Search Results (5)

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas. Full article

Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA); Class Average Accuracy (CAC); mean Intersection Of the Union (IOU); Precision (P); Recall (R); and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively. Full article

Cotton (Gossypium hirsutum L.) is one of the major fiber crops. Its production is under threat due to scarcity of water resources under a changing climatic scenario. Limited water availability also decreases the uptake of phosphorus, and less uptake of phosphorus can deteriorate the quality attributes of cotton fiber. There is a need to introduce bio-organic amendments which can mitigate osmotic stress on a sustainable basis. Inoculation of rhizobacteria can play an imperative role in this regard. Rhizobacteria can not only improve the growth of roots but also enhance the availability of immobile phosphorus in soil. That is why the current experiment was conducted to explore and compare the efficacy of sole application of diammonium phosphate (DAP) over plant growth-promoting rhizobacteria (PGPR) and phosphorus solubilizing bacteria (PSB) coated DAP on growth and quality attributes of cotton under artificially induced osmotic stress at flowering stage. The impact of phosphorus levels was found to be significant on the plant height, leaf area, average boll weight, stomatal conductance, net photosynthetic rate, and seed cotton yield, while the irrigation effect was significant on all the parameters. The PGPR coated phosphorus performed better as compared to other treatments under normal irrigation and osmotic stress. Results showed that PGPR coated phosphorus increased by 29.47%, 21.01%, 41.11%, 32.73%, 15.63% and 22.89% plant height, average boll weight, stomatal conductance, net photosynthetic rate, fiber length, and seed cotton yield respectively. In conclusion, PGPR coated DAP can be helpful to get higher cotton productivity as compared to control and sole application of DAP under normal irrigation and osmotic stress. Full article

Humus is the stable form of added crop and animal residues. The organic matter after a long-term decomposition process converts into humic substances. The naturally occurring humus is present in less amount in soils of the arid and semi-arid regions. The addition of commercially available humic acid can, therefore, contribute to improving soil health and crop yields. The present study was conducted to evaluate the effect of potassium humate, applied through soil seed dressing, on cotton productivity and fiber quality attributes. Seed dressing with potassium humate was done at the rate of 0, 100, 150 and 200 mL kg⁻¹ seed while in soil potassium humate was applied at the rate of 0, 10, 20 and 30 L ha⁻¹. Results showed that the combined application of potassium humate by seed dressing and through soil application improved the soil properties, productivity and fiber quality traits of cotton. All levels of soil applied potassium humate (10, 20 and 30 L ha⁻¹) performed better over seed dressing in terms of cotton productivity and fiber quality attributes. Among the soil application rates, 20 L ha⁻¹ potassium humate proved better as compared to other rates (0, 10 and 30 L ha⁻¹). Higher soil application of potassium humate (30 L ha⁻¹) showed depressing effects on all the traits studied like the reduction of 12.4% and 6.6% in Ginning out turn and fiber length, respectively, at a seeding dressing of 200 mL kg⁻¹. In conclusion, potassium humate seed dressing and soil application at the rate of 200 mL kg⁻¹ and 20 L ha⁻¹, respectively, is a better approach to improve cotton productivity. Soil potassium humate should not exceed a rate of 20 L ha⁻¹ when the seed dressing of potassium is also practiced. Full article

A series of symmetrical salicylaldehyde-bishydrazine azo molecules, 5a–5h, have been synthesized, characterized by ¹H-NMR and ¹³C-NMR, and evaluated for their in vitro α-glucosidase and α-amylase inhibitory activities. All the synthesized compounds efficiently inhibited both enzymes. Compound 5g was the most potent derivative in the series, and powerfully inhibited both α-glucosidase and α-amylase. The IC₅₀ of 5g against α-glucosidase was 0.35917 ± 0.0189 µM (standard acarbose IC₅₀ = 6.109 ± 0.329 µM), and the IC₅₀ value of 5g against α-amylase was 0.4379 ± 0.0423 µM (standard acarbose IC₅₀ = 33.178 ± 2.392 µM). The Lineweaver-Burk plot indicated that compound 5g is a competitive inhibitor of α-glucosidase. The binding interactions of the most active analogues were confirmed through molecular docking studies. Docking studies showed that 5g interacts with the residues Trp690, Asp548, Arg425, and Glu426, which form hydrogen bonds to 5g with distances of 2.05, 2.20, 2.10 and 2.18 Å, respectively. All compounds showed high mutagenic and tumorigenic behaviors, and only 5e showed irritant properties. In addition, all the derivatives showed good antioxidant activities. The pharmacokinetic evaluation also revealed promising results Full article