Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leads to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements on large datasets. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model forms a useful tool to aid alloy development microstructure examinations, and offers significant advantages to address the large datasets associated with highthroughput alloy development approaches

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using Electron Microscopy (EM), as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leads to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements on large datasets. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model forms a useful tool to aid alloy development microstructure examinations, and offers significant advantages to address the large datasets associated with high-throughput alloy development approaches

Differentially expressed serum proteins associated with calcium regulation and hypocalcemia in dairy cows

Objective Hypocalcemia is an important metabolic disease of dairy cows during the transition period, although the effect of hypocalcemia on biological function in dairy cows remains unknown. Methods In this study, proteomic, mass spectrum, bioinformatics and western blotting were employed to identify differentially expressed proteins related to serum Ca concentration. Serum samples from dairy cows were collected at three time points: 3rd days before calving (day −3), the day of calving (day 0), and 3rd days after calving (day +3). According to the Ca concentration on day 0, a total of 27 dairy cows were assigned to one of three groups (clinical, subclinical, and healthy). Samples collected on day −3 were used for discovery of differentially expressed proteins, which were separated and identified via proteomic analysis and mass spectrometry. Bioinformatics analysis was performed to determine the function of the identified proteins (gene ontology and pathway analysis). The differentially expressed proteins were verified by western blot analysis. Results There were 57 differential spots separated and eight different proteins were identified. Vitamin D-binding protein precursor (group-specific component, GC), alpha-2-macroglobulin (A2M) protein, and apolipoprotein A-IV were related to hypocalcemia by bioinformatics analysis. Due to its specific expression (up-regulated in clinical hypocalcemia and down-regulated in subclinical hypocalcemia), A2M was selected for validation. The results were consistent with those of proteomic analysis. Conclusion A2M was as an early detection index for distinguishing clinical and subclinical hypocalcemia. The possible pathogenesis of clinical hypocalcemia caused by GC and apolipoprotein A-IV was speculated. The down-regulated expression of GC was a probable cause of the decrease in calcium concentration

Fracture and Fatigue Analyses of Cracked Structures Using the Iterative Method

It is a quite challenging subject to efficiently perform fracture and fatigue analyses for complex structures with cracks in engineering. To precisely and efficiently study crack problems in practical engineering, an iterative method is developed in this study. The overall structure which contains no crack is analyzed by the traditional finite element method (FEM), and the crack itself is analyzed using analytical solution or other numerical solutions which are effective and efficient for solving crack problems. An iteration is carried out between the two abovementioned solutions, and the original crack problem could be solved based on the superposition principle. Several typical crack problems are studied using the present method, showing very high precision and efficiency of this method when making fracture and fatigue analyses of structures

Numerical Study on the Fracture Properties of Concrete Shield Tunnel Lining Segments

Shield tunnel lining structure is usually under very complex loading conditions in the underground space. As a kind of the common concrete structures, any defect in the tunnel lining segment may deteriorate its bearing capacity and even cause severe disasters. Three-dimensional numerical models of shield tunnel lining segments with initial cracks are built using the Symmetric Galerkin Boundary Element Method- (SGBEM-) Finite Element Method (FEM) Alternating Method. The cracking load and ultimate load of the tunnel segments are obtained, and crack propagation under fatigue load is also simulated by employing the Paris Fatigue Law. Results show that loading eccentricity has a very large influence on the bearing capacity of the cracked lining segment; the larger the loading eccentricity, the smaller the bearing capacity. Deformation and damage of the lining segment show obvious phases, which consist of the initial crack, cracking stage, steady crack propagation, unsteady crack propagation, and eventual failure

V\=arta: A Large-Scale Headline-Generation Dataset for Indic Languages

We present V\=arta, a large-scale multilingual dataset for headline generation in Indic languages. This dataset includes 41.8 million news articles in 14 different Indic languages (and English), which come from a variety of high-quality sources. To the best of our knowledge, this is the largest collection of curated articles for Indic languages currently available. We use the data collected in a series of experiments to answer important questions related to Indic NLP and multilinguality research in general. We show that the dataset is challenging even for state-of-the-art abstractive models and that they perform only slightly better than extractive baselines. Owing to its size, we also show that the dataset can be used to pretrain strong language models that outperform competitive baselines in both NLU and NLG benchmarks.Comment: Findings of ACL 202

Comparative Study of Park Evaluation Based on Text Analysis of Social Media: A Case Study of 50 Popular Parks in Beijing

With China’s urban renewal, parks have developed into significant green recreational areas in cities. This paper analyzed social media texts and compared the evaluation outcomes of the 50 most popular urban parks in Beijing from various perspectives, such as the characteristics of various groups of people, park types, and the spatial and temporal distribution characteristics of recreational activities. The importance–performance analysis method was used to analyze the main factors affecting visitors’ satisfaction with parks. The research found the following: (1) Positive evaluation of parks was related to environmental construction, event organization, etc., and negative evaluations focused on ticket supply, consumer spending, etc. (2) Visitors of different genders and from different regions focused on different aspects of parks. (3) In terms of traffic accessibility, historical and cultural display, parent–child activity organization, and ecological environment experience, people had diverse demands from various types of parks. (4) People were more likely to visit parks located within the range of all green belts in springs and parks located in the second green isolation belt in the fall. (5) The number of non-holiday reviews of parks was higher than that of holiday reviews. (6) Managers could improve visitor satisfaction by improving the infrastructure and management of parks

Smart nano-PROTACs reprogram tumor microenvironment for activatable photo-metabolic cancer immunotherapy

Protease inhibitors can modulate intratumoral metabolic processes to reprogram the immunosuppressive tumor microenvironment (TME), which however suffer from the limited efficacy and off-targeted side effects. We report smart nano-proteolysis targeting chimeras (nano-PROTACs) with phototherapeutic ablation and cancer-specific protein degradation to reprogram the TME for photo-metabolic cancer immunotherapy. This nano-PROTAC has a semiconducting polymer backbone linked with a cyclooxygenase 1/2 (COX-1/2)-targeting PROTAC peptide (CPP) via a cathepsin B (CatB)-cleavable segment. CPP can be activated by the tumor-overexpressed CatB to induce the degradation of COX-1/2 via the ubiquitin-proteasome system. The persistent degradation of COX-1/2 depletes their metabolite prostaglandin E2 which is responsible for activation of immune suppressor cells. Such a smart PROTAC strategy synergized with phototherapy specifically reprograms the immunosuppressive TME and reinvigorates antitumor immunity.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)K.P. thanks Singapore Ministry of Education, Academic Research Fund Tier 1 (2019-T1-002-045, RG125/19, RT05/ 20), Academic Research Fund Tier 2 (MOE2018-T2-2-042; MOE-T2EP30220-0010), and A*STAR SERC AME Programmatic Fund (SERC A18 A8b0059) for the financial support

A contrast-enhanced CT-based whole-spleen radiomics signature for early prediction of oxaliplatin-related thrombocytopenia in patients with gastrointestinal malignancies: a retrospective study

Background Thrombocytopenia is a common adverse event of oxaliplatin-based chemotherapy. Grade 2 or higher oxaliplatin-related thrombocytopenia may result in dose reduction, discontinuation or delay initiation of chemotherapy and may adversely affect the therapeutic efficacy and even overall survival of patients. Early recognition of patients at risk of developing grade 2 or higher thrombocytopenia is critical. However, to date there is no well-established method to early identify patients at high risk. The aims of this study were to develop and validate a contrast-enhanced CT-based whole-spleen radiomics signature for early prediction of grade 2 or higher thrombocytopenia in patients with gastrointestinal malignancies treated with oxaliplatin-based chemotherapy and to explore the incremental value of combining the radiomics signature and conventional clinical factors for risk prediction. Methods A total of 119 patients with gastrointestinal malignancies receiving oxaliplatin-based chemotherapy from March 2017 to December 2020 were retrospectively included and randomly divided into a training cohort (n = 85) and a validation cohort (n = 34). Grade 2 or higher thrombocytopenia occurred in 26.1% of patients (22 and nine patients in the training and validation cohort, respectively) with a median time interval of 101 days from the start of chemotherapy. The whole-spleen radiomics features were extracted on the portal venous phase of the first follow-up CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select radiomics features and to build the radiomics signature for the prediction of grade 2 or higher thrombocytopenia. A clinical model that included clinical factors only and a clinical-radiomics model that incorporated clinical factors and radiomics signature were constructed. The performances of both models were evaluated and compared in the training, validation and the whole cohorts. Results The radiomics signature yielded favorable performance in predicting grade 2 or higher thrombocytopenia, with the area under the curve (AUC), sensitivity and specificity being 0.865, 81.8% and 84.1% in the training cohort and 0.747, 77.8% and 80.0% in the validation cohort. The AUCs of the clinical-radiomics model in the training and validation cohorts reached 0.913 (95% CI [0.720–0.935]) and 0.867 (95% CI [0.727–1.000]), greater than the AUCs of the clinical model. Integrated discrimination improvement (IDI) index showed that incorporating radiomic signature into conventional clinical factors significantly improved the predictive accuracy by 17.0% (95% CI [4.9%–29.1%], p = 0.006) in the whole cohort. Conclusions Contrast-enhanced CT-based whole-spleen radiomics signature might serve as an early predictor for grade 2 or higher thrombocytopenia during oxaliplatin-based chemotherapy in patients with gastrointestinal malignancies and provide incremental value over conventional clinical factors

Bearing Fault Diagnosis Based on Small Sample Learning of Maml–Triplet

Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faults rarely occur, resulting in less fault data. Therefore, it is necessary to study small sample fault data. For the case of less fault data, the Maml–Triplet fault classification learning framework based on the combination of maml and the triplet neural network is proposed. In the framework of Maml-Triplet fault classification, firstly, an initial signal feature extractor is obtained using the Maml training method. Secondly, the feature vectors corresponding to signal data are obtained using depth distance measurement learning in the triplet neural network, and the fault type is judged based on the feature vectors of unknown signal. The results show that the accuracy of the Maml–Triplet model is 2% higher than that of the triplet model alone and 5% higher than that of the Maml–CNN meta learning method. When there are fewer data samples, the accuracy gap is more obvious. Therefore, in the case of less data, the Maml–Triplet model has an excellent fault identification ability