sites, the concentration of arsenic in both the water and the watercress samples exceeded the World Health Organisation (WHO) limit for drinking water (0.01 mg l(-1)) and foodstuffs (2 mg kg(-1) on a fresh weight basis). The average leaf and stem arsenic concentrations were, respectively, 29.0 and 15.9 mg kg(-1) on a fresh weight basis. Plants grown in solutions of >0.4 mg l(-1) arsenic concentration had fresh weight arsenic concentrations above the WHO limit. Despite these higher concentrations, arsenic levels in plants grown under greenhouse conditions were approximately fivefold lower than in plants growing in the Waikato River, possibly because under natural conditions, the watercress is rooted in sediment containing on average approximately 35 mg kg(-1) arsenic. It is recommended that watercress from the Waikato River, or other areas with elevated water arsenic concentrations, should not be consumed.     

Performance of a CO2 sorbent for indoor air cleaning applications: Effects of environmental conditions,sorbent aging,and adsorption of co-occurring formaldehyde

Indoor air cleaning systems that incorporate CO₂ sorbent materials enable HVAC load shifting and efficiency improvements. This study developed a bench-scale experimental system to evaluate the performance of a sorbent under controlled operation conditions. A thermostatic holder containing 3.15 g sorbent was connected to a manifold that delivered CO₂-enriched air at a known temperature and relative humidity (RH). The air stream was also enriched with 0.8-2.1 ppm formaldehyde. The CO₂ concentration was monitored in real-time upstream and downstream of the sorbent, and integrated formaldehyde samples were collected at different times using DNPH-coated silica cartridges. Sorbent regeneration was carried out by circulating clean air in countercurrent. Almost 200 loading/regeneration cycles were performed in the span of 17 months, from which 104 were carried out at reference test conditions defined by loading with air at 25°C, 38% RH, and 1000 ppm CO₂, and regenerating with air at 80°C, 3% RH and 400 ppm CO₂. The working capacity decreased slightly from 43-44 mg CO₂ per g sorbent to 39-40 mg per g over the 17 months. The capacity increased with lower loading temperature (in the range 15-35°C) and higher regeneration temperature, between 40 and 80°C. The CO₂ capacity was not sensitive to the moisture content in the range 6-9 g/m³, and decreased slightly when dry air was used. Loading isothermal breakthrough curves were fitted to three simple adsorption models, verifying that pseudo-first-order kinetics appropriately describes the adsorption process. The model predicted that equilibrium capacities decreased with increasing temperature from 15 to 35°C, while adsorption rate constants slightly increased. The formaldehyde adsorption efficiency was 80%-99% in different cycles, corresponding to an average capacity of 86 ± 36 µg/g. Formaldehyde was not quantitatively released during regeneration, but its accumulation on the sorbent did not affect CO₂ adsorption.     

Mind the Gap:Predicted vs. Actual Performance of Green Buildings

This paper reviews the major North American and Australian sustainability rating tools to　determine how they measure building energy performance. It then reviews the major building energy　simulation so...     

Aspects of linear estimation in H infinty

A recent but rapidly maturing field in the area of system identification has been that of 'estimation in H infinty'. Greatly influencing this work has been the phenomenon that no linear (in-the-data) algorithm exists which is 'robustly convergent'. This paper conducts a study of this issue by combining specific new analysis together with existing results from the mathematics literature on the topic of polynomial approximation theory. Particular attention is paid to the role of model order, and this leads to the consideration of model order selection from a deterministic worst-case perspective.