The driving force of government in promoting BIM implementation

The adoption of Building Information Modelling (BIM) is one of the greatest technological innovations in the construction industry to date. However, the implementation of BIM lags far behind its potential due to the existence of various barriers. Strong government support is critical for the successful development and deployment of complex technology systems. BIM could seek government support to drive its implementation process and overcome the barriers. Through a survey, this paper aims to discover stakeholders’ expectations of the government role in BIM implementation and explores specific ways for governments to promote BIM implementation. The research findings are expected to assist related departments to accelerate BIM implementation

A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image

Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to huge domain gap between real-world and synthetic LR ones obtained by generators. To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. Our Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes. Experiments on two synthetic and two real-world datasets demonstrate that, our SCGAN outperforms the state-of-the-art methods on recovering the face structures/details and quantitative metrics for real-world face SR. The code will be publicly released at https://github.com/HaoHou-98/SCGAN

The biomechanics of tree frogs climbing curved surfaces: a gripping problem

The adhesive mechanisms of climbing animals have become an important research topic because of their biomimetic implications. We examined the climbing abilities of hylid tree frogs on vertical cylinders of differing diameter and surface roughness to investigate the relative roles of adduction forces (gripping) and adhesion. Tree frogs adhere using their toe pads and subarticular tubercles, the adhesive joint being fluid-filled. Our hypothesis was that, on an effectively flat surface (adduction forces on the largest 120 mm diameter cylinder were insufficient to allow climbing), adhesion would effectively be the only means by which tree frogs could climb, but on the two smaller diameter cylinders (44 mm and 13 mm), frogs could additionally utilise adduction forces by gripping the cylinder either with their limbs outstretched or by grasping around the cylinder with their digits, respectively. The frogs' performance would also depend on whether the surfaces were smooth (easy to adhere to) or rough (relatively non-adhesive). Our findings showed that climbing performance was highest on the narrowest smooth cylinder. Frogs climbed faster, frequently using a 'walking trot' gait rather than the 'lateral sequence walk' used on other cylinders. Using an optical technique to visualize substrate contact during climbing on smooth surfaces, we also observed an increasing engagement of the subarticular tubercles on the narrower cylinders. Finally, on the rough substrate, frogs were unable to climb the largest diameter cylinder, but were able to climb the narrowest one slowly. These results support our hypotheses and have relevance for the design of climbing robots

MRChexNet: Multi-modal bridge and relational learning for thoracic disease recognition in chest X-rays

While diagnosing multiple lesion regions in chest X-ray (CXR) images, radiologists usually apply pathological relationships in medicine before making decisions. Therefore, a comprehensive analysis of labeling relationships in different data modes is essential to improve the recognition performance of the model. However, most automated CXR diagnostic methods that consider pathological relationships treat different data modalities as independent learning objects, ignoring the alignment of pathological relationships among different data modalities. In addition, some methods that use undirected graphs to model pathological relationships ignore the directed information, making it difficult to model all pathological relationships accurately. In this paper, we propose a novel multi-label CXR classification model called MRChexNet that consists of three modules: a representation learning module (RLM), a multi-modal bridge module (MBM) and a pathology graph learning module (PGL). RLM captures specific pathological features at the image level. MBM performs cross-modal alignment of pathology relationships in different data modalities. PGL models directed relationships between disease occurrences as directed graphs. Finally, the designed graph learning block in PGL performs the integrated learning of pathology relationships in different data modalities. We evaluated MRChexNet on two large-scale CXR datasets (ChestX-Ray14 and CheXpert) and achieved state-of-the-art performance. The mean area under the curve (AUC) scores for the 14 pathologies were 0.8503 (ChestX-Ray14) and 0.8649 (CheXpert). MRChexNet effectively aligns pathology relationships in different modalities and learns more detailed correlations between pathologies. It demonstrates high accuracy and generalization compared to competing approaches. MRChexNet can contribute to thoracic disease recognition in CXR

Genetically predicted serum testosterone and risk of gynecological disorders: a Mendelian randomization study

BackgroundTestosterone plays a key role in women, but the associations of serum testosterone level with gynecological disorders risk are inconclusive in observational studies.MethodsWe leveraged public genome-wide association studies to analyze the effects of four testosterone related exposure factors on nine gynecological diseases. Causal estimates were calculated by inverse variance–weighted (IVW), MR–Egger and weighted median methods. The heterogeneity test was performed on the obtained data through Cochrane’s Q value, and the horizontal pleiotropy test was performed on the data through MR–Egger intercept and MR-PRESSO methods. “mRnd” online analysis tool was used to evaluate the statistical power of MR estimates.ResultsThe results showed that total testosterone and bioavailable testosterone were protective factors for ovarian cancer (odds ratio (OR) = 0.885, P = 0.012; OR = 0.871, P = 0.005) and endometriosis (OR = 0.805, P = 0.020; OR = 0.842, P = 0.028) but were risk factors for endometrial cancer (OR = 1.549, P < 0.001; OR = 1.499, P < 0.001) and polycystic ovary syndrome (PCOS) (OR = 1.606, P = 0.019; OR = 1.637, P = 0.017). dehydroepiandrosterone sulfate (DHEAS) is a protective factor against endometriosis (OR = 0.840, P = 0.016) and premature ovarian failure (POF) (OR = 0.461, P = 0.046) and a risk factor for endometrial cancer (OR= 1.788, P < 0.001) and PCOS (OR= 1.970, P = 0.014). sex hormone-binding globulin (SHBG) is a protective factor against endometrial cancer (OR = 0.823, P < 0.001) and PCOS (OR = 0.715, P = 0.031).ConclusionOur analysis suggested causal associations between serum testosterone level and ovarian cancer, endometrial cancer, endometriosis, PCOS, POF

Features of visibility variation at Great Wall Station, Antarctica

The variation of visibility at Great Wall Station (GWS) was analyzed using manual observational data for the period of 1986 to 2012. Results show that the frequencies of occurrence of high (≥10 km) and low visibility (0―1 km) are 61.0% and 8.0%, respectively. Visibility at GWS shows an evident seasonal variation: The highest visibility between November and March, and the lowest visibility from June to October. Sea fog and precipitation are the main factors for low visibility during summer, whereas frequent adverse weather, such as falling snow, blowing snow, or blizzards, are responsible for low visibility in winter. The frequency of occurrence of low visibility has decreased significantly from 1986 to 2012. Conversely, the frequency of occurrence of high visibility has shown a significant increasing trend, especially during winter. The decreasing tendencies of fog, blowing snow, and snowfall have contributed to the increasing trend of high visibility during winter. Visibility at GWS exhibits significant synoptic-scale (2.1 to 8.3 d), annual, and inter-annual periods (2 a, 4.1 a, and 6.9 a to 8.2 a), among which the most significant period is 4.1 a. The visibility observed during 2012 indicates that instrumental observation can be applied in the continuous monitoring of visibility at GWS