Practical Application of the Probabilistic‐Statistical Model of the Suspension Separation in Hydrocyclones

An effective practical approach that allows not only a significant reduction in the scope of practical experiments in the course of studying suspension separation processes in hydrocyclones, but also makes it possible to assess the intensity of random components of the processes and define the interrelation between such components and hydrodynamics of flows in a hydrocyclone is presented. Within the frames of the developed probabilistic‐statistical model of suspension separation in hydrocyclones on the basis of statistical self‐similarity properties, a relationship was found between determined and random components of the processes. This allowed transitioning from three‐parameter probability density functions for suspension particles in hydrocyclones to two‐parameter functions; thus significantly improving the efficiency of practical application of the developed model.     

冷链条件下气调包装酱卤鸭肉制品货架期预测模型的建立

目的 以气调包装酱卤鸭肉制品为研究对象,在冷链温度范围内建立一套准确、高效的货架期预测模型。方法 利用选择性培养基测定不同温度下产品各微生物数量,确定4~25℃条件下产品优势腐败菌。对乳酸菌数量与感官评定值进行了回归分析确定最小腐败量Ns。分别采用修正的Gompertz方程和平方根方程建立一、二级模型,并通过预测值与实测值对比验证模型的可靠性。结果 确定了4~25℃条件下产品优势腐败菌为乳酸菌,最小腐败量Ns=6.14(lg(cfu /g))。一、二级模型拟合度均良好,三种温度下模型预测值与实际值间的差异均在30％左右,波动幅度在10%以内。结论 实现了对4~25℃内任何时间点产品剩余货架期的预测,为冷链条件下气调包装酱卤鸭肉制品品质的变化提供了理论指导。     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

P-doped CoSe2 nanoparticles embedded in 3D honeycomb-like carbon network for long cycle-life Na-ion batteries

We report for the first time a Na-ion battery anode material composed of P-doped CoSe2 nanoparticles(P-CoSe2)with the size of 5-20 nm that are uniformly embed in a 3D porous honeycomb-like carbon network.High rate capability and cycling stability are achieved simultaneously.The honeycomb-like carbon network is rationally designed to support high electrical conductivity,rapid Na-ion diffusion as well as the accommodation of the volume expansion from the active P-CoSe2 nanoparticles.In particular,heteroatom P-doping within CoSe2 introduces stronger P-Co bonds and additional P-Se bonds that signif-icantly improve the structure stability of P-CoSe2 for highly stable sodiation/desodiation over long-term cycling.P-doping also improves the electrical conductivity of the CoSe2 nanoparticles,leading to highly elevated electrochemical kinetics to deliver high specific capacities at high current densities.Benefiting from the unique nanostructure and atomic-level P-doping,the P-CoSe2(2∶1)/C anode delivers an excel-lent cycle stability with a specific capacity of 206.9 mA h g-1 achieved at 2000 mA g-1 after 1000 cycles.In addition,this material can be synthesized using a facile pyrolysis and selenization/phosphorization approach.This study provides new opportunities of heteroatom doping as an effective method to improve the cycling stability of Na-ion anode materials.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.     

Fabrication and characterizations of Cr3+-doped ZnGa2O4 transparent ceramics with persistent luminescence

0.5 at.% Cr:ZnGa₂O₄ precursor was synthesized by the co-precipitation method with nitrates as raw materials, using ammonium carbonate as the precipitant. Low-agglomerated Cr:ZnGa₂O₄ powders with an average particle size of 43 nm were obtained by calcining the precursor at 900℃ for 4 h. Using the powders as starting materials, 0.5 at.% Cr:ZnGa₂O₄ ceramics with an average grain size of about 515 nm were prepared by presintering at 1150℃ for 5 h in air and HIP post-treatment at 1100℃ for 3 h under 200 MPa Ar. The in-line transmittance of 0.5 at.% Cr:ZnGa₂O₄ ceramics with a thickness of 1.3 mm reaches 59.5% at the wavelength of 700 nm. The Cr:ZnGa₂O₄ ceramics can be effectively excited by visible light and produce persistent luminescence at 700 nm. For Cr:ZnGa₂O₄ transparent ceramics, the brightness of afterglow was larger than 0.32 mcd/m² after 30 min, which is far superior to that of Cr:ZnGa₂O₄ persistent luminescence powders.     

径向不耦合装药爆破效果的数值研究

为了研究径向不耦合条件下爆破效果的影响因素,利用AUTODYN-2D对比不耦合系数、不耦合介质、炸药种类对爆破效果的影响规律。结果表明,与耦合装药相比,径向不耦合装药条件下炮孔压力明显降低。对比水介质和空气介质下的爆破效果可知,水介质不耦合条件下,炸药爆炸能量的传递效率更高。炮孔压力与炸药种类密切相关,TNT产生的炮孔压力最大,但作用时间较短,而ANFO和乳化炸药的作用时间较长。