Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation

Binding Strength and Hydrogen Bond Numbers between COVID-19 RBD and HVR of Antibody

The global battle against the COVID-19 pandemic relies strongly on the human defense of antibody, which is assumed to bind the antigen’s receptor binding domain (RBD) with its hypervariable region (HVR). Due to the similarity to other viruses such as SARS, however, our understanding of the antibody-virus interaction has been largely limited to the genomic sequencing, which poses serious challenges to containment and rapid serum testing. Based on the physical/chemical nature of the interaction, infrared spectroscopy was employed to reveal the binding disparity, the real cause of the antibody-virus specificity at the molecular level, which is inconceivable to be investigated otherwise. Temperature dependence was discovered in the absorption value from the 1550 cm−1 absorption band, attributed to the hydrogen bonds by carboxyl/amino groups, binding the SARS-CoV-2 spike protein and closely resembled SARS-CoV-2 or SARS-CoV-1 antibodies. The infrared absorption intensity, associated with the number of hydrogen bonds, was found to increase sharply between 27 °C and 31 °C, with the relative absorbance matching the hydrogen bonding numbers of the two antibody types (19 vs. 12) at 37 °C. Meanwhile, the ratio of bonds at 27 °C, calculated by thermodynamic exponentials, produces at least 5% inaccuracy. Beyond genomic sequencing, the temperature dependence, as well as the bond number match at 37 °C between relative absorbance and the hydrogen bonding numbers of the two antibody types, is not only of clinical significance in particular but also as a sample for the physical/chemical understanding of vaccine–antibody interactions in general

Numerical modeling of failed rifts in the northern South China Sea margin: implications for continental rifting and breakup

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Failed rifts record important information of continental extension and breakup process in the northern South China Sea (SCS) margin. The Tainan Southern Depression and the Baiyun Sag to the east are characterized with lower-crust high-velocity anomalies (LCHVA), and intracrust detachment faults, whereas the Xisha Trough to the west develops on a larger scale with crust-cutting normal faults and absence of LCHVA. These contrasts indicate different rifting processes between the northeastern and northwestern SCS. 2D numerical modeling is performed to understand the formation mechanism of these failed rifts. Two types of mechanisms are proposed: I) syn-rift competitive type and II) rift migration type with a half extension rate of 2 cm/yr and 1.5 cm/yr, respectively. In type I, two rifts develop initially on the shoulders of the weak zone, but they compete with each other during extension. One rift becomes dominant to furnish the final breakup, whereas the other one is abandoned. The crust structure of this type fits the observations in the Baiyun Sag and the Tainan Southern Depression. However, in type II, only one rift develops at the beginning. The initial rifting center will migrate and the final continental breakup will occur at a place far from the initial rifting location. In this type, normal faults cut through whole crust and wide extensional margins will form, such as observed in the Xisha Trough. Our results suggest that the depth-dependent extension of the SCS is strongly heterogeneous, resulting primarily from varying extensional rates.Peer ReviewedPostprint (author's final draft

Protein Microspheres with Unique Green and Red Autofluorescence for Noninvasively Tracking and Modeling Their in Vivo Biodegradation

Bovine serum albumin (BSA) microspheres were prepared through a facile and low-cost route including a high-speed dispersion of BSA in cross-linking solution followed by spray drying. Interestingly the as-prepared BSA microspheres possess unique blue-green, green, green-yellow, and red fluorescence when excited by specific wavelengths of laser or LED light. The studies of UV–visible reflectance spectra and fluorescence emission spectra indicated that four classes of fluorescent compounds are presumably formed during the fabrication processes. The formation and the potential contributors for the unique green and red autofluorescence were also discussed and proposed though the exact structures of the fluorophores formed remain elusive due to the complexity of the protein system. The effect of spray-drying conditions on the morphology of spray-dried samples was investigated and optimized. FTIR was further employed to characterize the formation of the functional groups in the as-prepared autofluorescent microspheres. Good in vitro and in vivo biocompatibility was demonstrated by the cytotoxicity test on the A549 cancer cells and tissue histological analysis, respectively. The autofluorescent BSA microspheres themselves were then applied as a novel tracer for convenient tracking/modeling of the biodegradation of autofluorescent BSA microspheres injected into mouse model based on noninvasive, time-dependent fluorescence images of the mice, in which experimental data are in good agreement with the proposed mathematical model. All these studies indicate that the as-developed protein microspheres exhibiting good biocompatibility, biodegradability, and unique autofluorescence, can significantly broaden biomedical applications of fluorescent protein particles

An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Background: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms