Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

A side-sensitive synthetic coefficient of variation chart

Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic-γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic-γ charts do not differentiate between the points falling outside the upper control limit (UCL) and lower control limit (LCL). Hence, this paper proposes a side-sensitive synthetic-γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL. Formulae to compute the average run length (ARL), the standard deviation of the run length (SDRL) and expected average run length (EARL) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side-sensitive synthetic-γ chart consistently outperforms the existing synthetic-γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart-γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.     

Potential effects of ultrafiltration process and date powder on textural,sensory, bacterial viability,antioxidant properties and phenolic profile of dromedary Greek yogurt

The aim of this study was to improve textural and antioxidant capacity of dromedary yogurt using ultrafiltration process and date powder. Ultrafiltration increased total solids content of dromedary milk within the range considered optimal to develop yogurt. Texture profile of Greek yogurt fortified with date powder (GYD) improved considerably compared to control. Sample of GYD are more appealing to consumer than control. LC-ESI-MS analysis of GYD extracts allowed the identification of fifteen phenolic compounds, among which quinic acid and cirsiliol were found to be the major phenolic acid and flavonol, respectively. GYD exhibited the highest DPPH•-radical scavenging activity, Fe²⁺ chelating capacity and Fe³⁺ reducing power. The follow-up of physical and microbiological stability of GYD and control during cold storage showed that date powder significantly increased bacterial counts and decreased syneresis. Therefore, ultrafiltration and date powder could be valued as effective tool to solve the poor consistency of dromedary milk products.     

TSL:predicting popularity of Facebook content based on tie strength

The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.     

Multi-Functional Black Bioactive Glasses Prepared via Containerless Melting Process for Tumor Therapy and Tissue Regeneration

Tissue regeneration in complex lesions such as the site of tumors, bacterial infection, and sites lacking blood vessels, has been a huge challenge. Therefore, developing bioactive implantable materials with multi-functional properties such as tumor destruction, bacteria growth inhibition, and angiogenesis promotion is of great significance. In this study, black CaO-SiO₂-TiO₂ (CST) glasses are prepared through the containerless melting approach, by which heterogeneous nucleation can be avoided and thereby glass formation becomes possible via fast quenching. This approach enables the formation of trivalent titanium (Ti³⁺) without using a reducing atmosphere or reducing agents. The black CST glasses are found in this study to possess a strong ability to inhibit bacteria and tumors by their excellent photothermal and photocatalytic effects. Strikingly, these glasses also promote the formation of blood vessels and accelerate the healing of chronic wounds by the synergistic effects of the photothermal effect and Si ions. Thus, this glass system can be a promising multi-functional material for tissue regeneration in complex lesions.     

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.     

Symmetric and asymmetric membranes based on La0.5Sr0.5Fe0.7Ga0.3O3-δ perovskite with high oxygen semipermeation flux performances and identification of the rate-determining step of oxygen transport

This work focuses on identifying the rate-determining step of oxygen transport through La_0.5Sr_0.5Fe_0.7Ga_0.3O_3-δ membranes with symmetric and asymmetric architectures. The best oxygen semipermeation fluxes are 3.4 10⁻³ mol. m^-2.s^-1 and 6.3 10⁻³ mol. m^-2.s^-1 at 900 °C for the symmetric membrane and asymmetric membrane with a modified surface. The asymmetric membrane with a modified surface leads to an increase of approximately 7 times the oxygen flux compared to that obtained with the La_0.5Sr_0.5Fe_0.7Ga_0.3O_3-δ dense membrane without surface modification. This work also shows that the oxygen flux is mainly governed by gaseous oxygen diffusion through the porous support of asymmetric La_0.5Sr_0.5Fe_0.7Ga_0.3O_3-δ membranes.     

Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

Deconvoluting Energy Transport Mechanisms in Metal Halide Perovskites Using CsPbBr3 Nanowires as a Model System

Understanding energy transport in metal halide perovskites is essential to effectively guide further optimization of materials and device designs. However, difficulties to disentangle charge carrier diffusion, photon recycling, and photon transport have led to contradicting reports and uncertainty regarding which mechanism dominates. In this study, monocrystalline CsPbBr₃ nanowires serve as 1D model systems to help unravel the respective contribution of energy transport processes in metal-halide perovskites. Spatially, temporally, and spectrally resolved photoluminescence (PL) microscopy reveals characteristic signatures of each transport mechanism from which a robust model describing the PL signal accounting for carrier diffusion, photon propagation, and photon recycling is developed. For the investigated CsPbBr₃ nanowires, an ambipolar carrier mobility of μ = 35 cm² V⁻¹ s⁻¹ is determined, and is found that charge carrier diffusion dominates the energy transport process over photon recycling. Moreover, the general applicability of the developed model is demonstrated on different perovskite compounds by applying it to data provided in previous related reports, from which clarity is gained as to why conflicting reports exist. These findings, therefore, serve as a useful tool to assist future studies aimed at characterizing energy transport mechanisms in semiconductor nanowires using PL.