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The year 2023 marks the 10th anniversary of the Chinese government’s announcement of the strategy to build ‘new-type think tanks with Chinese characteristics’. As thousands of new think tanks launched across the country, has the Chinese government attained its strategic goals including advancing the scientific and democratic decision-making and the modernization of the national governance system and capacity? What key questions concerning think tanks remain unsolved? And what prospects on new think tanks could be expected? To answer these questions, this essay reviews five books written by representative Chinese think tank scholars and practitioners. It argues that while China has made progress in terms of quality, quantity, and institution building of new think tanks, as the books under review demonstrate, some major challenges remain. Among them, the most severe is that China has yet to develop a competitive idea market and cultivate an open think tank culture. This essay concludes by arguing that there is a long road ahead for China to shift from a big think tank country to a powerful think tank country.

Objective: This study aimed to observe the diagnostic value of cranial magnetic resonance imaging (MRI) in patients with vascular dementia induced by ischemic stroke. Methods: The experiment was designed according to the randomized control principle. Two hundred and eighty patients with ischemic stroke who were admitted to Gucheng County Hospital between June 2019 and June 2021 were selected as research subjects. Patients without vascular dementia after stroke were included in the control group, and patients with vascular dementia after stroke were included in the observation group. The cranial MRI was performed in both groups. Results: Proportions of patients with large and moderate infarct lesions in brain tissues were significantly higher in the observation group than the control group. The data variation of relevant MRI detection indicators of the observation group was more obvious than that of the control group (P0.05). Patients with changes in brain morphology were more in the observation group than the control group (P

Through a quantitative analysis of Taiwan–related legislation between 1979 and 2020, the article finds that the degree of Taiwan–related legislation is significantly correlated with the degree of t...

The present study quantifies probabilistic characteristics of the wall thickness of welded pipe joints in onshore gas transmission pipelines based on about 5900 field-measured wall thicknesses collected from a pipeline system in Canada. The collected data cover a wide range of pipe nominal wall thicknesses, from 3.18 to 16.67 mm. By considering the measurement error involved in the collected wall thickness data, statistical analyses indicate that the actual-over-nominal wall thickness ratio (AONR) follows a normal distribution with a mean of 1.01 and a coefficient of variation (COV) ranging from 1.6% to 2.2% depending on the nominal pipe wall thickness. The implications of the developed AONR statistics for the reliability analysis of corroded pipe joints are investigated. This study provides key input to the reliability-based design and assessment of pipelines with respect to various threats, such as metal-loss corrosion and stress corrosion cracking.

The decomposition of litter is one of the core links of the material cycling of ecosystem, studying this process is helpful for understanding the indirect effects of environmental alterations on the stability of ecosystems. In the traditional method litter is placed into litterbags which are placed outdoor on or in soil. Since under these conditions it is very difficult to analyze the parameters involved in litter decomposition separately, these experiments are also done indoor under controlled conditions. Inoculating the litter with soil or phyllosphere microorganisms and preventing any other interaction of the litter samples with the surrounding soil is an emerging method in the indoor investigation of plant litter decomposition, in order to experimentally minimize the impacts of invading soil constituents on litter decomposition parameters. However, few parallel-experiments were conducted to compare this inoculation method with the traditional litterbag method. Hence, it is unclear whether the inoculation method, when compared to the litterbag method, would cause artifacts with respect to the decomposition rate and microbial communities under controlled indoor conditions. In the present study, litters of Pinus tabuliformis, Salix babylonica and mixed litter from a Robinia pseudoacacia forest, representing litters with different decomposabilities, were chosen for a comparative experiment. Each type of litter was incubated under controlled indoor conditions for six months using the litterbag method and soil microorganism inoculation method. The decomposition rates and the litter fungal communities of the same litters were compared. Using microbial inoculation method, the decomposition rates exhibited no significant differences relative to those observed in the litterbag method. More importantly, the disturbance by biotic and abiotic factors from external soil on the fungal community in the samples could be excluded.

Dent-gouges as a result of the mechanical damage have serious implications for the burst capacity of oil and gas pipelines. The burst capacity of pipelines containing dent-gouges is lower than that of the same plain dented pipelines without gouges and that of the same gouged pipelines without dents. The well-known burst capacity prediction model adopted by the European Pipeline Research Group, i.e. the EPRG model, results in predictions of the burst capacity with high variability. In this study, a machine learning tool is employed to improve the predictive accuracy of the EPRG model for pipelines containing dent-gouges. To this end, a relatively large number of full-scale burst tests of pipe specimens containing dent-gouges are collected from the literature. The Gaussian process regression (GPR) technique, which is a class of non-parametric Bayesian model widely used in the machine learning, is employed to improve the EPRG model based on the collected full-scale burst test data. The full-scale burst tests are used to evaluate the hyper-parameters involved in the GPR analysis and validate the predictive accuracy of the improved EPRG model after the application of GPR. To facilitate the practical application of the improved EPRG model, a computer program with a graphic user interface (GUI) is further developed to compute the burst capacity of pipelines containing dent-gouges by inputting key parameters such as the pipe geometry and material properties as well as sizes of the dent and gouge through a GUI. This research will improve the fitness-for-service assessment of pipelines containing dent-gouges.

Near-neutral pH stress corrosion cracking (NNpHSCC) is one of the leading causes of failure for buried pipelines. Characterizing the NNpHSCC growth rate accurately remains a challenging task for the pipeline industry. In this study, an NNpHSCC growth model for buried pipelines is developed based on experimental data obtained from full-scale tests conducted at the CanmetMATERIALS of Natural Resources Canada of pipe specimens that are in contact with near-neutral pH environment and subjected to cyclic internal pressures. Four machine learning algorithms, namely the random forest (RF), extremely randomized trees (ET), gradient boosting (GB) and extreme gradient boosting (XGB), are employed to estimate the crack growth rates da/dN from input variables characterizing the pipe geometry, internal pressure and environmental condition. The machine learning models are trained through hyperparameter tuning and k-fold cross validation to improve the model robustness. Model performances are validated and compared using an independent test dataset. This study provides an initial step in using machine learning tools to develop robust NNpHSCC growth models suitable for practical applications.

This study aimed to investigate the effects of different overground surfaces on running biomechanics of non-rearfoot strikers. Thirty-one male habitual non-rearfoot strikers were required to run at 3.3 ± 0.2 m/s on a customized runway with artificial grass, concrete, or synthetic rubber surfaces in a random order. Vertical loading rates, three-dimensional ground reaction forces (GRFs), and lower-limb joint angles and moments were compared among surfaces. Regarding kinematics, significances were only detected in maximum knee flexion angle, with greater values when running on artificial grass compared to synthetic rubber or concrete. Regarding kinetics, changes were demonstrated in GRF peaks and lower-limb joint moments. GRF peaks were significantly greater when running on synthetic rubber or artificial grass compared to concrete; lower-limb joint moments were significantly lower when running on synthetic rubber compared to concrete; these changes were inconsistent when running on artificial grass compared to concrete. Significant differences were demonstrated in running kinetics when habitual non-rearfoot strikers ran on different overground surfaces. Running on artificial grass or synthetic rubber caused greater GRFs than running on concrete. However, only synthetic rubber could reduce joint loads.

Based on 14 naturally corroded full-scale pipe specimens, the present study investigates to what extent a corrosion depth threshold influences the burst capacity of naturally corroded pipelines predicted using the finite element analysis (FEA) and well-known RSTRENG model. The study also employs FEA and RSTRENG to investigate the accuracy and effectiveness of five representative interaction rules, namely the B31G, B31.4, CSA, DNV and CW rules, for combining individual naturally-occurring corrosion anomalies into clusters. The analysis results indicate that applying a corrosion depth threshold of 10% of the pipe wall thickness (t) (i.e. any corrosion depths < 10%t are ignored) has a negligible impact on the predicted burst capacity. Furthermore, the CSA interaction rule is considered the most effective among the five interaction rules investigated in that it captures the most critical region of a corroded area without resulting in very large corrosion clusters that are time-consuming to identify and analyze.

The present study develops a nonparametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties. The dependence structure and parameters of the NPBN model are extracted from Velázquez’s dataset, which consists of 250 samples of corrosion depths, pipeline age, and such local soil properties as the water content, redox potential, and pH value. The NPBN models the joint distribution of the corrosion depth, pipeline age, and local soil parameters by a Gaussian copula. The five-fold cross-validation is used to examine the predictive capability of the developed NPBN model. The results indicate that the predicted mean values of corrosion depths in general agree well with the corresponding field measurements, and more than 95% of the field-measured depths are within the 5 to 95 percentile range of the predicted distribution for the corrosion depth. The NPBN and the associated model mining method provide an effective data-driven approach to develop predictive models of corrosion depths using soil parameters as predictors. The developed NPBN will benefit the corrosion management of pipelines for which direct inspections are infeasible.

In this study, the interaction effects of closely spaced corrosion defects on the burst capacity of oil and gas pipelines under combined internal pressure and longitudinal compression are investigated by using parametric three-dimensional elasto-plastic finite element analyses. Full-scale burst tests reported in the literature are used to validate the finite element model. It is observed that the interaction effects of diagonally spaced defects on the burst capacity are strongly related to the overlapping portion of the defect width or circumferential spacing between the two defects. The analysis results indicate that the strongest interaction between diagonally spaced defects under combined loads occurs if the defects have zero circumferential separation. The interaction weakens as the defects are more and more overlapped or separated circumferentially. It is also observed that the interaction effect associated with longitudinally or circumferentially aligned, unequal-sized corrosion defects is negligible under the internal pressure only or combined loads.

The present study proposes a new semi-empirical burst capacity model for corroded oil and gas pipelines under combined internal pressure and longitudinal compression. The proposed model evaluates the burst capacity of a corroded pipeline under combined loads as the burst capacity of the pipeline under internal pressure only, which is developed in a recently completed study, multiplied by a correction factor to account for the effect of the longitudinal compression. Extensive parametric elastoplastic finite element analyses (FEA) are carried out, the results of which are used as the basis to develop the correction factor as a function of the corrosion defect sizes and magnitude of the longitudinal compressive stress. The proposed model is validated by a large set of parametric FEA and full-scale burst tests reported in the literature and is shown to provide marked improvements over two existing models, the DNV and RPA-PLLC models, for corroded pipelines under combined loads.

The present study integrates the corrosion growth modeling, reliability analysis and quantification of measurement errors of in-line inspection (ILI) tools in a single dynamic Bayesian network (DBN...

This paper is focused on the development of burst pressure models for thin-walled pipe elbows subjected to internal pressure. Three analytical burst pressure models are derived based on the Tresca, Mises, and ASSY yield criteria, respectively. A modified Goodall model is further proposed with markedly improved accuracy in burst pressure predictions compared with the original Goodall model. The proposed models are validated using full-scale burst tests of pipe elbows reported in the literature and parametric finite element analyses carried out in the present study. The accuracy of the proposed models is also compared with that of analytical burst pressure models reported in the literature.

The Tresca and von Mises criteria are two classic yield criteria for metals. While the von Mises criterion has been widely implemented in commercial finite element analysis (FEA) software package, the Tresca yield criterion in the context of the associated-plastic flow rule is not available in any well-known FEA packages such as ABAQUS and ANSYS. In this study, the Tresca yield criterion as well as the associated plastic flow and hardening rules are implemented in a user-defined material model (UMAT) in commercial FEA package ABAQUS based on the return-mapping technique. The consistent tangent operator for the Tresca criterion is also implemented in the UMAT model to facilitate the computation of the tangent stiffness matrix during the Newton-Raphson iteration for solving the nonlinear global equilibrium equations in the plastic domain. The constitutive integration algorithm for the Tresca UMAT is written in the principal stress space, which markedly simplifies the implementation. The implemented Tresca UMAT model is validated by theoretical solution and full-scale burst tests of both pristine and defected pipes. Furthermore, based on the Tresca-based and von Mises-based FEA predictions, a new formula is proposed to accurately predict burst pressure of thin-walled corroded pipes.

In this study, leaf litters of Betula albo-sinensis and 5 coniferous species were used as samples. The B. albo-sinensis leaf litter was buried in soil and termly treated with the water extracts of five types of coniferous leaf litter for a 6-month simulation decomposition experiment. The dynamics of mass loss and nutrients (C, N, P, and K) content of leaf litter and the soil enzymatic activities were measured to investigate the effects of plant secondary metabolites (PSM) from coniferous leaf litters on the decomposition processes of B. albo-sinensis leaf litter. The results indicated that the extracts of Pinus tabuliformis, Platycladus orientalis, P. armandii and Larix principis-rupprechtii leaf litters significantly inhibited the whole decomposition process and overall nutrients release of B. albo-sinensis leaf litter, while the extracts of Picea asperata leaf litter exhibited no significant influence. The general suppression of PSM on the soil sucrase, carboxymethyl cellulase and β-glucosidase activities might be the main reason leading to the inhibitory effects of the extracts of P. tabuliformis, P. orientalis, P. armandii and L. principis-rupprechtii leaf litter. The species causing inhibitory effects, especially L. principis-rupprechtii, was not recommended to be planted mixed with B. albo-sinensis, or their planting density should be lower in the mixed forests.

Unsupervised feature learning, as a promising tool for extracting features automatically, overcomes shortcomings of traditional feature extraction methods which generally take plenty of effort on designing features. Among various unsupervised feature learning methods, sparse filtering is an efficient and popular one owing to its simplicity with fewer hyper-parameters. However, the standard sparse filtering fails to consider the local structure of input samples, which may affect and restrict its feature learning ability. To address this deficiency, a new feature extraction approach named sparse filtering with local structure preserved (SF-Local) is proposed. The objective function of SF-Local is composed by the standard sparse filtering and a local structural regularization (LSR) which is formulated to preserve the local structure of the input samples. To design LSR, we adopt complexity-invariant distance to select neighbors for each sample rather than the widely used Euclidean distance, which is beneficial to establishing a reliable K-nearest graph. Based on SF-Local, an intelligent fault diagnosis method is developed by leveraging a supervised classifier. The developed diagnostic approach is applied to diagnose the bearing faults as well as its performance is evaluated on a bearing data set with a constant operating condition and a data set with variable operating conditions. Experimental results demonstrate the effectiveness and superiority of the proposed SF-Local.

Purpose: Sports surface is one of the known external factors affecting running performance and injury. To date, we have found no study that examined the lower extremity stiffness in habitual forefoot strikers running on different overground surfaces. Therefore, the objective of this study was to investigate lower extremity stiffness and relevant kinematic adjustments in habitual forefoot strikers while running on different surfaces. Methods: Thirty-one male habitual forefoot strikers were recruited in this study. Runners were instructed to run at a speed of 3.3 m/s (±5%) on three surfaces, named synthetic rubber, concrete, and artificial grass. Results: No significant differences were found in leg stiffness, vertical stiffness, and joint stiffness in the sagittal plane during running on the three surfaces (p > 0.05). Running on artificial grass exerted a greater displacement in knee joint angle than running on synthetic rubber (p = 0.002, 95% CI = 1.52–7.35 degrees) and concrete (p = 0.006, 95% CI = 1.04–7.25 degrees). In the sagittal plane, peak knee moment was lower on concrete than on artificial grass (p = 0.003, 95% CI = 0.11–0.58 Nm/kg), whereas peak ankle moment was lower on synthetic rubber than on concrete (p < 0.001, 95% CI = 0.03–0.07 Nm/kg) and on artificial grass (p < 0.001, 95% CI = 0.02–0.06 Nm/kg). Among the three surfaces, the maximal ground reaction forces on concrete were the lowest (p < 0.05). Conclusions: This study indicated that running surfaces cannot influence lower extremity stiffness in habitual forefoot strikers at current running speed. Kinematic adjustments of knee and ankle, as well as ground reaction forces, may contribute to maintaining similar lower extremity stiffness.

The present study investigates the impact of corrosion anomaly classes on the prediction accuracy of seven existing burst capacity models for corroded pipelines, namely B31G, B31G-M, Shell92, PCORRC, PCORRC-M, CSA and RSTRENG, based on 897 corrosion anomalies on 16 naturally corroded pipe specimens removed from in-service pipelines. The 897 corrosion anomalies are classified into seven classes, namely pin hole, axial slotting, axial grooving, circumferential slotting, circumferential grooving, pitting and general corrosion, based on the pipeline operators forum (POF) anomaly classification system. The seven burst capacity models and finite element analyses (FEA) are employed to evaluate the burst capacities of the corrosion anomalies. The accuracies of the burst capacity models are assessed and compared based on the FEA-to-model predicted burst capacity ratios for different classes of anomalies. The results suggest that the PCORRC model is suitable for the non-general classes of corrosion anomalies, whereas the CSA model is recommended for the general corrosion class of anomalies.

In-service pipelines are often subjected to longitudinal forces and bending moments resulting from, for example, ground movement or formation of free spans in addition to internal pressures. In practice, there are some site-specific cases where corrosion anomalies interact with the external loads. A refined assessment model is required to understand the load carrying capacity of pipe. In this study, a burst capacity model for corroded pipelines under combined internal pressure and axial compression is developed based on extensive parametric three-dimensional (3D) elasto-plastic finite element analyses (FEA) and artificial neural network (ANN) technique. The parametric FEA employs the ultimate tensile strength (UTS)-based burst criterion and idealizes corrosion defects as semi-ellipsoidal shaped flaws. The FEA model is validated by full-scale burst tests of pipe specimens containing semi-ellipsoidal shaped flaws reported in the literature. Extensive parametric FEA are carried out to evaluate the burst capacity of corroded pipelines under combined internal pressure and axial compression by varying the pipe geometric and material properties, defect depth, length and width, and magnitude of axial compressive stress. Based on the FEA results, an ANN model is developed utilizing the open-source platform PYTHON to predict the burst capacity of corroded pipelines under combined internal pressure and axial compression. The well-trained ANN model is further validated by full-scale burst tests of corroded pipelines under combined internal pressure and axial compression carried out by Det Norske Veritas (DNV).

In this paper, a facial expression recognition model for children was presented. Based on the existing research, this study divides common learner emotions into happiness, concentration, panic and boredom, and builds a large-scale learner emotion database based on this, and proposes a child emotion recognition method based on deep learning. Compared with traditional learner emotion recognition methods, this method has higher accuracy and robustness.