Effect of End Friction on the Dynamic Compressive Mechanical Behavior of Concrete under Medium and Low Strain Rates

The objective of the study is to examine the quantitative influence of end friction on the dynamic mechanical behavior of concrete under medium and low strain rates. Considering the concrete heterogeneity, a mesoscale mechanical model was established to study the confinement effect of end friction, in which the concrete was assumed to be composed of aggregates, mortar matrix, and the interfacial transition zones between them. The friction behavior was utilized to describe the interaction between the concrete specimens and loading apparatus. The dynamic axial compressive mechanical behavior of concrete subjected to different medium and low strain rates and friction coefficients was simulated. Furthermore, the confinement mechanism of end friction on the compressive dynamic increase factor (DIF) of concrete was studied. The simulation results indicate that with the increase of end friction coefficient, the uniaxial compressive strength of concrete first increases and then becomes stable; the end friction confinement changes the local stress state and damage distribution of concrete, and it thus contributes to the increase in compressive strength of concrete; the friction contribution factor presents a descending tendency with increasing the strain rate and decreases obviously when the end friction coefficient increases

A Curriculum Batching Strategy for Automatic ICD Coding with Deep Multi-Label Classification Models

The International Classification of Diseases (ICD) has an important role in building applications for clinical medicine. Extremely large ICD coding label sets and imbalanced label distribution bring the problem of inconsistency between the local batch data distribution and the global training data distribution into the minibatch gradient descent (MBGD)-based training procedure for deep multi-label classification models for automatic ICD coding. The problem further leads to an overfitting issue. In order to improve the performance and generalization ability of the deep learning automatic ICD coding model, we proposed a simple and effective curriculum batching strategy in this paper for improving the MBGD-based training procedure. This strategy generates three batch sets offline through applying three predefined sampling algorithms. These batch sets satisfy a uniform data distribution, a shuffling data distribution and the original training data distribution, respectively, and the learning tasks corresponding to these batch sets range from simple to complex. Experiments show that, after replacing the original shuffling algorithm-based batching strategy with the proposed curriculum batching strategy, the performance of the three investigated deep multi-label classification models for automatic ICD coding all have dramatic improvements. At the same time, the models avoid the overfitting issue and all show better ability to learn the long-tailed label information. The performance is also better than a SOTA label set reconstruction model

Effect of Mo content on microstructure and mechanical properties of CoCrFeNi Series high-entropy alloys

In this paper, the microstructure and mechanical properties of CoCrFeNiMox (x = 0, 0.1, 0.3, 0.5, 0.7, and 1.0) high-entropy alloys (HEAs) prepared with high-vacuum arc melting method were studied in detail. After Mo-doping, the strengthening mechanism of HEAs mainly included the component segregation strengthening and the second phase strengthening. Cr and Mo elements were enriched at the grain boundary of HEAs, which leaded to the formation of σ phase and strengthens the alloys. The microhardness and strength increased with Mo contents, while the elongation decreased gradually. Among which, the higher microhardness, yield strength, and ultimate tensile strength of CoCrFeNiMo0.3 alloy reach 205.96 HV, 292.22 MPa, and 593.25 MPa, respectively, in compared with other alloys. More importantly, the percentage elongation of which remains 37.36%, and the microhardness distribution was uniform with an average value of 7.4 GPa and an elastic modulus of 263.6 GPa. The research findings presented in this paper could serve as a valuable theoretical basis and practical foundations for the strengthening efforts of face centered cubic (FCC) HEAs

Immune Cell Types and Secreted Factors Contributing to Inflammation-to-Cancer Transition and Immune Therapy Response

Summary: Although chronic inflammation increases many cancers’ risk, how inflammation facilitates cancer development is still not well studied. Recognizing whether and when inflamed tissues transition to cancerous tissues is of utmost importance. To unbiasedly infer molecular events, immune cell types, and secreted factors contributing to the inflammation-to-cancer (I2C) transition, we develop a computational package called “SwitchDetector” based on liver, gastric, and colon cancer I2C data. Using it, we identify angiogenesis associated with a common critical transition stage for multiple I2C events. Furthermore, we infer infiltrated immune cell type composition and their secreted or suppressed extracellular proteins to predict expression of important transition stage genes. This identifies extracellular proteins that may serve as early-detection biomarkers for pre-cancer and early-cancer stages. They alone or together with I2C hallmark angiogenesis genes are significantly related to cancer prognosis and can predict immune therapy response. The SwitchDetector and I2C database are publicly available at www.inflammation2cancer.org. : Chen et al. develop the SwitchDetector package for transcriptome module detection during inflammation-to-cancer (I2C) stage transitions. They show that angiogenesis is a common critical event for I2C in multiple cancers. The data also suggest that immune cells and secreted cytokines contribute to the I2C transition. Keywords: inflammation, cancer, network analysis, microenvironment, secreted factor, biomarker, cancer transition, immune therapy, cancer surviva

Strained epitaxy of monolayer transition metal dichalcogenides for wrinkle arrays

Wrinkling two-dimensional (2D) transition metal dichalcogenides (TMDCs) provides a mechanism to adjust the physical and chemical properties as per need. Traditionally, TMDCs wrinkles achieved by transferring exfoliated materials on prestretched polymer suffer from poor control and limited sample area, which significantly hinders desirable applications. Herein, we fabricate large-area monolayer TMDCs wrinkle arrays directly on the m-quartz substrate using strained epitaxy. The uniaxial thermal expansion coefficient mismatch between the substrate and TMDCs materials enables the generation of large uniaxial thermal strain. By quenching the TMDCs after growth, this uniaxial thermal strain can be quickly released as a form of wrinkle arrays along the [0001] quartz direction. Using WS2 as a model system, the size of as-grown wrinkles can be finely modulated within sub-100 nm by changing the quenching temperature. These WS2 wrinkles can be locally folded and form various multilayer structures with odd layer numbers during the transfer process. Besides, the corrugated structures in WS2 wrinkles induce significant changes to optical properties including anisotropic Raman response, enhanced photoluminescence, and second harmonic generation emissions. Furthermore, these wrinkle arrays exhibit enhanced chemical reactivity that can be selectively engineered to ribbon arrays with improved electrocatalytic performance. The developed strategy of strained epitaxy here should enable flexibility in the design of more sophisticated 2D-based structures, offering a simple but effective way toward the modulation of properties with enhanced performances

Impact of the COVID‐19 pandemic on cancer healthcare utilization in southwestern China to March 2021

Abstract Background Oncological care has been disrupted worldwide during the COVID‐19 pandemic. We aimed to quantify the long‐term impact of the pandemic on cancer care utilization and to examine how this impact varied by sociodemographic and clinical factors in southwestern China, where the Dynamic Zero‐COVID Strategy was implemented. This strategy mainly included lockdowns, stringent testing, and travel restrictions to prevent the spread of COVID‐19. Method We identified 859,497 episodes of the utilization of cancer care from electronic medical records between January 1, 2019, and March 31, 2021, from the cancer center of a tertiary hospital serving an estimated population of 8.4 million in southwestern China. Changes in weekly utilization were evaluated via segmented Poisson regression across service categories, stratified by cancer type and sociodemographic factors. Results A sharp reduction in utilization of in‐person cancer services occurred during the first week of the pandemic outbreak in January 2020, followed by a quick rebound in February 2020. Although there were few COVID‐19 cases from March 2020 until this analysis, the recovery of most in‐person services was slow and remained incomplete as of March 31, 2021. The exceptions were outpatient radiation and surgery, which increased and exceeded pre‐pandemic levels, particularly among lung cancer patients; meanwhile, telemedicine utilization increased substantially after the onset of the pandemic. Care disruptions were most prominent for women, rural residents, uninsured, and breast cancer patients. Conclusions As of March 2021, despite few COVID‐19 cases, the COVID‐19 pandemic has had a strong and continuing impact on in‐person oncology care utilization in southwestern China under the Dynamic Zero‐COVID Strategy. Equitable and timely access to cancer care requires adjustment in strict policies for COVID‐19 prevention and control, as well as targeted remedies for the most vulnerable populations during and beyond the pandemic. Future studies should monitor the long‐term effects of the COVID‐19 pandemic and response strategies on cancer care and outcomes