The spread of infectious diseases are inevitably affected by natural and social factors, and their evolution presents oscillations and other uncertainties. Therefore, it is of practical significance ...to consider stochastic noise interference in the studies of infectious disease models. In this paper, a stochastic SIR model with nonlinear incidence and recovery rate is studied. First, a unique global positive solution for any initial value of the system is proved. Second, we provide the sufficient conditions for disease extinction or persistence, and the influence of threshold $ \tilde{R_{0}} $ of the stochastic SIR model on disease state transition is analyzed. Additionally, we prove that the system has a stationary distribution under some given parameter conditions by building an appropriate stochastic Lyapunov function as well as using the equivalent condition of the Hasminskii theorem. Finally, the correctness of these theoretical results are validated by numerical simulations.
Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses ...the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.
Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire functional metabolic information and anatomical information of the human body. How to rationally fuse the ...complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality information for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the probability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the probability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: (1) Only a few training samples were needed for training the designed network to produce the probability map; (2) The proposed method can be applied to small datasets, normally seen in clinic research; (3) The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multimodality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); (4) The proposed method had a good performance for tumor segmentation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ± 0.05, sensitivity (SE) of 0.86 ± 0.07, positive predictive value (PPV) of 0.87 ± 0.10, volume error (VE) of 0.16 ± 0.12, and classification error (CE) of 0.30 ± 0.12.
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•A new improved optimization method is proposed.•The standard deviation of the optimal solutions decision variables increased by 70%.•The implementation of the optimal solutions has ...been improved.•The requirements of overall planners and implementers are comprehensively considered.
The multiobjective optimization of the system usually focuses on the optimization of the objective functions while ignoring the influence of decision variables on the implementation of the solution. This paper proposes a new improved optimization method by embedding the decision variable diversification mechanism in the optimization process, adopting the discretization mechanism in the multisource complementary heating model, and improving the search space. The new improved optimization method and the original method have similar performance in obtaining the Pareto front, and the hypervolume of the two algorithms differs by only 1.37% on average. The standard deviations of the decision variables in the optimal solutions obtained by the improved algorithm are increased by 70%, and it has a higher diversity of solutions in the decision space. The equipment capacity obtained by the improved algorithm is discretized, and avoids equipment with lower capacity which is beneficial to construction. In this paper, the optimal implementation solution is obtained through the selection of the objective functions by the overall planners and the construction preference of the solution implementers. In this way, the overall planners' requirements for energy conservation, emission reduction and economy, as well as the solution implementers' choice of implementation solutions can be comprehensively considered. In addition, this paper also obtains another optimal solution for adopting the carbon pricing method.
•Pure, binary, and ternary refrigerants of 14 refrigerants are analyzed.•Different refrigeration systems are considered.•Pareto fronts of the COP and volumetric heating capacity are obtained.•Binary ...refrigerants have similar advantages to ternary refrigerants.
Mixed refrigerants are widely used in heat pumps; thus, it is necessary to analyze the performance advantages of pure, binary and ternary refrigerants. In this paper, 14 kinds of pure refrigerants were selected, and 24,024 kinds of pure, binary and ternary refrigerants were obtained and analyzed. The refrigerant performance in terms of the COP and volumetric heating capacity were compared in normal, vapor injection with internal heat exchanger and with flash tank systems. In this paper, 40 °C was used as the temperature lift, and four cases with heating production temperatures of 70, 80, 90 and 100°C were analyzed. The Pareto fronts for the different refrigerant types and compositions were obtained by using exhaustive method. The COP and volumetric heating capacity exhibited contradictory trends in all cases. The results showed that mixed refrigerants had significant advantages over pure refrigerants in achieving the Pareto fronts. However, in all cases, when the COP was higher than 5, the binary refrigerants had similar advantages to ternary refrigerants; thus, ternary refrigerants were not necessary. When the COP was lower than 5, it was difficult to further increase the volumetric heating capacity by sacrificing the COP.
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Graphene exhibits extraordinary electronic and mechanical properties, and extremely high thermal conductivity. Being a very stable atomically thick membrane that can be suspended between two leads, ...graphene provides a perfect test platform for studying thermal conductivity in two-dimensional systems, which is of primary importance for phonon transport in low-dimensional materials. Here we report experimental measurements and non-equilibrium molecular dynamics simulations of thermal conduction in suspended single-layer graphene as a function of both temperature and sample length. Interestingly and in contrast to bulk materials, at 300 K, thermal conductivity keeps increasing and remains logarithmically divergent with sample length even for sample lengths much larger than the average phonon mean free path. This result is a consequence of the two-dimensional nature of phonons in graphene, and provides fundamental understanding of thermal transport in two-dimensional materials.
A growing body of work has demonstrated that the δ2H values of alkenones reflect the δ2H values (δ2HH2O) and / or salinity of the fluid in which they are produced. If so, δ2Halkenone values would act ...as a surface seawater isotope / salinity proxy, similar to foraminiferal δ18O values, but advantaged in locations with poor carbonate preservation and / or high organic content. Nevertheless, laboratory culture, sediment trap, and water column studies have failed to consistently characterize the effects of temperature, alkenone-producing species, and salinity itself on the δ2Halkenone-salinity and -seawater isotope relationships, and a robust sedimentary alkenone-based calibration remains elusive.
Most δ2Halkenone datasets report δ2HC37, i.e., combined δ2HC37:3 and δ2HC37:2 values, and differ in how they address inter-alkenone fractionation (i.e., αC37:3-C37:2). To constrain controls on alkenone hydrogen isotope systematics in the natural environment, we measured δ2H values of C37 and C38 alkenones from 20 open ocean core tops by gas chromatography-stable isotope ratio mass spectrometry after separation of di- and tri-unsaturated forms. Core-top δ2Halkenone data points are currently concentrated in extreme-salinity regions; in combination with our new values from a more moderate range of open ocean δ2HH2O / salinity values, for sedimentary alkenones, we show that 1) mean inter-alkenone hydrogen isotope fractionation is negligible (αC37:3-C37:2 = 1.002 ± 0.006), and therefore that δ2HC37:3 and δ2HC37:2 values can be measured in bulk; 2) temperature and salinity have little impact on alkenone-water fractionation (i.e., αC37-H2O) (mean 0.803 ± 0.010) relative to their expected variability in the ocean; and 3) δ2HC37 and δ2HH2O values are correlated such that statistically identical δ2HC37-, δ2HC37:3-, and δ2HC37:2-δ2HH2O regressions yield a core-top-based calibration of δ2HC37 = 1.44 (± 0.13) * δ2HH2O – 191.62 (± 1.13) ‰. This is indistinguishable from water column calibrations, suggesting a consistent response of environmental δ2HC37 values to changes in δ2HH2O values.
This calibration still contains a high amount of scatter (∼ 7 ‰), perhaps attributable to irradiance, growth rate, intra- or interspecies variability, or other factors difficult to constrain in sedimentary material. Nevertheless, when applied to the well-constrained Last Glacial Maximum-to-present mean ocean δ2HH2O change of ∼ 8.8 ‰, it (1.44 ‰ δ2HC37 per 1 ‰ δ2HH2O change) reproduces the mean δ2HC37 Modern – δ2HC37 LGM shift observed from the handful of extant down-core records, legitimizing the observed lack of temperature or salinity effects on αC37-H2O. This suggests that combined δ2HC37:3 + δ2HC37:2 values are a valid proxy for δ2HH2O values in open ocean settings where E. huxleyi and G. oceanica dominate, although additional efforts will be required to refine the core-top calibration for universal use.
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