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The application of deep learning in construction has attracted increasing attention among researchers in recent years. In this review article, comprehensive scientometric analysis and critical review were performed to analyze the state- of-the-art literature on the application of deep learning in construction. This research used the science mapping method to quantitatively and systematically analyze 423 related bibliographic records retrieved from the Scopus database, and further, a critical review was performed on the collected themes of all the related publications. The results of the critical review indicate that deep convolution neural networks, you only look once, single-shot detectors, recurrent neural networks, residual neural networks, and fast region-based convolution neural networks have been the most widely used deep-learning methods in the construction industry. The most commonly addressed problems in the construction industry using deep-learning methods include classification of construction equipment, worker's safety helmet detection, ergonomics analysis, image enhancement, and feature extraction. This paper provides an in-depth understanding and big-picture overview of the existing literature along with the challenges and future direction of research on deep learning in construction. Key words: scientometric analysis, deep learning, critical review, construction industry, Introduction As the construction industry moves towards adopting advanced computer technologies to improve the efficiency of its operations, artificial intelligence is becoming increasingly popular among researchers who are developing innovative [...]

As an important macro element for all living cells, phosphorus is essential in agricultural production systems and is required in large quantities by elite varieties of crops to maintain yields. Approximately 70% of the worldwide cultivated land suffers from phosphorous deficiency, and it has recently been estimated that the worldwide phosphorous resources will be shattered by the end of this century, thereby increasing the need to develop phosphorus-efficient crops. A greater understanding of how plants can maintain yield with lower phosphorous availability is highly desirable to both breeders and farmers. For this research, we focused on the phosphorusstarvation tolerance 1 (PSTOL1) gene, which is known to be involved in enhancing early root growth, thereby enabling plants to acquire more phosphorus and other nutrients and enhancing grain yield in phosphorus-deficient soil. As there is no reported structure and function of the PSTOL1 gene, this project involves a distinct set of opportunities and challenges and requires different approaches to model the interaction between PSTOL1 and the gene phosphorous uptake 1 (PUP1). This article covers the modeling, docking, and simulation of PSTOL1 to check the protein stability and its behavior over time. The physiochemical properties were ascertained, a phylogenetic tree was constructed to find the evolutionary relationship, and the conserved domains were analyzed for functional annotation. This study reports that the advancement of the PSTOL1-mediated phosphorous uptake 1 (PUP1) signaling cascade using structural bioinformatics is a potent biological mechanism against phosphorous starvation in wheat. [ABSTRACT FROM AUTHOR]

Background: Salinity is one of the main abiotic factors that restrict plant growth, physiology, and crop productivity is salt stress. About 33% of the total irrigated land suffers from severe salinity because of intensive underground water extraction and irrigation with brackish water. Thus, it is important to understand the genetic mechanism and identify the novel genes involved in salt tolerance for the development of climate-resilient rice cultivars. Methods and results: In this study, two rice genotypes with varying tolerance to salt stress were used to investigate the differential expressed genes and molecular pathways to adapt under saline soil by comparative RNA sequencing at 42 days of the seedling stage. Salt-susceptible (S3) and -tolerant (S13) genotypes revealed 3982 and 3463 differentially expressed genes in S3 and S13 genotypes. The up-regulated genes in both genotypes were substantially enriched in different metabolic processes and binding activities. Biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, and plant signal transduction mechanisms were highly enriched. Salt-susceptible and -tolerant genotypes shared the same salt adaptability mechanism with no significant quantitative differences at the transcriptome level. Moreover, bHLH, ERF, NAC, WRKY, and MYB transcription factors were substantially up-regulated under salt stress. 391 out of 1806 identified novel genes involved in signal transduction mechanisms. Expression profiling of six novel genes further validated the findings from RNA-seq data. Conclusion: These findings suggest that the differentially expressed genes and molecular mechanisms involved in salt stress adaptation are conserved in both salt-susceptible and salt-tolerant rice genotypes. Further molecular characterization of novel genes will help to understand the genetic mechanism underlying salt tolerance in rice. [ABSTRACT FROM AUTHOR]

The growing prevalence of modular construction, while it offers benefits in terms of productivity and sustainability, has led to increased use of heavy mobile cranes and related resources on construction sites. One significant resource associated with crane use is the crane mat, which offers practical mobile crane ground support against poor soil-bearing capacity. Due to increased use of crane mats, crane mat layout plans/drawings have become increasingly significant in today's construction industry. The present work describes an automated crane mat optimization framework for preparing crane mat layout plans/drawings built on an agent-based greedy algorithm and reinforcement learning. The proposed framework employs these approaches to achieve the maximum area with the minimum number of crane mats. The proposed framework is found to decrease the time required for preparing a crane mat layout plan/drawing (approximately 97% time saving) with more uniform and efficient crane mat planning outcomes (approximately 63% crane mat material reduction). The research presented in this manuscript examined the largely unexplored topics of crane mat layout optimization and layout preparation, proposing an agent-based greedy algorithm and reinforcement learning (RL) approach for automated crane mat layout optimization as an innovative approach to developing sustainable crane mat layouts. This approach takes into account the site constraints and can be applied to mitigate crane mat crowding on construction sites. Crane mat optimization is applied using both methods (greedy and RL) to achieve the maximum area covered with the minimum number of crane mats used. The results demonstrate that the practitioner time spent preparing a crane mat layout plan/drawing can be reduced considerably, with more uniform and cost-effective crane mat optimization outcomes. Another outcome of this research is that developed approaches reduce the crane mat requirements compared with the outputs generated by the manual approach, thereby reducing the CO2 emissions associated with crane mat manufacturing and usage. [ABSTRACT FROM AUTHOR]

The genetic basis of salinity tolerance for three parameters Ct, C50 and C0 in P. americanum using the diallel analysis was investigated. Tolerance for Ct, C50, and C0 was due to both additive and dominance genetic effects with indication of over-dominance. For these three parameters, dominance is predominantly towards salt sensitivity, but it appears that in different accessions, there are different gene effects for each character. Recessive genes are evident in the salt tolerant line ICMV-94474 for Ct and C50, and in the other tolerant line 10878 are also evident for C50 and C0 for recessive genes. Salinity data set were found to be adequate totally for C50 and C0, but partially adequate for Ct. It appears in pearl millet that in some accessions recessive genes are responsible for salt tolerance for Ct, C50, and C0, but this trend is not consistent. Different (dominant / recessive) genes control each accession for Ct, C50, and C0. The maximum narrow sense and broad sense of heritability were found for C50 and this suggests that C50 is the best character for selection, rather than absolute root length. C50 is highly correlated with Ct, and C50 is also signifi cantly correlated with C0. These results suggest that the genetic bases of these genes are linked to some degree. These correlations would be of considerable value in breeding for improved salinity tolerance.

Information about genetic diversity and relationships among the breeding materials has a signifi cant impact on crop improvement. Association between parental divergence and progeny performance has not been well documented in cotton. A cotton study was conducted in the department of Plant Breeding and Genetics, University College of Agriculture, Bahauddin Zakaryia University, Multan, (30.2oN, 71.4oE) Pakistan and National Food Research Institute, Tsukuba, Japan. The objective of this study was to estimate genetic diversity among selected cotton genotypes and their reciprocal crosses. Six U.S. and two local (Pakistani) cultivars all belonging to Gossypium hirsutum L were genotyped by polyacrylamide gel electrophoresis and compared with BioRad molecular weight markers. Cluster analysis revealed low genetic diversity among the parents and pooled the crosses with their parents, indicating success of hybridization. The present study, combined PAGE analysis with cluster analysis confi rmed the genetic similarities between parents and their crosses while it also confi rmed the dissimilarities between the parents as showed by the morphological characters.