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Abstract In order to clarify the risk of demolition construction of large-span continuous rigid structure bridge and put forward an intelligent safety assessment method to ensure the safety of demolition construction of the closure segment. Taking a concrete continuous rigid bridge as an example, this paper uses the combination of finite element analysis, theoretical calculation and actual measurement verification to study the influencing parameters and construction safety assessment methods of the long-span continuous rigid bridge in the demolition construction stage of the closure segment. The results show that the parameters that have a great influence on the stress state of box girder and pier during the demolition stage of the closure segment are mainly the self-weight of the structure, tendon prestress state and construction temperature difference. Through the influence envelope analysis of each parameter, it is clear that the ultimate failure mode caused by the most unfavorable parameter combination in the demolition stage of the closure segment is the crushing of the bottom plate of the box girder in the middle span, and the cracking of the piers on the side span at the top and the variable section. In order to further accurately evaluate the construction safety in the demolition stage of the closure segment, based on the long-term down-warping state inversion analysis of the box girder, the identification method of cross-section damage and prestress loss of the box girder and the calculation results of engineering examples are given. Finally, a safety assessment method of the most unfavorable section based on the principle of influence matrix is proposed. Through the analysis of an example, the safety of the closure segment demolition construction is clarified, and the correctness of the analysis is verified by intelligent monitoring means.

Abstract. With the accelerated aging society in China, the incidence of biliary surgical diseases in the elderly has increased significantly. The clinical characteristics of these patients indicate that improving treatment outcomes and realizing healthy aging are worthy of attention. How to effectively improve the treatment effect of geriatric biliary surgical diseases has attracted widespread attention. This paper reviews and comments on the hotspots and difficulties of biliary surgery in older patients from six aspects: (1) higher morbidity associated with an aging society, (2) prevention and control of pre-operative risks, (3) extending the indications of laparoscopic surgery, (4) urgent standardization of minimally invasive surgery, (5) precise technological progress in hepatobiliary surgery, and (6) guarantee of peri-operative safety. It is of great significance to fully understand the focus of controversy, actively make use of its favorable factors, and effectively avoid its unfavorable factors, for further improving the therapeutic effects of geriatric biliary surgical diseases, and thus benefits the vast older patients with biliary surgical diseases. Accordingly, a historical record with the highest age of 93 years for laparoscopic transcystic common bile duct exploration has been created by us recently.

The cement composite calcium lignosulfonate is used to enhance the mechanical properties and the freeze–thaw resistance of loess. Based on an unconfined compressive test under different freeze–thaw cycles, the influence of cement dosage, curing age, and freeze–thaw cycles on compressive strength are discussed. The results indicate that the strength of loess can increase by up to 13 times, and the loss of strength is reduced from 72% to 28% under the reinforcement of cement dosage and curing age. The loss of strength is mainly concentrated in the initial 5 freeze–thaw cycles, and the structure gradually stabilizes after 10 freeze–thaw cycles. In addition, according to the X-ray diffraction test, it is found that the stabilized loess exhibits a comparatively more stable mineral composition. The scanning electron microscope results reveal that hydration products enveloped the soil particles, forming a mesh structure that strengthens the connection between the soil particles. The freeze–thaw damage makes the small and medium pores turn into large pores in loess, while the stabilized loess changes micro and small pores into small and medium pores, with no large pores found. It is feasible to improve loess with the cement composite calcium lignosulfonate, which can provide references for the reinforcement treatment of loess.

PurposeTo introduce a model for automatic segmentation of thoracic organs at risk (OARs), especially the esophagus, in non-small cell lung cancer radiotherapy, using a novel two-step deep learning network.Materials and methodsA total of 59 lung cancer patients’ CT images were enrolled, of which 39 patients were randomly selected as the training set, 8 patients as the validation set, and 12 patients as the testing set. The automatic segmentations of the six OARs including the esophagus were carried out. In addition, two sets of treatment plans were made on the basis of the manually delineated tumor and OARs (Plan1) as well as the manually delineated tumor and the automatically delineated OARs (Plan2). The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) of the proposed model were compared with those of U-Net as a benchmark. Next, two groups of plans were also compared according to the dose–volume histogram parameters.ResultsThe DSC, HD95, and ASD of the proposed model were better than those of U-Net, while the two groups of plans were almost the same. The highest mean DSC of the proposed method was 0.94 for the left lung, and the lowest HD95 and ASD were 3.78 and 1.16 mm for the trachea, respectively. Moreover, the DSC reached 0.73 for the esophagus.ConclusionsThe two-step segmentation method can accurately segment the OARs of lung cancer. The mean DSC of the esophagus realized preliminary clinical significance (>0.70). Choosing different deep learning networks based on different characteristics of organs offers a new option for automatic segmentation in radiotherapy.

PurposeTo explore fecal immune-related proteins that can be used for colorectal cancer (CRC) diagnosis.Patients and methodsThree independent cohorts were used in present study. In the discovery cohort, which included 14 CRC patients and 6 healthy controls (HCs), label-free proteomics was applied to identify immune-related proteins in stool that could be used for CRC diagnosis. Exploring potential links between gut microbes and immune-related proteins by 16S rRNA sequencing. The abundance of fecal immune-associated proteins was verified by ELISA in two independent validation cohorts and a biomarker panel was constructed that could be used for CRC diagnosis. The validation cohort I included 192 CRC patients and 151 HCs from 6 different hospitals. The validation cohort II included 141 CRC patients, 82 colorectal adenoma (CRA) patients, and 87 HCs from another hospital. Finally, the expression of biomarkers in cancer tissues was verified by immunohistochemistry (IHC).ResultsIn the discovery study, 436 plausible fecal proteins were identified. And among 67 differential fecal proteins (|log2 fold change| > 1, P< 0.01) that could be used for CRC diagnosis, 16 immune-related proteins with diagnostic value were identified. The 16S rRNA sequencing results showed a positive correlation between immune-related proteins and the abundance of oncogenic bacteria. In the validation cohort I, a biomarker panel consisting of five fecal immune-related proteins (CAT, LTF, MMP9, RBP4, and SERPINA3) was constructed based on the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The biomarker panel was found to be superior to hemoglobin in the diagnosis of CRC in both validation cohort I and validation cohort II. The IHC result showed that protein expression levels of these five immune-related proteins were significantly higher in CRC tissue than in normal colorectal tissue.ConclusionA novel biomarker panel consisting of fecal immune-related proteins can be used for the diagnosis of CRC.

Purpose: This study aims to assess the efficacy and safety of Pelnac dermal regeneration template assisted by vacuum sealing drainage (VSD) and a split-thickness skin graft to cover the large skin and soft-tissue defects at foot and ankle. Methods: This study began from March 2013, up to February 2017. A total of 16 patients met the inclusion and exclusion criteria and were included. For every patient, 2 separate operations were performed, the first being thorough debridement of necrotic tissues immediate coverage of VSD at continuous negative pressure suction, and the second being the autologous split-thickness skin graft. At each follow-up, relevant data were documented. Results: The average follow-up was 16.5 months (range, 12 to 42 months). No infections, hematoma, or seroma were observed. 13 out of 16 patients had a complete skin graft “take” (100%). Patients’ satisfaction of esthetic appearance was 76.5 ± 5.2/100. The VSS value was 2.2 ± 2.1, representing a good result. Regarding the sensory recovery, the response “normal or near normal” could be obtained in 14/16 patients. Mean AROM for extension/flexion of the ankle was 48.5 ± 4.8° (range 35–62°), and 93.7% (15/16) of patient could obtain a satisfying functional result. Conclusions: Our report indicated Pelnac provided an effective method for management of complex wounds with underlying bone or tendons exposed.

PurposeTo introduce an end-to-end automatic segmentation model for organs at risk (OARs) in thoracic CT images based on modified DenseNet, and reduce the workload of radiation oncologists.Materials and MethodsThe computed tomography (CT) images of 36 lung cancer patients were included in this study, of which 27 patients’ images were randomly selected as the training set, 9 patients’ as the testing set. The validation set was generated by cross validation and 6 patients’ images were randomly selected from the training set during each epoch as the validation set. The autosegmentation task of the left and right lungs, spinal cord, heart, trachea and esophagus was implemented, and the whole training time was approximately 5 hours. Geometric evaluation metrics including the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD), were used to assess the autosegmentation performance of OARs based on the proposed model and were compared with those based on U-Net as benchmarks. Then, two sets of treatment plans were optimized based on the manually contoured targets and OARs (Plan1), as well as the manually contours targets and the automatically contoured OARs (Plan2). Dosimetric parameters, including Dmax, Dmean and Vx, of OARs were obtained and compared.ResultsThe DSC, HD95 and ASD of the proposed model were better than those of U-Net. The differences in the DSC of the spinal cord and esophagus, differences in the HD95 of the spinal cord, heart, trachea and esophagus, as well as differences in the ASD of the spinal cord were statistically significant between the two models (P0.05). Moreover, compared with manual segmentation, autosegmentation significantly reduced the contouring time by nearly 40.7% (P

Southwest China, which is rich in biodiversity and a wide range of ecosystem services (ESs), is a strong support for local human wellbeing. This area is also one of the key components of the ecological security shelter (ESS) for national ecological security and biodiversity conservation. Due to the combination of man-made and natural factors, Southwest China has suffered serious ecological degradation that directly threatens ecological security which refers to the health status of ecosystems and ESs functions. Mapping ESs-based ecological security patterns (ESPs) is essential for designing conservation strategies that suitably combine regional environment conservation with sustainable utilization. We used the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs Tool) model to identify ecological conservation priority areas which integrated ecological sensitivity (soil erosion sensitivity, rock desertification sensitivity, landslide sensitivity, debris flow sensitivity, and freezing-thawing erosion sensitivity), ecological risk (drought risk, natural disaster risk, and socio-economic risk), and ecological conservation importance (soil conservation, water conservation, and biodiversity conservation importance). In this research, we summarized a new designing framework of ESs-based ESPs. We divided the study area into two zones and four belts including: (A) the alpine steppe and wetland zone, (B) Hengduan Mountain zone, (C) northern shelter belt (Daba-Micang Mountain), (D) central shelter belt (Wumeng-Wuling Mountain), (E) southern shelter belt (southern border of China), and (F) southwestern shelter belt (eastern Himalayas Mountain). Identifying distributions of the ESs-based ESPs has practical significance to improve local human wellbeing and to maintain sustainable development of natural-social ecosystems in Southwest China. Furthermore, ESs-based ESPs are necessary for local administrations to create rationalizing plans on balancing conservation and utilization of natural resources, so that policy-makers can put into place targeted prevention and control measures to limit the development of excessive consumption of natural resources and ecological damages, which is worth promoting.

We propose a practical approach to identify plasmonic absorption profile on back focal plane (BFP) of surface plasmon microscopy (SPM). Compared to previous morphology approaches, the proposed one features: i) it is applicable in BFP images with non-concentricity; ii) the algorithm of Fourier Correlation Analysis (FCA) maximizes SPP while minimizes random coherent noises, which makes it extremely suited for BFP images with significant low-quality; (iii) it takes much less time for one identification process and the entire identification can be operated automatically.

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

Surface plasmon microscopy (SPM) provides the capability of measuring surface properties of subnanometer layers within the diffraction limited order. In a typical SPM, small changes correspond to surface plasmons (SPs) absorption profile variations on a reflecting back focal plane (BFP), which can be monitored in real-time. However, the lack of fast and high-accurate identification method on SPs profiles has posed significant challenges on objective-coupled SPM instruments, and also limited their practical applications in fast phenomenon sensing and image batch processing. Here we propose a morphological method to identify the SPs absorption profiles. It can extract the SPs profile information from experimentally recorded BFP images with low quality properly and automatically. Experimental verification and further discussions are included.

Scars, as the result of abnormal wound-healing response after skin injury, may lead to loss of aesthetics and physical dysfunction. Current clinical strategies, such as surgical excision, laser treatment, and drug application, provide late remedies for scarring, yet it is difficult to eliminate scars. In this review, the functions, roles of multiple polymer scaffolds in wound healing and scar inhibition are explored. Polysaccharide and protein scaffolds, an analog of extracellular matrix, act as templates for cell adhesion and migration, differentiation to facilitate wound reconstruction and limit scarring. Stem cell-seeded scaffolds and growth factors-loaded scaffolds offer significant bioactive substances to improve the wound healing process. Special emphasis is placed on scaffolds that continuously release oxygen, which greatly accelerates the vascularization process and ensures graft survival, providing convincing theoretical support and great promise for scarless healing.

The centrifugal underwater explosion tests and corresponding numerical simulations were carried out to study the laws of shock wave and bubble pulsation. A semiempirical method to determine JWL state equation parameters was given. The influence on numerical results caused by factors such as state equation of water, boundary condition, and mesh size was analyzed by comparing with the centrifugal underwater explosion test results. The results show that the similarity criterion is also suitable in numerical simulation; the shock wave peak pressure calculated by polynomial state equation is smaller than that of shock state equation. However, the maximum bubble radius and the pulsation period calculated by the two equations are almost the same. The maximum bubble radius is mainly affected by the boundary simulating the test model box, and the pulsation period is mainly affected by the artificial cutoff boundary. With the increase of standoff distance of measuring point, the mesh size required for the numerical calculation decreases; the size of the two-dimensional model is recommended to take 1/30 ∼ 1/10 explosion radius. In three-dimensional models, when mesh size is 2 times larger than explosion radius, the bubble motion change in the second pulsation period is not obvious. When mesh size is smaller than 1 time explosive radius, the full period of bubble pulsation can be well simulated, but calculation errors increase slowly and computation time greatly increases, so the three-dimensional mesh size is suggested to take the charge radius. Shock wave peak pressure is basically unaffected by gravity. As the gravity increases, the bubble maximum radius and the first pulsation period both decrease.

In the wireless sensor networks (WSNs) for electric field measurement system under the High-Voltage Direct Current (HVDC) transmission lines, it is necessary to obtain the electric field distribution with multiple sensors. The location information of each sensor is essential to the correct analysis of measurement results. Compared with the existing approach which gathers the location information by manually labelling sensors during deployment, the automatic localization can reduce the workload and improve the measurement efficiency. A novel and practical range-free localization algorithm for the localization of one-dimensional linear topology wireless networks in the electric field measurement system is presented. The algorithm utilizes unknown nodes’ neighbor lists based on the Received Signal Strength Indicator (RSSI) values to determine the relative locations of nodes. The algorithm is able to handle the exceptional situation of the output permutation which can effectively improve the accuracy of localization. The performance of this algorithm under real circumstances has been evaluated through several experiments with different numbers of nodes and different node deployments in the China State Grid HVDC test base. Results show that the proposed algorithm achieves an accuracy of over 96% under different conditions.

The electric field environment under high-voltage direct current (HVDC) transmission lines is an important design consideration. In the wireless sensor networks for electric field measurement system under the HVDC transmission lines, it is necessary to obtain the electric field distribution with multiple sensors. The accurate localisation of sensing nodes is essential to the analysis of measurement results. However, most current techniques are limited to constant measurement environment with fixed and known path-loss exponent. Here, the authors report a localisation method based on received signal strength indication with unknown path-loss exponent for the localisation of one-dimensional linear topology wireless networks in the electric field measurement system. The optimisation method is utilised to obtain the optimal pass-loss parameter without involving the previous environment parameters. Afterwards, simulations are employed to demonstrate the feasibility and the effectiveness of the proposed method by comparing with other methods.

In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system. Using these signals, the field engineers can evaluate the operational states, even predict future behaviors of the monitored system. A novel method of future operational trend forecast of a complex system is proposed in this paper. It is based on empirical wavelet transform (EWT) and autoregressive moving average (ARMA) techniques. Firstly, empirical wavelet transform is used to extract the significant mode from each recorded signal, which reflects one aspect of the operating system. Secondly, the system states are represented by the indicator function which are obtained from those normalized and weighted significant modes. Finally, the future trend is forecast by the parametric model of ARMA. The effectiveness and practicality of the proposed method are verified by a set of numerical experiments.

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.