Enhanced singular jet formation in oil-coated bubble bursting

Bubbles are ubiquitous in many natural and engineering processes, and bubble bursting aerosols are of particular interest because of their critical role in mass and momentum transfer across interfaces. All prior studies claim that bursting of a millimeter-sized bare bubble at an aqueous surface produces jet drops with a typical size of $\boldsymbol{O}$(100 \si{\micro\relax}m), much larger than film drops of $\boldsymbol{O}$(1 \si{\micro\relax}m) from the disintegration of a bubble cap. Here, we document the hitherto unknown phenomenon that jet drops can be as small as a few microns when the bursting bubble is coated by a thin oil layer. We provide evidence that the faster and smaller jet drops result from the singular dynamics of the oil-coated cavity collapse. The unique air-oil-water compound interface offers a distinct damping mechanism to smooth out the precursor capillary waves during cavity collapse, leading to a more efficient focusing of the dominant wave and thus allowing singular jets over a much wider parameter space beyond that of a bare bubble. We develop a theoretical explanation for the parameter limits of the singular jet regime by considering the interplay among inertia, surface tension, and viscous effects. As such contaminated bubbles are widely observed, the previously unrecognized fast and small contaminant-laden jet drops may enhance bubble-driven flux across the interface, contributing to the aerosolization and airborne transmission of bulk substances

Object Impacted and Transported by Dry Granular Flow: 3D MPM-SDEM

Granular geophysical flows pose a significant threat to human lives and infrastructure in mountainous regions globally. Property and victims impacted by these flows often get carried away and buried. Quickly locating the potential location of victims is critical for post-disaster recovery and rescue. However, the trajectory of object impacted by granular flows is difficult to predict because of the scale disparity between the sizes of granular particles and the object being transported. To deal with this multi-scale problem, a continuum-discrete solver, specifically the material-discrete element method (MPM-SDEM), was developed to simulate the trajectory of a cube (the object) impacted by a dry granular flow. Simulated results are used to understand the trajectory of objects under different flow and impact dynamics

A novel target state detection method for accurate cardiopulmonary signal extraction based on FMCW radar signals

Frequency-modulated continuous wave radar is capable of constant, real-time detection of human presence and monitoring of cardiopulmonary signals such as respiration and heartbeat. In highly cluttered environments or when the human body moves randomly, noise signals may be relatively large in some range bins, making it crucial to accurately select the range bin containing the target cardiopulmonary signal. In this paper, we propose a target range bin selection algorithm based on a mixed-modal information threshold. We introduce a confidence value in the frequency domain to determine the state of the human target and employ the range bin variance in the time domain to determine the range bin change status of the target. The proposed method accurately detects the state of the target and effectively selects the range bin containing the cardiopulmonary signal with a high signal-to-noise ratio. Experimental results demonstrate that the proposed method achieves better accuracy in cardiopulmonary signal rate estimation. Moreover, the proposed algorithm is lightweight in data processing and has good real-time performance

Estimating cancer incidence based on claims data from medical insurance systems in two areas lacking cancer registries in China.

BACKGROUND: We aimed to establish a Medical-Insurance-System-based Cancer Surveillance System (MIS-CASS) in China and evaluate the completeness and timeliness of this system through reporting cancer incidence rates using claims data in two regions in northern and southern China. METHODS: We extracted claims data from medical insurance systems in Hua County of Henan Province, and Shantou City in Guangdong Province in China from Jan 1, 2012 to Jun 30, 2019. These two regions have been considered to be high risk regions for oesophageal cancer. We developed a rigorous procedure to establish the MIS-CASS, which includes data extraction, cleaning, processing, case ascertainment, privacy protection, etc. Text-based diagnosis in conjunction with ICD-10 codes were used to determine cancer diagnosis. FINDINGS: In 2018, the overall age-standardised (Segi population) incidence rates (ASR World) of cancer in Hua County and Shantou City were 167·39/100,000 and 159·78/100,000 respectively. In both of these areas, lung cancer and breast cancer were the most common cancers in males and females respectively. Hua County is a high-risk region for oesophageal cancer (ASR World: 25·95/100,000), whereas Shantou City is not a high-risk region for oesophageal cancer (ASR World: 11·43/100,000). However, Nanao island had the highest incidence of oesophageal cancer among all districts and counties in Shantou (ASR World: 36·39/100,000). The age-standardised male-to-female ratio for oesophageal cancer was lower in Hua County than in Shantou (1·69 vs. 4·02). A six-month lag time was needed to report these cancer incidences for the MIS-CASS. INTERPRETATION: MIS-CASS efficiently reflects cancer burden in real-time, and has the potential to provide insight for improvement of cancer surveillance in China. FUNDING: The National Key R&D Program of China (2016YFC0901404), the Digestive Medical Coordinated Development Center of Beijing Municipal Administration of Hospitals (XXZ0204), the Sanming Project of Shenzhen (SZSM201612061), and the Shantou Science and Technology Bureau (190829105556145, 180918114960704)

Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer

BackgroundClinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.MethodsA total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists.ResultsThe DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort.ConclusionThe proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa

Covert Information Mapped Spatial and Directional Modulation toward Secure Wireless Transmission

Recently, the concept of spatial and direction modulation (SDM) has been developed to reap the advantages of both spatial modulation (SM) and directional modulation (DM). On the one hand, DM ensures the transmission security at the expected direction. On the other hand, the structure of SM-aided distributed receivers can enhance the security even if the eavesdropper is located in the same direction as the legitimate receiver. However, the above advantages are achieved based on the assumption that the eavesdropper is not equipped with distributed receivers. On the other hand, the information security can no longer be guaranteed when the eavesdropper is also equipped with distributed receivers. To alleviate this problem, we considered a joint design of SDM and covert information mapping (CIM) in order to conceive of a more robust structure of CIM-SDM. Furthermore, both the detection performances at the eavesdropper and the legitimate user were quantified through theoretical derivation. In general, both the analysis and simulation results supported that the proposed CIM-SDM structure provides more robust secure performance compared to the original SDM, even if the extreme condition of distributed receivers at the eavesdropper is considered, at the cost of moderate performance loss at the legitimate user

Lithium inhibits tumorigenic potential of PDA cells through targeting hedgehog-GLI signaling pathway.

Hedgehog signaling pathway plays a critical role in the initiation and development of pancreatic ductal adenocarcinoma (PDA) and represents an attractive target for PDA treatment. Lithium, a clinical mood stabilizer for mental disorders, potently inhibits the activity of glycogen synthase kinase 3β (GSK3β) that promotes the ubiquitin-dependent proteasome degradation of GLI1, an important downstream component of hedgehog signaling. Herein, we report that lithium inhibits cell proliferation, blocks G1/S cell-cycle progression, induces cell apoptosis and suppresses tumorigenic potential of PDA cells through down-regulation of the expression and activity of GLI1. Moreover, lithium synergistically enhances the anti-cancer effect of gemcitabine. These findings further our knowledge of mechanisms of action for lithium and provide a potentially new therapeutic strategy for PDA through targeting GLI1

Optimization of the Anaerobic-Anoxic-Oxic Process by Integrating ASM2d with Pareto Analysis of Variance and Response Surface Methodology

Wastewater treatment plants (WWTPs) are high-energy-consuming units. Reasonable operation strategies can enable WWTPs to meet discharge standards while reducing the operating cost. In this study, the activated sludge model 2d (ASM2d), Pareto analysis of variance (ANOVA), and response surface methodology (RSM) were jointly used to simulate and optimize the operation of a lab-scale anaerobic-anoxic-oxic (AAO) reactor. The optimization objective was to determine the optimal design and operational parameters (DOPs) that could enhance both pollutant removal and energy saving. The DOPs that had significant influence on the optimization objective, such as sludge retention time (SRT), dissolved oxygen (DO), and the ratio of biodegradable chemical oxygen demand to total nitrogen (BCOD/TN), were identified by Pareto ANOVA. The optimal DOPs with SRT of 15 days, DO concentration of 0.5 mg/L, and BCOD/TN of 5.21 were determined by RSM. Under the optimal conditions, the removal efficiencies of NH4+-N, total nitrogen (TN), and total phosphorus (TP) were 96.2%, 76.8%, and 92.8%, respectively, and the annual operating cost was $26.4. Furthermore, this combination of DOPs was validated using a pilot-scale AAO system. The TN and TP removal efficiencies were improved by 11.0% and 5.0%, respectively, and the annual operating cost could be reduced by 15.0%. Overall, this study confirmed that the method integrating ASM2d with Pareto ANOVA and RSM was effective in optimizing wastewater treatment processes