Generative Adversarial Networks (GANs) have emerged as a powerful tool rapidly advancing the state-of-the-art in numerous domains. This paper conducts a comprehensive review to analyse the applications of GANs in the construction industry over the years, and the review aims to enrich the body of knowledge on this emerging Deep Learning (DL) algorithm in the construction sector. To achieve this, a comprehensive exploration of the variation in GANs is first conducted to establish a general foundation of knowledge. Subsequently, 76 publications from the year 2014 to 2023 are analysed to identify the growth and significance of the current research landscape in the construction field. The results of the study indicate that GANs are predominantly applied in four key construction domains, yet several limitations persist. This study serves as a crucial reference point for researchers, practitioners, and stakeholders seeking to understand and harness the transformative power of GANs in construction. 

Generative adversarial networks in construction applications

The different imaging conditions of high spatial resolution remote sensing images (HSRRSIs) tend to cause large differences in the background information of bridges from the images, including problems of difficult detection of multiscale bridges, leakage of small bridges and insufficient detection accuracy for their detection. To address these problems, a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper. First, the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network (BiFPN). Then, the convolutional block attention module (CBAM) is fused into the three effective feature layers after feature pyramid network processing. The bridge feature information is effectively extracted from the channel and spatial dimensions. Next, the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously. Finally, the practical effect is evaluated by calculating the average precision (AP). According to the experimental results, the AP of the proposed method is 98.1%, which is improved by 4.1%∼23.5% compared with other models. 

Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head

FRN: Fusion and recalibration network for low-light image enhancement

This study investigated whether perinatal exposure to nonylphenol (NP) induces mitochondrial autophagy (i.e., mitophagy) damage in neonatal rat cardiomyocytes (NRCMs) and whether the PINK1/Parkin signaling pathway is involved in NP-induced primary cardiomyocyte injury.In vivo: Perinatal NP exposure increased apoptosis and mitochondrial damage in NRCMs. Mitochondrial swelling and autophagosome-like structures with multiple concentric membranes were observed in the 100 mg/kg NP group, with an increase in the number of autophagosomes. Disorganized fiber arrangement and elevated serum myocardial enzyme levels were observed with increasing NP dosage. Additionally, NP exposure led to increased MDA levels and decreased SOD activity and ATP levels in myocardial tissue. The mRNA expression levels of autophagy-related genes, including Beclin-1, p62, and LC3B, as well as the expression of mitochondrial autophagy-related proteins (PINK1, p-Parkin, Parkin, Beclin-1, p62, LC3-I, LC3-II, and LC3-II/I) and apoptosis-related proteins (Bax and caspase-3), increased, whereas the expression levels of the mitochondrial membrane protein TOMM20 and the anti-apoptotic protein Bcl-2 decreased. In vitro: NP increased ROS levels, LDH release, and decreased ATP levels in NRCMs. CsA treatment significantly inhibited the expression of autophagy-related proteins (Beclin-1, LC3-II/I, and p62) and apoptosis-related proteins (caspase-3 and Bax), increased the expression levels of TOMM20 and Bcl-2 proteins, increased cellular ATP levels, and inhibited LDH release. The inhibition of the PINK1/Parkin signaling pathway suppressed the expression of mitochondrial autophagy-related proteins (PINK1, p-Parkin, Parkin, Beclin-1, LC3-II/I, and p62) and apoptosis-related proteins (caspase-3 and Bax), increased TOMM20 and Bcl-2 protein expression, increased ATP levels, and decreased LDH levels in NRCMs.This study is novel in reporting that perinatal NP exposure induced myocardial injury in male neonatal rats, thereby inducing mitophagy. The PINK1/Parkin signaling pathway was involved in this injury by regulating mitophagy. 

Regulatory mechanism of perinatal nonylphenol exposure on cardiac mitochondrial autophagy and the PINK1/Parkin signaling pathway in male offspring rats.

Background: Previous observational studies have found that lichen sclerosus (LS) is associated with metabolic statuses, such as diabetes mellitus (DM) and body mass index (BMI). However, there are also some studies showing that LS is not related to DM and BMI. The mechanism behind observational results is still unclear. Therefore, the causality of this relationship remains unknown. In this study, a bidirectional two-sample Mendelian randomization (MR) was conducted to investigate the correlation between DM, BMI, and LS. Methods: The instrumental variables related to DM (including type 1 and type 2 diabetes), and BMI were identified from genome-wide association studies (GWAS) and a GWAS meta-analysis. The GWAS data for LS was from obtained the eighth edition of the FinnGen biological database released in 2022. Inverse variance weighted (IVW), weighted median, and MR-Egger methods were used to conduct a bidirectional two-sample MR analysis. Thereafter, the heterogeneity and horizontal pleiotropy were examined to determine whether the results were affected by a single-nucleotide polymorphism (SNP). Results: We found a lack of evidence for the causal association of DM, and BMI on LS in inverse variance weighted (type 1 diabetes, OR=0.97, 95% CI=0.91–1.04, p=0.429; type 2 diabetes, OR=0.91, 95% CI=0.82–1.00, p=0.0511; BMI, OR=0.92, 95% CI=0.73–1.15, p=0.4554). In the other direction, the results also showed that LS had no significant causal effect on DM and BMI. Conclusion: This MR analysis demonstrated no significant causal relationship between DM and BMI with LS in both directions, which contradicts previous observational studies reporting a positive association. Potential confounding factors may contribute to previously observed associations, and further research is necessary.

Causal Association Between Diabetes, Body Mass Index and Lichen Sclerosus: A Bidirectional Two-Sample Mendelian Randomization Analysis

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one. This paper develops a robust real-time face recognition approach that uses super-resolution to improve images and detect faces in the video. Many previously developed face detection systems are constrained by the severe distortion in the captured images. Further, many systems failed to handle the effect of motion, blur, and noise on the images registered on a camera. The presented approach improves descriptor count of the image based on the super-resolved faces and mitigates the effect of noise. Furthermore, it uses a parallel architecture to implement a super-resolution algorithm and overcomes the efficiency drawback increasing face recognition performance. Experimental analysis on the ORL, Caltech, and Chokepoint datasets has been carried out to evaluate the performance of the presented approach. The PSNR (Peak Signal-to-Noise-Ratio) and face recognition rate are used as the performance measures. The results showed significant improvement in the recognition rates for images where the face didn’t contain pose expressions and scale variations. Further, for the complicated cases involving scale, pose, and lighting variations, the presented approach resulted in an improvement of 5%-6% in each case. 

pdf/towards-a-super-resolution-based-approach-for-improved-face-39dnir2t.pdf

Towards a super-resolution based approach for improved face recognition in low resolution environment

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Towards a super-resolution based approach for improved face recognition in low resolution environment