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In this work, experimental and numerical simulation methods are used to study the gas-solid two-phase flow in a three-dimensional rectangular spouted bed. In particular, the TFM and the CFD-DEM simulation results are compared with experimental data of the spouted bed. The influence of different drag models and friction stress models on the applicability of the simulation technology, Gidaspow, BVK, Koch-Hill, and Syamal-O'Brein drag models are investigated, respectively. Besides, the influence of the Syamal (S-R-O) and Srivastava-Sundaresan (S-S) friction stress models considering different transition points on the flow characteristics of particles in a spouted bed is also studied. Experimental verification shows that the Gidaspow drag, and S-S friction stress models are more consistent with experimental results. The fountain height predicted by CFD-DEM is closer to the experiment. It is found that the heterogeneous flow structure resulted in such a phenomenon in that the bubble cap blocked the gas flow pathway and increased the drag coefficient, while the bypass of the gas phase near the walls in the bubble reduced the drag coefficient.

The aging of the population has become a global health problem. It is an important risk factor for major diseases such as cardiovascular disease, Alzheimer's disease, Parkinson's disease, and cancer. Presently, there is no definite and effective anti-aging treatment. Chinese herbal medicine has a long history of application and is often used for aging-related diseases in China. A large number of clinical and preclinical studies on the anti-aging effect of Chinese herbal medicine has been performed. Through literature research, we reviewed the anti-aging clinical research of Chinese herbal medicine and preclinical research of Chinese herbal medicine monomers, components, extracts, and compounds, and their mechanisms. Results from preclinical studies have shown that Chinese herbal medicines have beneficial anti-aging effects. The mechanism mainly includes anti-oxidative stress, anti-inflammatory, neuroprotection, apoptosis and mitochondrial function regulation, etc. However, more detailed high-quality clinical trials are required for future investigation of the anti-aging effects of Chinese herbal medicines.

Poria cocos is an edible and medicinal fungus that is widely used in Traditional Chinese Medicines as well as in modern applications. Retinoid X receptor (RXR) occupies a central place in nuclear receptor signaling, and a pharmacological RXR-dependent pathway is involved in myeloid cell function. Here, structural information for 82 triterpenes from P. cocos and 17 known RXR agonists was collected in a compound library and retrieved for a molecular docking study. Three triterpenes, 16α-hydroxytrametenolic acid (HTA), pachymic acid (PA), and polyporenic acid C (PPAC), were identified as novel RXR-specific agonists based on luciferase reporter assays and in silico evidence. Treatment with HTA, PA, and PPAC significantly induced differentiation of the human promyelocytic leukemia cell line HL-60 with EC50 values of 21.0 ± 0.52, 6.7 ± 0.37, and 9.4 ± 0.65 μM, respectively. These effects were partly blocked by the RXR antagonist UVI3003, suggesting that an RXR-dependent pathway may play an important role in their anti-acute promyelocytic leukemia (APL) effects. Taken together, triterpenes from P. cocos are revealed as naturally occurring RXR selective agonists with the potential for anti-cancer activity. These results suggest a novel approach to the treatment or prevention of APL.

Electric fields can make significant impact on meso-scale structures, such as bubbles and clusters, in gas-solid fluidized beds. In this work, a 2-D gas-solid fluidized bed coupled with a DC electric field is established, and numerical simulations are carried out based on CFD-DEM method. Electric-field induced forces are modeled based on a revised point-dipole approximation by considering local-field effects and multipolar interactions. The reason for bubble size reduction and clusters formation is investigated in detail from the perspective of particle motions and forces, respectively. Simulated results show that formation of chains suppress the vortex motion of particles and result in the elimination of bubbles. Although particle-particle interactions are repulsive in the direction normal to the electric field, particles are observed to attach to each other along an oblique angle to form a braided multi-particle chain. Moreover, distributions of particle concentration and velocity become more homogeneous as the field strength increased.

Traditional Chinese medicine (TCM) is a treasure of traditional Chinese culture and a gift to the world. TCM tacit knowledge refers to the knowledge and experiences formed in the process of learning and practice of TCM. The objective of this study is to discuss the importance of TCM tacit knowledge in the inheritance and education of TCM. As the essence of the TCM, TCM tacit knowledge has the characteristics of massive, complicated, relativistic, highly individualized, constantly innovative, the dependence of cultural background and the regional environment, as well as difficult to explicate. It exists in every aspect of the TCM theory and the process of dialectical treatment. Besides the traditional master-apprentice, family-based, school-based, and inheritance and education methods, together with the inheritance based on the books, images, and network platforms, in the process of TCM modernization, a variety of modern theoretical models and computing techniques have also been used in the mining of the TCM tacit knowledge. In this study, we introduced the usage of SECI model, complexity adaptive system, latent variable model, and some of the data mining technologies in the TCM tacit knowledge mining. An accurate and efficient inheritance of TCM tacit knowledge is the key to maintain the vitality and innovative development of TCM. Under the reasonable application and combination of the traditional education methods, modern mining methods, and further the artificial intelligence, the explicit and inheritance of TCM tacit knowledge will get tremendous development, and it could extremely improve the efficiency and accuracy of the TCM inheritance and the TCM modernization.

Machine intelligence has been greatly developed in the past decades and has been widely used in many fields. In the recent years, many reports have shown its satisfactory effect in drug discovery. In this study, machine intelligence methods were explored to assist the cell activity prediction. Multiple machine intelligence methods including support vector machine, decision tree, random forest, extra trees, gradient boosting machine, convolutional neural network, long short-term memory network, and gated recurrent unit network were employed to separate compounds based on their cell activity. Different from some reported classification models, compounds were expressed as a string by the simplified molecular input line entry system and directly used as input rather than any chemical descriptors, which mimicked natural language processing. Both the single cell strain and whole data set under the balanced and imbalanced data distributions were discussed, respectively. Different activity cutoffs were set for the single (Z-score = 3) and the whole (Z-score = 5 and 6) data set. Nine metrics were used to evaluate the models including accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve score, Cohen's κ, Brier score, Matthews correlation coefficient, and balanced accuracy. The results show that the gradient boosting machine is competent at balanced data distribution, and convolutional neural network is qualified for the imbalanced one. The results demonstrate that both classic machine learning methods and deep learning methods have potential in classification of compound cell activity.

Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

Background: Hypertension in the elderly with cognitive impairment has been one of the global health issues. Mild cognitive impairment (MCI) is the state of transition between the normal aging process and cognitive changes of unformed dementia. Diagnosis and treatment of MCI are the keys to prevent dementia, and hypertension is one of the important influencing factors of MCI. Our preclinical experiment found that Yizhi Qingxin Decoction (YQD) could effectively reduce the blood pressure of spontaneously hypertensive rats (SHR), improve their spatial learning and memory abilities in Morris water maze, and play a neuroprotective role. The objective is to estimate the safety and efficacy of YQD (capsules) in the treatment of hypertension in the elderly with MCI (deficiency of kidney essence syndrome) through this study. Methods: According to the random number generated by the block random method, 100 participants will be randomly and equally divided into the treatment group (YQD) or the control group (Ginkgo biloba extract tablets). The conversion rate of dementia will be used as the main evaluating indicator by the CDR scale. The MoCA scale, MMSE scale, ADCS-MCI-ADL-24 scale, CGIC-KDS scale, and 24-h ambulatory blood pressure will be used as the secondary evaluating indicator. Safety will be evaluated based on specific manifestations of adverse reactions and the incidence of adverse events. Objective: The objective is to estimate the curative effect of YQD (capsules) on hypertension in the elderly with MCI (deficiency of kidney essence syndrome), and to evaluate the safety of its clinical application. Trial registration: Chinese Clinical Trial Registry (ICTRP member): ChiCTR2000030292.

Aim: To analyze the effects of acupoint injection in the treatment of non-dialysis dependent chronic kidney disease through a systematic review with meta-analysis. Methods: This systematic review with meta-analysis was conducted following the recommendations of the declaration of PRISMA. Full-text literature of randomized controlled trial of acupoint injection therapy for non-dialysis chronic kidney disease was searched in PubMed, Embase, Cochrane Library, China National Knowledge Internet, the Chinese Scientific Journal Database, the Wanfang Database, China Biology Medicine database. The efficacy and safety of acupoint injection for non-dialysis chronic kidney disease were evaluated. Results: Seventeen studies containing 1414 patients met the criteria. The results shows that acupoint injection combined with basic treatment can significantly improve the levels of Ccr (WMD = 4.81; 95% CI:2.54 to 7.08) and Hb (WMD = 4.56; 95% CI:1.72 to 7.39), reduce the levels of BUN (WMD = −0.90; 95% CI: −1.26 to −0.54)and Scr (WMD = −7.66; 95% CI: −12.39 to −2.93), and improve the effective rate (OR = 3.12; 95% CI: 2.29 to 4.26). Conclusion: Our current analysis showed that combined acupoint injection therapy can reduce the levels of BUN and Scr, and increase Ccr and Hb in non-dialysis CKD patients. However, the existing evidence is still insufficient due to the high risk of included trial bias, and future research needs to improve methodological quality. Registration number: CRD42020168143.

Background Angelica root is the dry root of the Umbelliferae plant Angelica sinensis (oliv) Diels. Angelica organic acid (OA) is the main active ingredient in Angelica sinensis, and it exerts potential anti-atherosclerotic effects by preventing Oxidized low-density lipoprotein (Ox-LDL) induced endothelial injury. To study the protective effects of OA on ox-LDL-induced HUVECs autophagic flux dysfunction and inflammatory injury. Methods OA were isolated by water extraction and alcohol precipitation, and then the content of ferulic acid (FA) in the OA was determined by high performance liquid chromatography. The ox-LDL-induced endothelial injury model was established. The effect of ferulic acid on the survival of Human umbilical vein endothelial cells (HVUECs) was detected by CCK-8 assay. HUVECs were pretreated with different concentrations of OA (20 μmol/L, 40 μmol/L, and 80 μmol/L), and Western Blot was used to detect the expressions of LC3II, p62, MCP-1, VCAM-1 and LOX-1. The autophagosomes in HUVECs were observed by transmission electron microscopy (TEM). Results 20 μmol/L OA could increase the expression of LC3II and decrease the expression of p62, MCP-1, VCAM-1 and LOX-1. The results of TEM showed that angelica organic acids promoted cell organelle degradation in autolysosomes. Conclusion OA could reduce inflammation, protect endothelial cells and play an anti-atherosclerotic role by enhancing the autophagy flux of damaged endothelial cells, in which FA the major active ingredient of OA played a major role.

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.

The potential for polymer thin films to be used as coatings or dielectrics is limited by their low thermal conductivity, κ. However, there have been very limited studies on κ enhancement for polymer thin films. In this work, we show that the out-of-plane κ of a poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) thin film can be enhanced when it undergoes the appropriate thermal annealing conditions. When the film is annealed above its melting temperature, we achieve a 300–400% increase in κ across the measured temperature range, compared to the as-cast film. We attribute this enhancement to the extensive ordering of polymer backbone chains perpendicular to the substrate during the melt-recrystallization process.

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

Human pharmacokinetics is of great significance in the selection of drug candidates, and in silico estimation of pharmacokinetic parameters in the early stage of drug development has become the trend of drug research owing to its time- and cost-saving advantages. Herein, quantitative structure-property relationship studies were carried out to predict four human pharmacokinetic parameters including volume of distribution at steady state (VDss), clearance (CL), terminal half-life (t1/2), and fraction unbound in plasma (fu), using a data set consisting of 1352 drugs. A series of regression models were built using the most suitable features selected by Boruta algorithm and four machine learning methods including support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and XGBoost (XGB). For VDss, SVM showed the best performance with R2test = 0.870 and RMSEtest = 0.208. For the other three pharmacokinetic parameters, the RF models produced the superior prediction accuracy (for CL, R2test = 0.875 and RMSEtest = 0.103; for t1/2, R2test = 0.832 and RMSEtest = 0.154; for fu, R2test = 0.818 and RMSEtest = 0.291). Assessed by 10-fold cross validation, leave-one-out cross validation, Y-randomization test and applicability domain evaluation, these models demonstrated excellent stability and predictive ability. Compared with other published models for human pharmacokinetic parameters estimation, it was further confirmed that our models obtained better predictive ability and could be used in the selection of preclinical candidates.

Oxymatrine is a quinazine alkaloid extracted from Sophora flavescens with various therapeutic effects such as organ- and tissue-protective, anti-inflammatory, anti-cancer, and anti-viral effects. In this review, we summarize the protective effects of oxymatrine on damaged organs and tissues by analyzing both in vivo and in vitro studies. The mechanisms of protective effects of oxymatrine are mainly related to its anti-inflammatory, anti-oxidative stress, anti- or pro-apoptotic, anti-fibrotic, metabolism-regulation, and anti-nociceptive functions. In addition, a variety of signal pathways, cells, and molecules are influenced by oxymatrine, and by these comprehensive actions, maximum therapeutic effects can be achieved. Furthermore, we summarize the protective effects in clinical studies and adverse effects of oxymatrine. It is believed that through more in-depth animal experiments and standardized clinical research, oxymatrine holds a bright future in the process of organ and tissue protection and has a significant therapeutic promise to translate from bench to bedside.

Supplemental Digital Content is available in the text, Background: Chronic stable angina (CSA) is a cardiovascular disease with high prevalence. At present, drug treatment is still the main measure of stable angina pectoris. Traditional Chinese medicine has a long history in the treatment of CSA. Qi stagnation and Blood stasis syndrome is a common syndrome of CSA. Xinnaoning (XNN) capsule is considered as an effective adjuvant treatment for CSA with the efficacy of promoting qi and blood circulation but lack of high-quality clinical evidence. The purpose of this study is to evaluate the efficacy and safety of XNN capsule compared with placebo by clinical trial. Methods: This multicenter, randomized, double-blind, placebo-controlled trial will be conducted with a total of 240 participants diagnosed with chronic stable angina (qi stagnation and blood stasis syndrome). The participants will be randomized (1:1) into groups receiving either XNN or placebo for 12 weeks. After a 2-week run-in period, they will receive either XNN or placebo (3 pills, 3 times daily) for 12 weeks on the basis of conventional therapy. The primary outcomes include changes in the integral scores of angina symptoms. The secondary outcome measures include changes in the total score of traditional Chinese medicine syndrome, severity grading of angina pectoris, the number of angina pectoris per week, nitroglycerin dosage, score of seattle angina scale, serum homocysteine, incidence of cardiovascular events. Safety outcomes will also be assessed. Adverse events will be monitored throughout the trial. Results: This study will investigate whether XNN capsule can alleviate clinical symptoms, and improve quality of life of patients with chronic stable angina (qi stagnation and blood stasis syndrome). The results of this study will provide clinical evidence for the application of XNN capsule in the treatment of chronic stable angina. Trial registration: ClinicalTrials.gov: NCT 03914131.

Human ether-a-go-go-related gene (hERG) K+ channel blockage may cause severe cardiac side-effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates.

A mimicry honeypot framework based on game theory is presented in our work, which can calculate the equilibrium strategy of the deceptive game using non-cooperative incomplete dynamic game theory, and make decisions for the mimicry framework to deploy the simple service, the honeypot and the fake honeypot. A mimicry prototype is implemented using NS2 platform, and simulation experiments are launched to validate the decision-making result and the deceptive performance of the mimicry honeypot. The empirical study shows that the mimicry honeypot framework based on game theory can be able to influence the equilibrium strategy results by dynamically changing the deployment vector of the mimicry system. It validates that the mimicry honeypot framework has better flexibility, activeness and fraudulence than the traditional honeypot.

We investigated the phase behavior of thin film, thickness h ≈ 100 nm, mixtures of a polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) diblock copolymer with star-shaped polystyrene (SPS) molecules of varying functionalities f, where 4 ≤ f ≤ 64, and molecular weights per arm Marm. The miscibility of the system and the surface composition varied appreciably with Marm and f. For large values of Marm, regardless of f, the miscibility of the system was qualitatively similar to that of linear chain PS/PS-b-P2VP mixtures – the copolymer chains aggregate to form micelles, each composed of an inner P2VP core and PS corona, which preferentially segregate to the free surface. On the other hand, for large f and small Marm, SPS molecules preferentially resided at the free surface. Moreover, blends containing SPS molecules with the highest values of f and lowest values of Marm were phase separated. These observations are rationalized in terms of competing entropic interactions and the dependence of the surface tension of the star-shaped molecules on Marm and f.

A comprehensive solid stress model including the whole flow regimes for gas-particle flows is proposed. In the inertial regime characterized by low solid concentrations, solid stress is closed by kinetic theory based on particle collision mechanisms. In the quasi-static regime characterized by high particle concentrations, solid stress is closed by the frictional stress model based on soil mechanics. In the intermediate regime, solid stress is closed by an inertial number model without the need to specify a threshold of particle concentration for the transition. This new model is validated with experimental data from bubbling fluidized beds with a central jet, and compared with simulation results predicted by kinetic theories. Results show that this new model can give a more accurate prediction of bubble size, bubble shape and detachment time than through kinetic theory or other previous models. The problem of specifying a threshold value when employing previous models is solved.

Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross-validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure-based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.

The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood–brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure–property relationship study was carried out to predict blood–brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ( $$ R^{2} $$ = 0.938), test set ( $$ R^{2} $$ = 0.840) and tenfold cross-validation ( $$ Q^{2} $$ = 0.788). Finally, we found that the polar surface area and octanol–water partition coefficient have the greatest influence on blood–brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.

The optical constants of five highly transparent substrates (polycrystalline BaF2, CaF2, MgF2, ZnSe, and ZnS) were experimentally determined based on a combined technique using both the double optical pathlength transmission method and the ellipsometry method within temperature range 20°C-350°C in the ultraviolet-infrared region (0.2-20 μm). The results show that the refractive index spectra of polycrystalline BaF2, CaF2, and MgF2 are similar, but differ from that of polycrystalline ZnSe and ZnS. The thermo-optic coefficient of these highly transparent substrates increases with increasing temperature. The absorption indices show a significant temperature-dependent behavior, which increases with increasing temperature from 20°C to 350°C over the transparent region. For the sake of application, the fitted formulas of the refractive index of the five highly transparent substrates as a function of wavelength and temperature are presented.

The temporal evolution of the absorption cross-section and scattering cross-section of Anabaena sp. grown in flat-plate photobioreactor (PBR) was experimentally measured in the spectral region 380 to 850 nm. The time-dependent light field distribution in PBRs was presented for different growth phases. The relative deviation of light fluence rate and growth rate are calculated based on the growth dependent radiative properties and the radiative properties at stationary phase of microalgae. The results show that the absorption and scattering cross-sections vary significantly with growth time. The light fluence rate and growth rate within the reactor volume are remarkably affected by the growth dependent radiative properties of microalgae at the exponential growth phase. The growth dependent radiative properties of microalgae is crucial to accurately predict the microalgae biomass productivity within PBRs and to optimize the cultivation condition.

Ensuring the accuracy of signal detection under repeated deformation is a challenge for flexible sensor reported in recent years, and achieving reliable adhension between the sensor and substrate is a key factor. Here, this article reported a highly adhesive flexible strain sensor based on ultra-violet adhesive filled by modified graphene and carbon black for wearable monitoring. This strain sensor with a typical resistive behavior shows gauge factor calculated at 0∼10% strain is 2.1. It exhibits short curing time (∼1 h), fast response (∼40 ms), excellent adhesive strength (4500 kPa), and adhesiveness to various bending surfaces. Furthermore, this strain sensor can be directly prepared on elastic columns and rubber gloves, and exhibits excellent performance in three-dimensional force detection and writing gesture recognition. The ability of adhesive conformability to arbitrary and complex surfaces shows that the strain sensor has broad application prospects in wearable devices.

The rays propagate along curved path determined by the Fermat principle in the graded index medium. The radiative transfer equation in graded index medium (GRTE) contains two specific redistribution terms (with partial derivatives to the angular coordinates) accounting for the effect of the curved ray path. In this paper, the hybrid finite volume with finite element method (hybrid FVM/FEM) (P.J. Coelho, J. Quant. Spectrosc. Radiat. Transf., vol. 93, pp. 89–101, 2005) is extended to solve the radiative heat transfer in two-dimensional absorbing-emitting-scattering graded index media, in which the spatial discretization is carried out using a FVM, while the angular discretization is by a FEM. The FEM angular discretization is demonstrated to be preferable in dealing with the redistribution terms in the GRTE. Two stiff matrix assembly schemes of the angular FEM discretization, namely, the traditional assembly approach and a new spherical assembly approach (assembly on the unit sphere of the solid angular space), are discussed. The spherical assembly scheme is demonstrated to give better results than the traditional assembly approach. The predicted heat flux distributions and temperature distributions in radiative equilibrium are determined by the proposed method and compared with the results available in other references. The proposed hybrid FVM/FEM method can predict the radiative heat transfer in absorbing-emitting-scattering graded index medium with good accuracy.