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The modification of stereoregulated functionalized polymers on the surface of natural rubber (NR) is a challenging task. This work provided a practicable grafting method to design photochromic polymer brushes on the NR surfaces using surface-initiated ring-opening metathesis polymerization (SI-ROMP). The designed photochromic polymer brushes were based on norbornenes with spiropyrin (SP) pendents, and grew from the surface of NR films in the presence of Grubbs 2nd catalyst. The results showed that the grafting rate of polymer brushes with photoisomerization structure reached up to 44.91 %. The modified natural rubber (NR-SP) films exhibited rapid coloring/decolorization property under UV irradiation and visible light. This photochromic property initiated by the light-induced isomerisation of the spiropyran structure and the increased surface energy under light stimuli. NR-SP films exhibited stable photochromic characteristics and outstanding anti-fatigue properties after exposing to over six cycles of consecutive UV/Vis irradiation. A simple and practical strategy for the preparation of highly sensitive photochromic natural rubber materials is proposed and provides a potential application in the fields of information storage and anti-counterfeiting.

Abstract Background Circular RNAs (circRNAs) are important regulators for ovarian cancer (OC). Circ_0001741 has been found to be highly expressed in OC samples and is involved in regulating paclitaxel resistance in OC cells. Therefore, circ_0001741 may play a vital role in OC process, and its potential molecular mechanism is worth further revealing. Methods Circ_0001741, miR-491-5p, and PRSS8 levels in OC tumor tissues and cells were quantified by quantitative real-time PCR or western blot. The proliferation, apoptosis and metastasis of OC cells were detected by cell counting kit 8 assay, Edu assay, flow cytometry, and transwell assay. RNA interaction was verified by dual-luciferase reporter assay and RIP assay. Xenograft assay was used to detect the effect of circ_0001741 knockdown on OC tumor growth in vivo. Results Circ_0001741 was upregulated in OC tissues and cell lines. Knockdown of circ_0001741 repressed OC cell proliferation, metastasis, and enhanced apoptosis. Mechanistically, miR-491-5p was targeted by circ_0001741, and miR-491-5p inhibitor could attenuate the effect of circ_0001741 silencing on OC cell progression. Meanwhile, PRSS8 was a target of miR-491-5p, and miR-491-5p overexpression inhibited OC cell progression by targeting PRSS8. Circ_0001741 regulated PRSS8 expression by sponging miR-491-5p. Besides, circ_0001741 knockdown also inhibited OC tumor growth in vivo. Conclusion Our data showed that circ_0001741 could promote the growth and metastasis of OC cells through the miR-491-5p/PRSS8 axis, which provided a potential molecular target for the treatment of OC.

To investigate the effect of autoclaving treatment modifications on the multi-scale structure and physicochemical properties of yellow rice starch, a comparative analysis of the multi-scale structure (morphology structure, crystal structure, and molecular chain structure) and gelatinization characteristics were conducted on the pre- and post-modified starch using high-performance anion exchange chromatography, X-ray diffraction, infrared spectroscopy, scanning electron microscopy, differential scanning calorimetry, and rapid viscosity analyzer. The results showed that autoclaving treatment modifications did not significantly alter the total starch content of yellow rice starch. However, the content of amylose increased from 7.46% to 8.28%. In comparison to the original starch, the modified yellow rice starch exhibited an increase in the proportion of short chains, while the proportions of medium and long chains significantly (P

In various fuzzy multiple attribute decision making (MADM) applications, different information descriptions and aggregation operators (AOs) play a crucial role. However, both the Einstein sum and product can include their typical algebraic operation features, but they lack the characteristics of periodicity operations. To fill the research gap of Einstein AOs for single-valued neutrosophic values (SvNVs), this article aims to propose Einstein AOs of sine SvNVs and their MADM model as their new extension. In this study, we first define a new sine SvNV, which integrates sine functions into the membership functions of indeterminacy, falsehood, and truth, and the Einstein operation laws of sine SvNVs. Then, we present the sine SvNV Einstein weighted average and geometric AOs and their properties. Furthermore, we develop a MADM model based on the proposed Einstein AOs in a SvNV circumstance. Lastly, we apply the developed MADM model to a site selection example of a hydrogen power plant as the verification of its application in a SvNV circumstance. The decision results reveal the rationality and validity of the developed model with respect to the comparison of the related models.

Abstract Background Avoidant/Restrictive Food Intake Disorder (ARFID) is a newly described eating disorder. Adequate levels of food literacy allow individuals to have adequate food choices. This study aimed to assess the prevalence of ARFID and the level of food literacy in patients with inflammatory bowel disease (IBD) and to analyse the correlation between ARFID and food literacy. Method This cross-sectional study screened for ARFID and assessed food literacy levels in patients with IBD attending four tertiary hospitals in China. ARFID risk was measured using the Nine Item Avoidant/Restrictive Food Intake Disorder Screen (NIAS). Food literacy was assessed using the Food Literacy Evaluation Questionnaire (Chinese version, FLEQ-Ch).The relationship between individual NIAS scores and food literacy variables was analysed to assess which food literacy aspect is positively or negatively associated with NIAS scores. Stepwise linear regression analysis was performed to identify the possible predictors of NIAS scores in patients with IBD. Result A total of 372 IBD subjects completed the NIAS and FLEQ-Ch. The overall mean NIAS scores for the IBD cohort was 28.16 ± 8.03 (p

In this study, the effects of starch emulsion concentration, autoclave time and autoclave temperature on the preparation of glutinous millet resistant starch (GMRS) were systematically investigated by using the yield of GMRS as the detection index. Then, the physicochemical properties and microstructure of glutinous millet starch (GMS) before and after autoclave treatment were further analyzed and compared. The influence of different ratio of GMRS addition on the texture and GI value of biscuits were studied. The results showed that the optimum conditions for the preparation of GMRS by autoclave method were starch emulsion concentration of 10%, autoclave time of 40 min, and autoclave temperature of 120 ℃. Under these conditions, the yield of GMRS was 30.64%. Further detection indicated that compared with GMS, the light transmittance, solubility and swelling power of GMRS were decreased significantly. Unlike GMS, which had small particle size and relatively smooth surface, the morphology of GMRS prepared by autoclave method was flaky and rough surface with pore like depression. The data of the further experiments, in which GMRS was used to replace part of the flour to make biscuits, showed that with the increasing of the amount of GMRS, the shear force of biscuits increased, and the GI value decreased significantly, the biscuits could be transformed from high GI value food to medium GI value food, meeting the needs of consumers for low GI value food.

Fuzzy decision-making is a critical research topic in uncertain decision-making issues. Under uncertain scenarios, a group of decision makers/experts presents the fuzzy evaluation data of the criteria to an alternative. In this case, we can use a fuzzy multi-valued set (FMVS) to express them. To solve the operation problem between different fuzzy sequence lengths in FMVSs and ensure some confidence level of fuzzy assessment values from the perspective of probability, this paper first proposes a transformation technique from FMVS to a confidence neutrosophic number cubic set (CNNCS) based on confidence levels and normal distribution of fuzzy values in FMVS. Then, we present an exponential similarity measure between CNNCSs and its group DM model with some confidence levels and normal distribution in a FMVS circumstance. Finally, the developed group DM model is applied to the selection of intelligent manufacturing equipment, and then the decision results corresponding to the 90%, 95%, and 99% confidence levels reveal the decision flexibility and rationality/reliability.

Background Dietary management plays a crucial role in the treatment of patients with ulcerative colitis (UC). While various e-services provide dietary advice, the long-term dietary management requires continuous monitoring and dynamic adjustment to accommodate the evolving nature of the disease and meet the patients’ nutritional needs. Consequently, the development of a novel dietary management tool that incorporates diet tracking, personalized nutritional feedback, and evidence-based advice becomes imperative. This study aims to address this need by developing a WeChat applet called “HealthyGut” specifically designed for the dietary management of UC patients, and evaluate its feasibility, acceptability, and preliminary efficacy. Methods A total of 134 UC patients were equally allocated into the intervention group (receiving a 12-week mobile-based dietary management via HealthyGut) and control group (receiving a paper-based food diary and routine advice). The feasibility outcomes were recruitment, retention, engagement, satisfaction, and acceptability in the intervention group. Dietary intakes were effective outcomes. Results Both groups had satisfactory retention rates (89.6% and 77.6%, respectively). The System Usability Scale in the intervention group yielded “good usability” with a mean score of 79.63 (SD 7.39), and all participants reported good user experiences and perceived benefits after using HealthyGut. At week 12, intervention responders reported significantly higher daily energy intake than control group (Z = −3.089, p = 0.002). Conclusions and Implications The results display that HealthyGut as a dietary management tool is feasible and accepted by UC patients, and it may help them make healthier food choices. Larger sample studies should be considered in the future.

Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95.

The fruits of 12 blueberry varieties cultivated in the open field in southern Liaoning such as 'Bluegold', 'Reka', 'N5' were used as test materials. The best varieties for processing were selected through the analysis of fruit appearance, nutritional quality and processing performance indexes. The results showed that the blueberry fruit was mainly blue, flat spherical in varying degrees with bloom, and the single fruit weight was 0.95~2.63 g. The fruit contained more than 80% water, the larger the fruit the higher the water content, and the more flat. The fruit contained fructose, glucose, oxalic acid, quinic acid, malic acid, shikimic acid and citric acid. The content of fructose was equal to that of glucose, and the content of citric acid was significantly higher than that of other organic acids (P90 mg∙g−1), organic acid content (>10 mg∙g−1) and juice yield (>74.6%) of 'Sunrise', 'Bluegold' and 'Patroit' were higher, which were better processed varieties. In conclusion, the fruits of 'Sunrise', 'Bluegold' and 'Patroit' were rich in nutrition and high juice yield. They were ideal raw materials for processing.

Abstract As one of the most important ADAS systems, traditional adaptive cruise control is only suitable for relatively simple working conditions such as expressways, and it is difficult to deal with complex traffic environment and road working conditions. For the problem that the traditional cruise control strategy cannot effectively use the terrain to reduce vehicle fuel consumption under slope conditions and when the vehicle enters a small radius curve such as a ramp, the excessively high turning speed will cause the vehicle to skid. This paper proposes an adaptive cruise control system architecture with adaptability to multiple operating conditions. Simulation and real‐vehicle test results show that the adaptive control system architecture and optimisation algorithm proposed in this paper have good applicability to road conditions, and improve the economy of the system under ramp conditions and safety under curve conditions.

The current study examines the influence of word class (i.e., noun vs. adjective) and valence (i.e., positive vs. negative vs. neutral) on the processing of emotional words under different virtual reality (VR) emotional contexts. To this end, 115 participants performed a modified affect labeling task after experiencing different VR scenarios. Their galvanic skin responses were also examined to further gauge the different effects of VR contexts. The results demonstrated significant main effect for word valence, indicating more processing of positive words relative to neutral words which are processed more than negative words. The results also demonstrated significant main effect for word class, indicating more processing of nouns in contrast to adjectives. Additionally, the results indicated that both positive and negative VR contexts could stimulate participants to select more positive words though negatively valenced words were processed more under negative VR context relative to positive VR context. However, the amplitude of galvanic skin responses in positive VR was lower than that in negative VR. The results were interpreted in line with the situation-consistency effects, the mood-consistency effects, the specific nature of VR context, and the different features of different word classes in terms of concreteness, imageability, arousal, and valence.

In practical applications, how to use the complementary strengths of the direct and the feature-based methods for effective fusion may be the main challenge of simultaneous localization and mapping (SLAM). To solve this challenge, we propose the DO-SLAM, a novel fast and accurate semi-direct visual SLAM framework, which can maintain the direct method’s fast performance and the high precision and loop closure capability of the feature-based method. The direct method is used as the first half of the DO-SLAM to track the camera pose rapidly and robustly. The feature-based method is used as the second half of the DO-SLAM to refine the keyframe poses, perform loop closures, and build a globally consistent, long-term, sparse feature map that can be reused. The proposed pipeline fuses direct odometry and feature-based SLAM to perform three levels of parallel optimizations: (1) In the direct method module, the keyframe poses are estimated by minimizing the photometric error, (2) In the feature-based module, using the poses calculated by the inter-frame matching to correct and fuse the poses calculated by the direct method module as the initial poses, and the initial poses are optimized by the motion-only bundle adjustment, and (3) A pose graph optimization is used to achieve global map consistency in the presence of loop closures. Experimental evaluation on two benchmark datasets demonstrates that the proposed approach achieves higher accuracy and robustness on motion estimation compared to the other state-of-the-art methods.

Abstract Background Crohn’s fistula-in-ano is a refractory disease in colorectal and anal surgery. Although autologous adipose-derived stem cell (ADSC) has been used in the treatment of Crohn’s fistula-in-ano because of its convenience, non-incision of normal tissue, good tolerance, repeatability, quick recovery, less pain, less damage to anal function, and high quality of life during the perioperative period, there are no reports of its use in China. This is the first clinical trial in China on the treatment of Crohn’s fistula-in-ano with ADSC to evaluate its efficacy and safety. Methods A total of 22 patients with Crohn’s fistula-in-ano were enrolled in this study from January 2018 to October 2018 in the Colorectal Disease Center of Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine. Patients were divided (1:1) into an observation group (ADSC) and a control group (incision-thread-drawing procedure). Primary efficacy endpoint evaluated at months 3, 6, and 12 was the closure of fistulas (closure of all treated fistulas at baseline, confirmed by doctor’s clinical assessment and magnetic resonance imaging or transrectal ultrasonography). The patients additionally completed some scoring scales at each follow-up including simplified Crohn’s Disease Activity Index (CDAI), Perianal Disease Activity Index (PDAI), Inflammatory Bowel Disease Questionnaire (IBDQ), pain scores with visual analog score (VAS), and Wexner score. The data of inflammatory indexes were also collected. Results The healing rates of the observation group and the control group at months 3, 6, and 12 were as follows: 10/11(90.9%) vs 5/11(45.5%), 8/11(72.7%) vs 6/11(54.5%), and 7/11(63.6%) vs 6/11(54.5%), respectively. There was no statistical difference between the two groups. In addition, the improvement in simplified CDAI, PDAI, IBDQ, VAS, and Wexner score of the observation group were better than that of the control group at each follow-up. The inflammatory indexes decreased in both the observation group and the control group at 3 months follow-up. And there were no significant differences in the changes of inflammatory indexes between two groups at month 3 compared with the baseline. Safety was maintained throughout month 12, and adverse events occurred in 63.6% of patients in the observation group and 100% patients in the control group. And no adverse event associated with ADSC injection was observed in the study. Conclusion ADSC is a feasible and effective treatment for Crohn’s fistula-in-ano, compared with traditional incision and thread-drawing. It can protect anal function of patients, relieve pain, allow quick recovery, be well-tolerated, and improve the quality of life during perioperative period. Trial registration China Clinical Trials Registry, No. ChiCTR1800014599. Registered 23 January 2018.

Growing interest has been shown in the effects of the xu-argument based translation continuation task, which have been mainly explored via the linguistic dimension. The current study, using a pretest-intervention-posttest design, investigated the dynamic relationships among translation learning anxiety, foreign language learning proficiency, and English–Chinese translation strategies under an iterative translation continuation task (ITCT) that lasted 13 turns. The results yielded from 134 student translators showed a significant increase in their translation strategies comprehension and production, with those with a medium level of translation learning anxiety and foreign language learning proficiency achieving the most. It also showed that the significant partial mediating effect of translation learning anxiety between foreign language learning proficiency and the production of translation strategies in the pre-test became insignificant in the post-test, and the insignificant correlation between the comprehension and production of translation strategies in the pre-test became significant in the post-test. The dynamic relationships among translation learning anxiety, foreign language learning proficiency, and translation strategies confirmed the mediating role of the ITCT in attenuating the impact of higher level of translation learning anxiety and lower level of foreign language learning proficiency on the comprehension and use of translation strategies, though its effects could be different for student translators with different levels of translation anxiety and proficiency as measured by different assessments.

Multiple attribute group decision-making (MAGDM) issues may involve quantitative and qualitative attributes. In inconsistent and indeterminate decision-making issues, current assessment information of quantitative and qualitative attributes with respect to alternatives only contains either numerical neutrosophic values or linguistic neutrosophic values as the single information expression. However, existing neutrosophic techniques cannot perform the mixed information denotation and aggregation operations of numerical neutrosophic values and linguistic neutrosophic values in neutrosophic decision-making issues. To solve the puzzles, this article presents the information denotation, aggregation operations, and MAGDM models of single-valued neutrosophic and linguistic neutrosophic hybrid sets/elements (SVNLNHSs/SVNLHEs) as new techniques to perform MAGDM issues with quantitative and qualitative attributes in the environment of SVNLNHEs. In this study, we first propose a SVNLNHS/SVNLNHE notion that consists of a single-valued neutrosophic element (SVNE) for the quantitative argument and a linguistic neutrosophic element (LNE) for the qualitative argument. According to a linguistic and neutrosophic conversion function and its inverse conversion function, we present some basic operations of single-valued neutrosophic elements and linguistic neutrosophic elements, the SVNLNHE weighted arithmetic mean (SVNLNHEWAMN) and SVNLNHE weighted geometric mean (SVNLNHEWGMN) operators (forming SVNEs), and the SVNLNHEWAML and SVNLNHEWGML operators (forming LNEs). Next, MAGDM models are established based on the SVNLNHEWAMN and SVNLNHEWGMN operators or the SVNLNHEWAML and SVNLNHEWGML operators to realize MAGDM issues with single-valued neutrosophic and linguistic neutrosophic hybrid information, and then their applicability and availability are indicated through an illustrative example in the SVNLNHE circumstance. By comparison with the existing techniques, our new techniques reveal obvious advantages in the mixed information denotation, aggregation algorithms, and decision-making methods in handling MAGDM issues with the quantitative and qualitative attributes in the setting of SVNLNHSs.

Traffic vehicle behavior prediction is a necessary prerequisite for intelligent vehicle behavior decision and trajectory planning. The behaviors of vehicles are deeply interactive. In order to reasonably predict the future behavior of traffic vehicles, based on the Game theory, this paper designs the behavior prediction framework of traffic vehicles, and establishes the GMM(Gaussian Mixture Model)-HMM(Hidden Markov Model) behavior recognition model. Then, the revenue function is designed to model the driver's intent by calculating the vehicle's front running space, collision risk and comfort loss under each scenario. And the NGSIM dataset is used to train the parameters in the GMM-HMM model and those in the revenue function. Finally, two groups of experiments are designed to compare this method with the traditional method. The experimental results show that the proposed method can predict the future behavior of traffic vehicles earlier, and can also well reflect the interaction process of vehicle behavior, and has better robustness.

Visual simultaneous localization and mapping (VSLAM) plays a vital role in the field of positioning and navigation. At the heart of VSLAM is visual odometry (VO), which uses continuous images to estimate the camera’s ego-motion. However, due to many assumptions of the classical VO system, robots can hardly operate in challenging environments. To solve this challenge, we combine the multiview geometry constraints of the classical stereo VO system with the robustness of deep learning to present an unsupervised pose correction network for the classical stereo VO system. The pose correction network regresses a pose correction that results in positioning error due to violation of modeling assumptions to make the classical stereo VO positioning more accurate. The pose correction network does not rely on the dataset with ground truth poses for training. The pose correction network also simultaneously generates a depth map and an explainability mask. Extensive experiments on the KITTI dataset show the pose correction network can significantly improve the positioning accuracy of the classical stereo VO system. Notably, the corrected classical stereo VO system’s average absolute trajectory error, average translational relative pose error, and average translational root-mean-square drift on a length of 100–800 m in the KITTI dataset is 13.77 cm, 0.038 m, and 1.08%, respectively. Therefore, the improved stereo VO system has almost reached the state of the art.

In an autonomous vehicle, the lane following algorithm is an important component, which is a basic function of autonomous driving. However, the existing lane following system has a few shortcomings: first, the control method it adopts requires an accurate system model, and different vehicles have different parameters, which needs a lot of parameter calibration work. The second is that it may fail on road sections where the lateral acceleration requirements of vehicles are large, such as large curves. Third, its decision-making system is defined based on rules, which has disadvantages: it is difficult to formulate; human subjective factors cannot guarantee objectivity; coverage is difficult to guarantee. In recent years, the deep deterministic policy gradient (DDPG) algorithm has been widely used in the field of autonomous driving due to its strong nonlinear fitting ability and generalization performance. However, the DDPG algorithm has overestimated state action values and large cumulative errors, low training efficiency and other issues. Therefore, this paper improves the DDPG algorithm based on the double critic networks and priority experience replay mechanism. Then this paper proposes a lane following method based on this algorithm. Experiment shows that the algorithm can achieve excellent following results under various road conditions.

In this paper, a simple and efficient rule based energy management system for battery and supercapacitor hybrid energy storage system (HESS) used in electric vehicles is presented. The objective of the proposed energy management system is to focus on exploiting the supercapacitor characteristics and on increasing the battery lifetime and system efficiency. The role of the energy management system is to yield battery reference current, which is subsequently used by the controller of the DC/DC converter. First, a current controller is designed to realize load current distribution between battery and supercapacitor. Then a voltage controller is designed to ensure the supercapacitor SOC to fluctuate within a preset reasonable variation range. Finally, a commercial experimental platform is developed to verify the proposed control strategy. In addition, the energy efficiency and the cost analysis of the hybrid system are carried out based on the experimental results to explore the most cost-effective tradeoff.