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ABSTRACTThe 12% Cr heat-resistant steel used for critical components of fossil-fired power plants works in high-temperature and high-pressure steam environments for a long time and inevitably undergoes a certain amount of plastic deformation during service. This plastic deformation will affect the microstructure and hot ductility of the heat-resistant steel and ultimately influence its service life. Therefore, the effect of hot compression deformation on microstructural evolution at 650°C was investigated in this work. As the amount of hot compression deformation increased, the number of subgrains significantly increased, and their sizes gradually decreased. When the amount of deformation reached 50%, dynamic recrystallization occurred, forming a preferred orientation in the rolling direction. and fiber texture appeared in the microstructure when the deformation amount reached 8%, and the intensity of texture was significantly higher than that of texture. Their intensity values further increased with the increase of the amount of deformation. As the amount of hot compression deformation increased, the deviation angle of the Kurdjumov–Sachs (KS) orientation relationship between M23C6 particles and α-Fe gradually increased. Their KS orientation relationship began to be disrupted at the deformation amount of 6% and was completely destroyed at the deformation amount of 20%. With the amount of deformation increasing up to 50%, there were no micropores at the M23C6/matrix interfaces, and no hot cracking occurred. This was mainly attributed to the occurrence of dynamic recrystallization which alleviated stress concentration at the particles /matrix interfaces. The mechanisms of microstructural evolution involving subgrains, grain boundary characteristics, texture evolution, strain distribution, and orientation relationship were discussed in detail.

Corrosive environments can adversely affect the fatigue performance of bridges and other building structures. In order to determine the influence of corrosion on the fatigue failure of concrete composite girders with a corrugated web on a steel bottom plate (hereinafter referred to as CGCWSB), a scaled model test was conducted on a CGCWSB with a span of 30 m, which served as the structural prototype. Through the model test, theoretical analysis, and numerical simulation, the influence of uniform corrosion and pitting corrosion on the fatigue failure of the CGCWSB was determined, and the propagation law of pitting fatigue crack was determined. The results show that (1) the uniform corrosion caused the stress of the CGCWSB to become larger and the performance of the CGCWSB was reduced, the stress growth of the test girder after corrosion was about 10%, the corrosion rate was 9%, the pitting unevenness coefficient was 1.25, and the relative corrosion life was 26.34 years; (2) the fatigue failure of the non-corroded girder belongs to the weld fatigue failure, and the fatigue failure of the corroded girder was the coexistence of weld fatigue failure and pitting fatigue failure; (3) uniform corrosion did not create a new fatigue source, but it did result in the test girder’s fatigue failure ahead of time. Pitting corrosion did, however, create a new fatigue source; (4) an exponential correlation was present between the propagation length of a pitting crack and the number of equal load cycles. The ultimate failure mode of a pitting fatigue crack was when the crack length reached the thickness of the plate and the component was torn and destroyed; (5) following corrosion, the fatigue life of the test girder was found to be reduced by 10.65%, which suggests that salt corrosion had a significant impact on the fatigue life of the composite girder. This research work can provide a reference for the design and promotion of the use of the CGCWSB.

Dislocation-dominated plastic deformation in austenitic NiTi alloys is highly detrimental and it could be suppressed by precipitation of Ni4Ti3. Additionally, at a certain temperature range, not only Ni4Ti3 precipitate but also the B19’ martensite produced from stress-induced martensitic transformation may prevent dislocation glide in austenitic NiTi alloys. In order to shed further light on this issue, four atomic configurations were constructed using an edge dislocation (a [100]) and two Ni4Ti3 precipitate variants (precipitate-V1 and precipitate-V2) to investigate dislocation glide in austenite containing Ni4Ti3 precipitate under shear at temperatures of 500 K or 700 K. As a result, all configurations at 500 K undergo the shear-induced martensitic transformation, producing B19’ martensite that impedes dislocation glide. And at 700 K, dislocation was pinned by precipitate or distortion zones caused by the superposition of strain field of precipitate and dislocation, without martensitic transformation. Furthermore, it was proven that precipitate-V1 impedes dislocation glide more strongly than precipitate-V2 at both temperatures of 500K and 700 K. It was attributed to the different in spatial relative location between the precipitate and dislocation and explained by the Orowan dislocation-precipitate bypass stress. Finally, three strategies for achieving high shear fatigue resistance in NiTi shape memory alloys were proposed based on these findings.

The insulation aging of cross-linked polyethylene (XLPE) cables is the main reason for the reduction in cable life. There is currently a lack of rapid and effective methods for detecting cable insulation defects in power-related sectors. To this end, this paper presents a method for identifying insulation defects in XLPE cables based on deep learning algorithms. First, the principle of the harmonic method for detecting cable insulation defects is introduced. Second, the ANSYS software is used to simulate the cable insulation layer containing bubbles, protrusions, and water tree defects, and the effects of each type of defect on the magnetic field strength and eddy loss current of the cable insulation layer are analyzed. Then, a total of 10 characteristic quantities of the total harmonic content and 2nd to 10th harmonic currents are constructed to establish a database of cable insulation defects. Finally, the deep learning algorithm, long short-term memory (LSTM), is used to accurately identify the types of insulation defects in cables. The results indicate that the LSTM algorithm can effectively diagnose and identify insulation defects in cables with an accuracy of 95.83%.

Fast and accurate analysis of orientations and deformation states from Kikuchi patterns is vital to understand the crystalline materials’ micro- and macro-mechanical behavior. Here, a pattern reconstruction-aided Siamese network is developed to extract the crystal orientations and deformation states from Kikuchi patterns. We demonstrate the pattern reconstruction technique is essential for accurate predictions at large-area EBSD scans since the remapping operation enables the Siamese network to consider changes in experimental setup parameters dynamically. Moreover, the reconstruction technique serves as a data augmentation method that expands 342,225 raw patterns to 5,475,600 reconstructed patterns. The resulting Siamese network achieves unprecedented accuracy and robustness over a wide range of experimental setup parameters including pattern center, camera elevation and exposure time. Compared to the cross-correlation based high-resolution EBSD (HR-EBSD) analysis, it shows improved robustness against large rotations and achieves accurate measurements even if the rotation angle is as large as 25°. Further investigation also indicates the Siamese network trained on pure aluminum can be generalized at copper, nickel and 316 stainless steel without retraining, suggesting broad and desirable applicability.

The microstructure and impact toughness in an ultra-high-strength casing were investigated, attempting to reveal the reason for toughness fluctuations along the casing in terms of crystallography. The morphological structures at the head of the casing are lath bainite, while those at the end are granular bainite and lath bainite. The head exhibits a higher density of high-angle grain boundaries dominated by block boundaries and a higher impact toughness than the end, showing an inhomogeneous microstructure and causing toughness fluctuations in the casing. The higher density of block boundaries in the head resulted from weaker variant selection because of its higher cooling rate than the end. Therefore, the underlying reason for the toughness fluctuations lies in the varying densities of the block boundaries along the casing triggered by uneven cooling.

Abstract This study examines the impact of communication on investors’ trading frequency based on a unique dataset drawn from a Chinese social trading platform. We find robust evidence that real-account portfolio owners on the platform trade more frequently under the influence of the comments posted by their leaders (the owners of portfolios they have followed). Moreover, portfolio owners are more sensitive to the quantity than to the tone of leaders’ comments. Finally, both trading frequency and leaders’ comments negatively impact portfolio owners’ future performance. Our findings support the notion that social interaction promotes active investment strategies.

Background: Maternal Toxoplasma gondii (T. gondii) infection during pregnancy has been associated with various mental illnesses in the offspring. Ginsenoside Rh2 (GRh2) is a major bioactive compound obtained from ginseng that has an anti-T. gondii effect and attenuates microglial activation through toll-like receptor 4 (TLR4)/nuclear factor-kappa B (NF-κB) signaling pathway. GRh2 also alleviated tumor-associated or lipopolysaccharide-induced depression. However, the effects and potential mechanisms of GRh2 on depression-like behavior in mouse offspring caused by maternal T. gondii infection during pregnancy have not been investigated. Methods: We examined GRh2 effects on the depression-like behavior in mouse offspring, caused by maternal T. gondii infection during pregnancy, by measuring depression-like behaviors and assaying parameters at the neuronal and molecular level. Results: We showed that GRh2 significantly improved behavioral measures: sucrose consumption, forced swim time and tail suspended immobility time of their offspring. These corresponded with increased tissue concentrations of 5-hydroxytryptamine and dopamine, and attenuated indoleamine 2,3-dioxygenase or enhanced tyrosine hydroxylase expression in the prefrontal cortex. GRh2 ameliorated neuronal damage in the prefrontal cortex. Molecular docking results revealed that GRh2 binds strongly to both TLR4 and high mobility group box 1 (HMGB1). Conclusion: This study demonstrated that GRh2 ameliorated the depression-like behavior in mouse offspring of maternal T. gondii infection during pregnancy by attenuating the excessive activation of microglia and neuroinflammation through the HMGB1/TLR4/NF-κB signaling pathway. It suggests that GRh2 could be considered a potential therapy in preventing and treating psychiatric disorders in the offspring mice of mothers with prenatal exposure to T. gondii infection.

Machine learning approaches (ML) based on data-driven models are conducive to accelerating the assessments of the martensitic transformation peak temperature (Tp) of TiZrHfNiCoCu high entropy shape memory alloys (HESMAs) over a huge composition space. In this work, an interpretable machine learning workflow was established through dataset construction, feature selection, modeling and validation, and model interpretation. We identified a set of key feature combinations closely related to Tp, by exploiting Pearson correlation selection, univariate feature selection, and forward feature elimination. The established ML model was then used to estimate the Tp of three newly synthesized alloys, with their prediction relative errors of less than 3 % in comparison with the experimental measurements. The behaviors of our ML model were interpreted by the Shapley Additive exPlainations (SHAP) approach, demonstrating the crucial role of CV22 (Allred Rochow electronegativity) in the prediction of Tp. In addition, the ML model in combination with our designed interpretation strategy was further used to investigate the effects of alloying elements on the Tp, which showed that the TiZrHfNiCoCu HESMAs with 9 ≦ Co (mol%) ≤ 10 and 15 ≦ Cu (mol%) ≤ 17 have pronounced positive effects on Tp.

In this paper, the fatigue resistance of superelastic NiTi shape memory alloy (SMA) wires was improved by combining mechanical training and nanocrystallization. Fatigue tests were performed after mechanical training with a peak stress of 600 MPa for 60 cycles of nanocrystalline (NC) NiTi wires, and the associated microscopic mechanism was investigated by using transmission electron microscopy (TEM) and transmission Kikuchi diffraction (TKD). The results showed that stress-controlled training effectively improved the functional stability (the accumulated residual strain decreased by 83.8% in the first 5000 cycles) of NC NiTi SMA wires, as well as increased the average structural fatigue life by 187.4% (from 4538 cycles to 13,040 cycles). TEM observations and TKD results revealed that training-induced dislocations resulted in lattice rotation and preferential grain orientation. The finite element method (FEM) simulation results indicated that the training-induced preferential grain orientation tended to decrease the local stress concentration and strain energy density. Combined with fractography analysis, the uniform deformation caused by mechanical training changed the crack growth mode from multi-regional propagation to single-regional propagation, improving the structural fatigue life.

Accurately predicting properties of steels containing martensite by using models based on traditional strengthening mechanisms remains a challenge. In this study, a smart machine learning model possessing two-dimensional microstructure input terminals was developed using high-throughput experiments and machine learning on steels for low-temperature service. An algorithm based on a convolutional neural network enriched with the two-dimensional input terminals increased the prediction accuracy, achieving an average microhardness error of as low as 14.37 HV for the validation set. The improved prediction accuracy is ascribed to the comprehensive strengthening mechanism and coupling of strengthening effects contained in the multifarious input terminals. The information acquisition and cross-correlation of substructures related to strengthening mechanism played an important role. The reported strategy can deepen the cognition of the strengthening mechanism of tempered martensite. It is promising for application to different steels containing tempered martensite.

A review of experiments and models for the strain rate effect of NiTi Shape Memory Alloys (SMAs) is presented in this paper. Experimental observations on the rate-dependent properties, such as stress responses, temperature evolutions, and phase nucleation and propagation, under uniaxial loads are classified and summarized based on the strain rate values. The strain rates are divided into five ranges and in each range the deformation mechanism is unique. For comparison, results under other loading modes are also reviewed; however, these are shorter in length due to a limited number of experiments. A brief discussion on the influences of the microstructure on the strain-rate responses is followed. Modeling the rate-dependent behaviors of NiTi SMAs focuses on incorporating the physical origins in the constitutive relationship. Thermal source models are the key rate-dependent constitutive models under quasi-static loading to account for the self-heating mechanism. Thermal kinetic models, evolving from thermal source models, address the kinetic relationship in dynamic deformation.

Hepatocellular carcinoma results in a high risk of second primary malignancies and has prominent morbidity and mortality. There is a lack of effective treatment and prognosis is poor. Therefore, effective drugs need to be discovered. Carrimycin is a 16-member macrolide antibiotic with anticancer activity, and monomeric isovalerylspiramycin I is a main component. The aim of this study was to determine the anti-tumor effects of carrimycin and monomeric isovalerylspiramycin I on hepatocellular carcinoma through in vivo and in vitro experiments. In vitro, changes in cellular proliferation, migration, invasion, and apoptosis were analyzed by MTT, colony formation, EdU labeling, wound-healing, matrigel transwell invasion, and flow cytometric assays using SK-Hep1, Hep3B, SNU-354, SNU-387 hepatocellular carcinoma cell lines. Western blotting and RT-PCR were used to detect the effects of carrimycin and monomeric isovalerylspiramycin I on the expression levels of vascular endothelial growth factor (VEGF) and programmed death ligand 1 (PD-L1). Nude mice were subcutaneously transplanted with SK-Hep1 cells or C57BL/6J mice were orthotopically transplanted with hepatocarcinoma H22 cells. Tumor volume, pathological changes in tumor tissues, and the concentration of VEGF in mouse serum were measured after treatments. Carrimycin and monomeric isovalerylspiramycin I dose-dependently inhibited hepatocellular carcinoma cell viability, colony formation, and DNA replication. These agents markedly suppressed migration and invasion and promoted apoptosis of the cell lines. Western blotting and RT-PCR demonstrated that carrimycin and monomeric isovalerylspiramycin I reduced VEGF and PD-L1 protein and mRNA levels in a dose-dependent manner. In vivo studies further confirmed that carrimycin and monomeric isovalerylspiramycin I could significantly inhibit tumor growth, tumor histopathological alterations, and the concentration of VEGF in both mouse tumor models. These results show that carrimycin and monomeric isovalerylspiramycin I promoted apoptosis and inhibited proliferation, migration, and invasion of hepatocellular carcinoma cells. Therefore, our discovery suggests anti-tumor capacity for carrimycin and monomeric isovalerylspiramycin I and provides data on potential new drugs for inhibiting hepatocellular carcinoma.

It is a well-documented phenomenon that individuals stop searching earlier than predicted by the optimal, risk-neutral stopping rule, leading to inefficient searches. Individuals' search behaviors during making investment decisions in financial markets can be easily affected by their peers. In this study, we designed a search game in a simplified experimental stock market in which subjects were required to search for the best sell prices for their stocks. By randomly assigning subjects into pairs and presenting them with real-time information on their peers' searches, we investigated the effects of peers' decisions on search behaviors. The results showed that two subjects in the same group with real-time peer information learned and engaged in similar search behaviors. However, this peer effect did not exist when subjects had access to feedback information on the ex-post best response. In addition, we found that the presence of information about peers' decisions alone had no significant impact on search efficiency, whereas access to both information on peers' decisions and feedback information significantly improved subjects' search efficiency.

Abstract Currently, transplanting mechanisms for dryland plug seedlings in China are mainly semiautomatic and have low efficiency. The rotary seedling pick-up mechanism with a planetary gear train for non-uniform intermittent transmission, and a concave and convex locking arc device, has a large rigid impact. To solve these problems, according to the design requirements for a dryland plug seedling transplanting mechanism, a rotary seedling pick-up mechanism of a planetary gear train with combined non-circular gear transmission of incomplete eccentric circular and non-circular gears was proposed. This has the characteristics of two-times greater fluctuation of the transmission ratio in a cycle, and can achieve a non-uniform continuous drive. Through analysis of the working principle of the seedling pick-up mechanism, its kinematics model was established. The human–computer interaction optimization method and self-developed computer-aided analysis and optimization software were used to obtain a set of parameters that satisfy the operation requirements of the seedling pick-up mechanism. According to the optimized parameters, the structure of the seedling pick-up mechanism was designed, a virtual prototype of the mechanism was created, and a physical prototype was manufactured. A virtual motion simulation of the mechanism was performed, high-speed photographic kinematics tests were conducted, and the kinematic properties of the physical prototype were investigated, whereby the correctness of the theoretical model and the optimized design of the mechanism were verified. Further, laboratory seedling pick-up tests were conducted. The success ratio of seedling pick-up was 93.8% when the seedling pick-up efficiency of the mechanism was 60 plants per minute per row, indicating that the mechanism has a high efficiency and success ratio for seedling pick-up and can be applied to a dryland plug seedling transplanter.

With data collected from a directed social trading network, this paper investigates how social interaction affects the disposition effect. We constantly observe a negative association between them: After being exposed to social interaction, a trader's odds ratio to sell a paper gain stock decreases by 9% to 10%, depending on different model settings. We then test the mechanisms of social interaction by decomposing it into three channels: learning intensity (willingness to learn), learning quality (information advantage through learning), and public scrutinization (exposure of trading outcome to others). We find that all three channels contribute to a smaller disposition effect. Specifically, our findings support the claim that public scrutinization promotes self-consciousness and reduces disposition effect. Also, our results extend previous studies on investors' information advantage by suggesting that it could also help to mitigate the disposition effect through the reduction of uncertainty. Overall, this paper suggests a positive role of social trading platforms in helping investors make better decisions.

Rapid development of intelligent information equipment accelerates the expansion of mobile social network. Speed of information spreading is gradually growing, there are lots of changes in the scale and mode of information spreading. But the basic communication network is not developed and not mature, when online information platforms breakdown sometimes it happens to be when important information appears. Therefore, the research is done to solve these occasion problems, help network information platform filter hot news and discuss the reason that hot news exists longer than other news in the Internet. In this paper, a multiple information propagation model incorporating both local information environment and people's limited attention is proposed based on Susceptible Infected Recovered (SIR) model. Two new concepts are introduced into the model: heat rate and popular rate, to measure the local information influence power and people's limited attention to information respectively, which are key factors determining node state transformation instead of fixed probability. In order to analyze the influence from limited attention, a situation is designed that several pieces of information are popular successively. The theoretical analysis shows that the early popular information gets more attention than the later popular information, and more attention makes it easier to spread. Besides, numerical simulation is conducted in both uniform network and scale-free network. The simulation results show that the early popular information is less vulnerable to the increase of information acceptance threshold and more sensitive to the decrease of information rejection threshold than the later popular information. Moreover, the model can also be used in the case of large amount of information transmission without adding too much complexity. Reasons are given in the research that the top hot news exists very much longer than the other ones, and latter news which have same influence as top news are hard to get the same focus. Meanwhile, results in the research can provide some ways for the other researches in the related fields. They also help related information platforms to filter and push news and referable strategies to maintain hot news.

The combination of multilayer feedforward neural networks (MLFFNN) and thermodynamic knowledge has excellent advantages to study the complicated phenomena in material science. In the present study, a thermodynamic knowledge-based MLFFNN has been developed to predict the martensite start temperature (MS) of steels that accounts for the variations in critical driving force (ΔGc) and austenitization temperature (Tγ). This is achieved by integrating two crudely estimated properties (ΔGc and Tγ) in the feature space. Instead of using the original dataset directly, the feature space was reshaped by kernel principal component analysis (KPCA). The genetic algorithm was implemented to find a suitable hyperparameters set that was able to induce a model with high predictive capability. The resulting neural network performs well in the present dataset, and the RMSE in the unseen datasets is 21.52 K. Benchmarking of the MLFFNN predictions against JmatPro-V8 calculations also shows a significant improvement in predictive accuracy. Results indicate KPCA and the integration of crude estimation property (CEP) boost the predictive accuracy of MLFFNN. Besides, the present study also demonstrates the CEP strategy is general enough to be employed in several well-known machine learning methods including support vector regression, decision tree and Gaussian process regression.

This study developed a powder feeding laser metal deposition (LMD) method that can be used to achieve a layer-by-layer fabrication of component grade material for efficiently screening the properties of different chemical compositions and determining the optimized composition range for TRIP stainless steel. The 13Cr-xNi-(10-x)Mn-3Al steel with continuous graded composition was produced by the LMD method. An optimized composition range for TRIP stainless steel was determined by testing the properties including hardness, austenite content and surface potential of positions with different compositions. The suggested Ni content is between 3%–6% and Mn content is between 4%–7%. This study demonstrates the potential of LMD method to accelerate the development of steel economically. Keywords: Component gradient, Layer metal deposition, Layer by layer

Many studies have discussed the neural basis of asset bubbles. They found that the dorsolateral prefrontal cortex (DLPFC) played an important role in bubble formation, but whether a causal relationship exists and the mechanism of the effect of the DLPFC on bubbles remains unsettled. Using transcranial direct current stimulation (tDCS), we modulated the activity of the DLPFC and investigated the causal relationship between the DLPFC and the asset bubble in the classical learning-to-forecast experiment. 126 subjects were randomly divided into three groups and received different stimulations (left anodal/right cathodal, right anodal/left cathodal, or sham stimulation), respectively. We also conducted a 2-back task before and after stimulation to measure changes in subjects’ cognitive abilities and explore in detail the cognitive mechanism of the effect of DLPFC stimulation on asset bubbles. Based on our results, we found that the bubble of the left anodal/right cathodal stimulation group was significantly smaller than that of the sham stimulation group. In the meantime, subjects performed significantly better in the 2-back task after left anodal/right cathodal stimulation but not right anodal/left cathodal or sham stimulation, which is consistent with their performance in the learning-to-forecast experiment, supporting the cognitive mechanism to some extent. Furthermore, we examined different forecasting rules across individuals and discovered that the left anodal/right cathodal stimulation group preferred the adaptive learning rule, while the sham and right anodal/left cathodal stimulation groups adopted a pure trend-following rule that tended to intensify market volatility aggressively.

The nuclear factor-κB (NF-κB) transcription factors control many physiological processes including inflammation, apoptosis, and angiogenesis. In our search for NF-κB inhibitors from natural resources, we identified 4′,6-dihydroxy-4-methoxyisoaurone (ISOA) as an inhibitor of NF-κB activation from the seeds of Trichosanthes kirilowii. However, the mechanism by which ISOA inhibits NF-κB activation is not fully understood. In the present study, we demonstrated the effect of ISOA on NF-κB activation in TNF-α-stimulated HeLa cells. This compound suppressed NF-κB activation through the inhibition of IκB kinase (IKK) activation. ISOA also has an influence on upstream signaling of IKK through the inhibition of expression of adaptor proteins, TNF receptor-associated factor 2 (TRAF2) and receptor interacting protein 1 (RIP1). Consequently, ISOA blocked the phosphorylation and degradation of the inhibitor of NF-κB alpha (IκBα), and subsequent phosphorylation and nuclear translocation of p65. The suppression of NF-κB activation by ISOA led to the down-regulation of target genes involved in inflammation, proliferation, as well as potentiation of TNF-α-induced apoptosis. Taken together, this study extends our understanding on the mechanisms underlying the anti-inflammatory and anti-cancer activities of ISOA. Our findings provide new insight into the molecular mechanisms and a potential application of ISOA for inflammatory diseases as well as certain cancers.

Studies of search behavior have shown that individuals stop searching earlier and accept a lower point than predicted by the optimal, risk-neutral stopping rule. This behavior may be related to individual risk preferences. Studies have also found correlativity between risk preferences and the dorsolateral prefrontal cortex (DLPFC). As risk attitude plays a crucial role in search behavior, we studied whether modulating the activity of DLPFC, by using a transcranial direct current stimulation (tDCS) device, can change individual search behavior. We performed a sequential search task in which subjects decided when to accept a point randomly drawn from a uniform distribution. A total of 49 subjects (23 females, mean age = 21.84 ± 2.09 years, all right-handed) were recruited at Zhejiang University from May 2017 to September 2017. They repeated the task in 80 trials and received the stimulation at the end of the 40th trial. The results showed that after receiving right anodal/left cathodal stimulation, subjects increased their searching duration, which led to an increase in their accepted point from 778.17 to 826.12. That is, the subjects may have changed their risk attitude to search for a higher acceptable point and received a higher benefit. In addition, the effect of stimulation on search behavior was mainly driven by the female subjects rather than by the male subjects: the female subjects significantly increased their accepted point from 764.15 to 809.17 after right anodal/left cathodal stimulation, while the male subjects increased their accepted point from 794.18 to 845.49, but the change was not significant.

A polymer cable–driven parallel robot has a wide range of potential industrial applications by virtue of its light actuator dynamics, high payload capability, and large workspace. However, due to a viscoelastic behavior of polymer cable and difficulty in actual cable length measurement, there have been inevitable position and tracking control accuracy problems such as pick and place a high payload application. In this article, to overcome control problem, we propose a model-based open-loop control with the cable elongation compensation via experimentally driven cable model and switching control logic without additional Cartesian space feedback signal. The approach suggests a five-element cable model that is made with series combination of a linear spring and two Voigt models as a function of payload and cable length that are available to be measured in real-time. Experimental results show that using the suggested method, the cable length error due to viscoelastic effect can be compensated, and thus the position control accuracy of the polymer cable–driven parallel robot improved remarkably especially in gravity direction.

Abstract.: 4′,6-Dihydroxy-4-methoxyisoaurone (ISOA) is an isoaurone compound isolated from Trichosanthes kirilowii seeds, which was identified as an inhibitor of tumor growth. However, the mechanism by which ISOA inhibits hypoxia-inducible factor-1 (HIF-1)-mediated tumor growth is not fully understood. We here demonstrated the effect of ISOA on HIF-1 activation. ISOA showed a potent inhibitory activity against HIF-1 activation induced by hypoxia in various human cancer cell lines. This compound markedly decreased the hypoxia-induced accumulation of HIF-1α protein dose-dependently, whereas it did not affect the expressions of HIF-1β and topoisomerase-I (Topo-I). Further analysis revealed that the suppression of HIF-1α accumulation by ISOA was closely correlated with strong dephosphorylation of Akt, mammalian target of rapamycin (mTOR), and its effectors ribosomal protein S6 kinase (p70S6K) and eukaryotic initiation factor 4E-binding protein-1 (4E-BP1), a pathway known to regulate HIF-1α expression at the translational level. Furthermore, ISOA prevented hypoxia-induced expression of HIF-1 target genes and suppresses the invasiveness of tumor cells. Taken together, our results suggested that ISOA is an effective inhibitor of HIF-1 through targeting Akt/mTOR/p70S6K/4E-BP1 pathway, thereby, providing new perspectives into the mechanism of its anticancer activity. Keywords:: 4′,6-dihydroxy-4-methoxyisoaurone, HIF-1α, translation, antitumor

The nuclear factor-κB (NF-κB) transcription factors control many physiological processes including inflammation, immunity, and apoptosis. In our search for NF-κB inhibitors from natural resources, we identified yangonin from Piper methysticum as an inhibitor of NF-κB activation. In the present study, we demonstrate that yangonin potently inhibits NF-κB activation through suppression of the transcriptional activity of the RelA/p65 subunit of NF-κB. This compound significantly inhibited the induced expression of the NF-κB-reporter gene. However, this compound did not interfere with tumor necrosis factor-α (TNF-α)-induced inhibitor of κBα (IκBα) degradation, p65 nuclear translocation, and DNA-binding activity of NF-κB. Further analysis revealed that yangonin inhibited not only the induced NF-κB activation by overexpression of RelA/p65, but also transactivation activity of RelA/p65. Moreover, yangonin did not inhibit TNF-α-induced activation of p38, but it significantly impaired activation of extracellular signal-regulated kinase 1/2 and stress-activated protein kinase/c-Jun NH2-terminal kinase. We also demonstrated that pretreatment of cells with this compound prevented TNF-α-induced expression of NF-κB target genes, such as interleukin 6, interleukin 8, monocyte chemotactic protein 1, cyclooxygenase-2 and inducible nitric oxide. Taken together, yangonin could be a valuable candidate for the intervention of NF-κB-dependent pathological conditions such as inflammation. Keywords:: yangonin, nuclear factor-κB (NF-κB), RelA/p65, transactivation, inflammation

A cable-driven parallel robot has benefits of wide workspace, high payload, and high dynamic response owing to its light cable actuator utilization. For wide workspace applications, in particular, the body frame becomes large to cover the wide workspace that causes robot kinematic errors resulting from geometric uncertainty. However, appropriate sensors as well as inexpensive and easy calibration methods to measure the actual robot kinematic parameters are not currently available. Hence, we present a calibration sensor device and an auto-calibration methodology for the over-constrained cable-driven parallel robots using one-dimension laser distance sensors attached to the robot end-effector, to overcome the robot geometric uncertainty and to implement precise robot control. A novel calibration workflow with five phases—preparation, modeling, measuring, identification, and adjustment—is proposed. The proposed calibration algorithms cover the cable-driven parallel robot kinematics, as well as uncertainty modeling such as cable elongation and pulley kinematics. We performed extensive simulations and experiments to verify the performance of the suggested method using the MINI cable robot. The experimental results show that the kinematic parameters can be identified correctly with 0.92 mm accuracy, and the robot position control accuracy is increased by 58%. Finally, we verified that the developed calibration sensor devices and the calibration methodology are applicable to the massive-size cable-driven parallel robot system.

The hepatoprotective effects of sarmentosin-containing extracts of Sedum sarmentosum (SS) in d-galactosamine ( d-GalN) / lipopolysaccharide (LPS)–induced fulminant hepatic failure mouse model. Pretreatment with SS markedly protected mice from lethal liver injury, which has known to be associated with an abrupt elevation of serum tumor necrosis factor (TNF)-α level. Indeed, SS significantly blocked the elevation of TNF-α and alanine aminotransferase and aspartate aminotransferase as well. SS also remarkably reduced number of apoptotic hepatocytes and DNA fragmentation in the liver, which correlated with blockade of caspase-3 activation. In addition, SS suppressed the increased expression of toll-like receptor 4 (TLR4). The activation of c-Jun NH2-terminal kinase, extracellular signal-regulated kinase, and p38 induced by d-GalN/LPS was also significantly suppressed by SS treatment. Furthermore, SS significantly inhibited the activation of nuclear factor-κB. In RAW 264.7 cells stimulated with LPS, TNF-α release and TLR4 expression was suppressed by SS pretreatment, which was in line with in vivo results. These findings suggested that SS prevents d-GalN/LPS–induced fulminant hepatic failure, and this protection is likely associated with its anti-apoptotic activity and the down-regulation of mitogen activated protein kinase activity associated at least in part with suppressing the transcription of LPS receptors. Keywords:: Sedum sarmentosum, d-galactosamine (d-GalN) / lipopolysaccharide (LPS), toll-like receptor 4 (TLR4), tumor necrosis factor (TNF)-α, nuclear factor (NF)-κB

Cryptopleurine, a phenanthroquinolizidine alkaloid, was known to exhibit anticancer activity; however, the underlying mechanism is poorly understood. Because the nuclear factor-κB (NF-κB) transcription factors control many physiological processes including inflammation, immunity, and development and progression of cancer, we investigated the effects of cryptopleurine on tumor necrosis factor alpha (TNF-α)-induced NF-κB activation pathway and on the expression of NF-κB-regulated gene products associated with many pathophysiological processes.MDA-MB231, MDA-MB435, MCF-7, HEK293, RAW264.7 and Hep3B cells were used to examine cryptopleurine's effect on the NF-κB activation pathway. Major assays were promoter-reporter gene assay, electrophoretic mobility shift assay (EMSA), in vitro immune complex kinase assay, real-time PCR, Western blot analysis, and Matrigel invasion assay. Experiments documenting cell proliferation and apoptosis were analyzed by MTT method and flow cytometry, respectively. The results indicated that cryptopleurine suppressed the NF-κB activation through the inhibition of IκB kinase (IKK) activation, thereby blocking the phosphorylation and degradation of the inhibitor of NF-κB alpha (IκBα) and the nuclear translocation and DNA-binding activity of p65. The suppression of NF-κB by cryptopleurine led to the down-regulation of gene products involved in inflammation, cell survival, proliferation, invasion, and angiogenesis.Our results show that cryptopleurine inhibited NF-κB activation pathway, which leads to inhibition of inflammation, proliferation, and invasion, as well as potentiation of apoptosis. Our findings provide a new insight into the molecular mechanisms and a potential application of cryptopleurine for inflammatory diseases as well as certain cancers associated with abnormal NF-κB activation.