Mathematical Model and Parameter Estimation for Tumor Growth

We consider a tumor growth model initially proposed by Ward and King in 1997. Our primary goal is to find an efficient and accurate numerical method for the identification of parameters in the model (an inverse problem) from measurements of the evolving tumor over time. The so-called direct problem, in this case, is to solve a system of coupled nonlinear partial differential equations for given fixed values of the unknown parameters. We compare several derivative-free and gradient-based methods for the solution of the inverse problem which is formulated as an optimization problem with the system of partial differential equations (PDEs) as the constraint. We modify the original model by incorporating uncertainty in one of the parameters. We use the Monte Carlo method based sampling strategy, coupled with optimization methods, for the uncertainty quantification

Multiscale Hierarchical Structure and Laminated Strengthening and Toughening Mechanisms

Metal matrix composites with multiscale hierarchical structure and laminated structure have been developed to provide a novel route to achieve high strength, toughness and ductility. In this chapter, a lot of scientific research has been carried out in the preparation, processing, properties and application of metal matrix composite. Many toughening mechanisms and fracture behavior of composites with multiscale hierarchical structure and laminated structure are overviewed. It is revealed that elastic property and yield strength of laminated composites follow the “rule of average.” However, the estimation of fracture elongation and fracture toughness is complex, which is inconsistent with the “rule of average.” The fracture elongation of laminated composites is related to the layer thickness size, interface, gradient structure, strain hardening exponent, strain rate parameter and tunnel crack, which are accompanied with crack deflection, crack blunting, crack bridging, stress redistribution, local stress deformation, interfacial delamination crack and so on. The concept of laminated composites can be extended by applying different combination of individual layer, and provides theoretical as well as experimental fundamentals on strengthening and toughening of metal matrix composites

Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling

Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves performing a large number of sampling steps, which impedes the possibility of real-time sample synthesis. Traditional accelerated sampling algorithms via knowledge distillation rely on pre-trained model weights and discrete time step scenarios, necessitating additional training sessions to achieve their goals. To address these issues, we propose the Catch-Up Distillation (CUD), which encourages the current moment output of the velocity estimation model ``catch up'' with its previous moment output. Specifically, CUD adjusts the original Ordinary Differential Equation (ODE) training objective to align the current moment output with both the ground truth label and the previous moment output, utilizing Runge-Kutta-based multi-step alignment distillation for precise ODE estimation while preventing asynchronous updates. Furthermore, we investigate the design space for CUDs under continuous time-step scenarios and analyze how to determine the suitable strategies. To demonstrate CUD's effectiveness, we conduct thorough ablation and comparison experiments on CIFAR-10, MNIST, and ImageNet-64. On CIFAR-10, we obtain a FID of 2.80 by sampling in 15 steps under one-session training and the new state-of-the-art FID of 3.37 by sampling in one step with additional training. This latter result necessitated only 620k iterations with a batch size of 128, in contrast to Consistency Distillation, which demanded 2100k iterations with a larger batch size of 256. Our code is released at https://anonymous.4open.science/r/Catch-Up-Distillation-E31F

A Three-Dimensional Melamine Sponge Modified with MnOx Mixed Graphitic Carbon Nitride for Photothermal Catalysis of Formaldehyde

Much attention has been paid to developing effective visible light catalytic technologies for VOC oxidation without requiring extra energy. In this paper, a series of sponge-based catalysts with rich three-dimensional porosity are synthesized by combining MnOx and graphitic carbon nitride (GCN) with commercial melamine sponges (MS) coated with polydopamine (PDA), demonstrating excellent photothermal catalytic performance for formaldehyde (HCHO). The three-dimensional porous framework of MS can provide a good surface for material modification and a reliable interface for gas-solid interaction. The grown layer of PDA framework not only increases the near-infrared wavelength absorption for improving the light-to-heat conversion of catalysts, but also brings excellent adhesion for the subsequent addition of MnOX and GCN. The efficient formaldehyde oxidation is attributed to the sufficient oxygen vacancies generated by co-loaded MnOX and GCN, which is conducive to the activation of more O2− in the oxidation process. As the surface temperature of catalyst rapidly increases to its maximum value at ca. 115 °C under visible light irradiation, the HCHO concentration drops from 160 ppm to 46 ppm within 20 min. The reaction mechanism is certified as a classical Mars-van Krevelen mechanism based on the photo-induced thermal catalysis process

Coupling mass balance analysis and multi-criteria ranking to assess the commercial-scale synthetic alternatives: a case study on glyphosate

Department of Science, Technology and Standards of China's Ministry of Environmental Protection [201009059]; China's Ministry of Science and Technology [2006BAC02A16]Quantitative and systematical assessment of the greenness of synthetic alternatives is one of the key topics of green chemistry. By coupling mass balance analysis and multi-criteria decision analysis, the paper seeks to assess the greenness of three commercial-scale production processes of a broad spectrum herbicide named glyphosate on the basis of seven assessment criteria and sixteen metrics. The seven criteria include mass intensity, efficiency of four core elements (i.e., carbon, nitrogen, phosphorous, and chlorine), energy consumption, nature of the industrial waste, cost of raw materials, and toxicity of materials. The multi-criteria decision method is applied to rank the greenness of glyphosate's three synthetic alternatives in a comprehensive, aggregate manner. Our findings highlight the discrepancy between greenness-driven alternative and cost-driven alternative in fine chemical production. At present, the actual choice of glyphosate production process in China remains dominated by the economic criteria rather than a more comprehensive, balanced set of criteria spanning economic profitability, environmental soundness, and social responsibility. Nonetheless, the underlining research method is relevant to the choice of synthetic alternatives of other fine chemicals

Estimating PM_2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China

Fine particulate matter (PM2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM2.5. Daily surface PM2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R2) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m3, and mean absolution error (MAE) of 8.42 μg/m3. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM2.5 concentrations decrease of 3 to 7 μg/m3 in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM2.5 values higher than 35 μg/m3 and the maximum reaching 95.57 μg/m3. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas

Bifidobacterium adolescentis Exerts Strain-Specific Effects on Constipation Induced by Loperamide in BALB/c Mice

Constipation is one of the most common gastrointestinal complaints worldwide. This study was performed to determine whether Bifidobacterium adolescentis exerts inter-strain differences in alleviating constipation induced by loperamide in BALB/c mice and to analyze the main reasons for these differences. BALB/c mice underwent gavage with B. adolescentis (CCFM 626, 667, and 669) once per day for 17 days. The primary outcome measures included related constipation indicators, and the secondary outcome measures were the basic biological characteristics of the strains, the concentration changes of short-chain fatty acids in feces, and the changes in the fecal flora. B. adolescentis CCFM 669 and 667 relieved constipation symptoms by adhering to intestinal epithelial cells, growing quickly in vitro and increasing the concentrations of propionic and butyric acids. The effect of B. adolescentis on the gut microbiota in mice with constipation was investigated via 16S rRNA metagenomic analysis. The results revealed that the relative abundance of Lactobacillus increased and the amount of Clostridium decreased in the B. adolescentis CCFM 669 and 667 treatment groups. In conclusion, B. adolescentis exhibits strain-specific effects in the alleviation of constipation, mostly due to the strains’ growth rates, adhesive capacity and effects on the gut microbiome and microenvironment

An ultra-thin high-efficiency plasmonic metalens with symmetric split ring transmitarray metasurfaces

Metasurface lenses (or metalenses) have aroused great attentions and efforts in the community of metamaterials or metasurfaces due to its ultrathin device dimension and superior focusing performances. High-efficiency transmissive metalenses with an ultra-thin device thickness are an important aspect especially in the low frequency by using plasmonic transmitarray antennas. In this paper, an ultra-thin plasmonic metalens with only 0.1λ (λ is working wavelength, the aperture size is 7λ) device thickness is designed by changing the radius of the proposed symmetric complementary split ring resonator antenna metasurfaces. Thanks to its high transmittance and large phase shift of the plasmonic meta-atoms, the designed metalens achieves both high transmissive efficiency of 80% and high focusing efficiency of 50% on the focal plane of F = 4.6λ in the simulations. The designed ultra-thin plasmonic metalens has a moderate large numerical aperture of 0.67 (NA = 0.67). In order to verify its high working efficiency of the proposed plasmonic metalens, a sample is also fabricated and a much higher focusing efficiency of 65% is realized in the measurements. The influence of the open angles of the symmetric split ring transmitarray metasurface on the focusing performances such as working efficiency and NA of the designed metalens is also studied and analyzed finally, which can add new degree of freedoms to optimize its focusing performance. The presented studies can facilitate the development of high-efficiency metalenses in the low frequency and have significant potential applications in high-resolution microwave imaging, high-gain metalens antennas and others