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Abstract Oxide-ion conducting materials are gaining considerable attention in various applications ranging from oxide fuel cells to oxygen permeation membranes. The oxide ion migration mechanisms are the basis for designing oxide-ion conducting materials. Here, enlightened by proton diffusion in hydrogen-bond networks, we report the coordination polyhedra cooperative mechanism with similar Grotthuss process of oxide ion migration in tellurites. Bi2Te2O7 and Bi2Te4O11 were selected due to their abundance of secondary bonds similar to hydrogen bonds and show high oxide ionic conductivity as mixed electronic and ionic conductors. Neutron total scattering experiments with reverse Monte Carlo simulations indicated that the oxide ion migration in those two tellurites is a synergetic effect of mutual transition between Te-O secondary bonds and covalent bonds assisted by Te-O polyhedra rotation. This detailed investigation of the cooperative mechanism with similar Grotthuss process at the atomic scale provides a direction for optimization and discovering oxide ion conducting materials.

Autonomous vehicles need to continuously analyse the driving context and establish a comprehensive understanding of the dynamic traffic environment. To ensure the safety and efficiency of their operations, it would be beneficial to have accurate predictions of surrounding vehicles’ future trajectories. AVs can adjust their motions proactively to improve road safety and comfort with such information. This paper proposes a novel approach to predict the future trajectories of interacting vehicles, through a model of potential spatial-temporal interactions. A unique kernel function that emphasises risk-awareness was developed to extract spatial dependencies. The established model was trained and evaluated with the publicly available Highway Drone Dataset and Intersection Drone Dataset. The performance of the developed model was assessed with eight state-of-the-art methods. An ablation study and safety analysis were also conducted to evaluate the proposed risk-awareness kernel function. Results show that the proposed model’s inference speed is over eight times faster than the commonly used LSTM-based models. It also achieves an improvement of over 8% in prediction accuracy when compared with the state-of-the-art model.

The depositional facies types of the fourth member of the Middle Triassic Leokoupo Formation (Lei-4 Member) in western Sichuan Basin are examined through the methods of sedimentology, lithology and analysis of well-logging data, as well as the special lithofacies indicators such as microbialite, gypsum-salt rock and tempestites, using the data of about 400 wells and 11 outcrop sections. The distribution and evolution and its hydrocarbon geological significances of the bay facies have been discussed. The Lei-4 Member in western Sichuan Basin has an ocean–bay–flat depositional model, with the presence of evaporated tidal flat, restricted tidal flat and paleo-bay facies from east to west. The subfacies such as bay margin, subtidal bay and bay slope are recognized within the paleo-bay, with microbial reef and grain bank microfacies in the bay margin, microbial flat, deep-water spongy reef and hydrostatic mudstone microfacies in the subtidal bay, and tempestites and collapsed deposits in the upper bay slope. The bay boundary covered the Guangyuan-Zitong-Dujiangyan area in the period of the first submember of the Lei-4 Member (Lei-4-1) with falling sea level, regressed westward into the Shangsi-Jiangyou-Dujiangyan area in the period of Lei-4-2, and expanded to the Shangsi-Zitong-Langzhong- Wusheng-Yanting-Chengdu area in the northern part of central Sichuan Basin in the period of Lei-4-3 along with a small-scale transgression. The topographic pattern of “one high and two lows” is confirmed in the Lei-4 Member, corresponding to a configuration of source rocks and reservoir rocks alternated horizontally and superimposed vertically. Two efficient source-reservoir configuration models, i.e. side source & side reservoir, and self-generating & self-storing, are available with the microbial reef and grain bank reservoirs at the bay margin and the high-quality source rocks within the sags on both sides of the bay. The research findings will inevitably open up a new situation for the hydrocarbon exploration in the Leikoupo Formation.

Abstract In order to explore more effective methods of landslide disaster monitoring and controlling, NPR anchor cable and optical fiber grating strain sensor are physically combined to form a slope reinforcement-monitoring integration system, with PVC pipe an intermediary. Physical model test is carried out according to timely warning of landslide disasters of the Newtonian force monitoring system. At the same time, the optical fiber sensing technology has the condition of continuous perception of time and space. The feasibility of monitoring the whole life cycle of slope instability and positioning the potential sliding surface is discussed. Through the analysis of the test results, it is concluded that the fiber grating strain sensor can effectively monitor the deep displacement of the slope. PVC pipe and the soil have deformation coordination, which is able to effectively reinforce the slope body together with the combined anchor cable sensing device, and can continuously sense the potential sliding surface in the whole life cycle of the slope. By comparing the results of Newton force monitoring curve with the fiber grating strain monitoring, the mechanical law of “Newton force sudden drops, immediately catastrophe happens” is further verified. The feasibility of the combined sensing and early warning method of optical fiber sensing technology and Newton force monitoring is verified. This method realizes the further optimization of Newton force monitoring system.

IntroductionThe Ecological Systems Model of Creativity Development (ESMCD) proposes that parental involvement positively impacts student creativity. However, prior empirical studies present mixed results, including positive, negative, and no correlations between these variables.MethodsTo synthesize these inconsistent primary studies, the current study conducted a systematic meta-analysis synthesizing 30 primary studies involving 37 independent samples with 70 effect sizes and a total N = 20,906 participants.ResultsThe results demonstrated: (1) an overall significant small, positive correlation (r = 0.101) between parental involvement and student creativity; (2) significant small, positive correlations between specific involvement types (autonomy support r = 0.144; behavioral control r = 0.133; content support r = 0.131) and creativity, alongside a significant small, negative correlation between psychological control and creativity (r = −0.117); (3) no statistically significant moderating effects of student grade level, parental gender, region, or publication type.DiscussionThis systematic meta-analytic review consolidates empirical evidence indicating that parental involvement positively predicts students’ creativity, while highlighting the detrimental impact of psychological control on creative outcomes. Further research elucidating the mechanisms underlying these relations is critical for informing parenting approaches and education policies seeking to foster creativity development among students.

To address the issue of non-standard grain sizes and difficult control of uniformity in the manufacturing of large, complex-shaped nuclear power pipelines. In this paper, double wedge-shaped samples of X2 CrNiMo 18.12 (CN) austenitic stainless steel were hot deformed at 1000 °C, 1100 °C, and 1180 °C to obtain deformation samples with a gradient strain, and the deformation sample at 1180 °C was subjected to solid solution treatment at 1060 °C × 4 h (water quenching; the measured cooling rate of water cooling was approximately 4400 °C/min). Large-area electron backscattered diffraction mapping technology was used to characterize the 80 mm × 5 mm full-area range of samples. The initiation sequence of various dynamic recrystallization mechanisms and the grain evolution under continuous effective strain conditions at a hot deformation temperature of 1000–1180 °C were investigated. The influence of the hot deformation process parameters on the grains after solid solution treatment was examined, the process interval for the generation of severe mixed grains was determined. The results show that the recrystallization behavior of X2 CrNiMo 18.12 (CN) austenitic stainless steel is mainly determined by a combination of recrystallization mechanisms dominated by discontinuous dynamic recrystallization, supplemented by continuous dynamic recrystallization, twinning-induced dynamic recrystallization, and geometrical dynamic recrystallization during the hot deformation process at 1000–1180 °C. The effective strain intervals of the solid solution sample that produces severely mixed grains are in the range of 0.11–0.22 mm/mm, with the probability of producing severely mixed grains with an effective strain in the range of 0.13–0.14 mm/mm reaching 74%.

Global Navigation Satellite Systems (GNSS) support numerous applications, including mission-critical ones that require a high level of integrity for safe operations, such as air, maritime, and land-based navigation. Fault Detection and Exclusion (FDE) is crucial for mission-critical applications, as faulty measurements significantly impact system integrity. FDE can be applied within the positioning algorithm in the measurement’s domain and the integrity monitoring domain. Previous research has utilized a limited number of Machine Learning (ML) models and Quality Indicators (QIs) for the FDE process in the measurement domain. It has not evaluated the pseudorange measurement fault thresholds that need to be detected. In addition, ML models were mainly evaluated based on accuracy, which alone does not provide a comprehensive evaluation. This paper introduces a comprehensive framework for traditional ML-based FDE prediction models in the measurement domain for pseudorange in complex environments. For the first time, this study evaluates the fault detection thresholds across 40 values, ranging from 1 to 40 m, using six ML models for FDE. These models include Decision Tree, K-Nearest Neighbors (KNN), Discriminant, Logistic, Neural Network, and Trees (Boosted, Bagged, and Rusboosted). The models are comprehensively assessed based on four key aspects: accuracy, probability of misdetection, probability of fault detection, and the percentage of excluded data. The results show that ML models can provide a high level of performance in the FDE process, exceeding 95% accuracy when the fault threshold is equal to or greater than 4 m, with KNN providing the highest FDE performance.

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

The principle of system resilience is its ability to withstand disruptions and maintain an equilibrium state. In urban network systems, adaptive traffic signal control (ATSC) has been an effective countermeasure to mitigate traffic flow disturbance and improve resilience. This research has explored the usage of a decentralised advantage actor-critic (a2c) algorithm-based ATSC in mitigating disruptions, particularly nonrecurring congestion caused by car accidents. A reward function has also been proposed, combining deduced resilience metric, safety indicator time to collision (TTC) and system performance. A virtual simulation environment was created using simulation of urban mobility (SUMO) to facilitate the evaluation of the proposed approach. In the grid simulation environment, an overall 5.8% improvement is achieved, exceeding benchmark algorithms in three metrics, especially performance with a margin of over 5.2%. Robustness against different levels of car accidents are proven as well. Further evaluation is also implemented based on a real-world case study and demonstrates an improvement of 20.08%, highlighting the correlation of proposed method’s efficiency on the traffic flow rate and road structure.

Abstract None-Line-of-Sight (NLOS) signals denote Global Navigation Satellite System (GNSS) signals received indirectly from satellites and could result in unacceptable positioning errors. To meet the high mission-critical transportation and logistics demand, NLOS signals received in the built environment should be detected, corrected, and excluded. This paper proposes a cost-effective NLOS impact mitigation approach using only GNSS receivers. By exploiting more signal Quality Indicators (QIs), such as the standard deviation of pseudorange, Carrier-to-Noise Ratio (C/N0), elevation and azimuth angle, this paper compares machine-learning-based classification algorithms to detect and exclude NLOS signals in the pre-processing step. The probability of the presence of NLOS is predicted using regression algorithms. With a pre-defined threshold, the signals can be classified as Line-of-Sight (LOS) or NLOS. The probability of the occurrence of NLOS is also used for signal subset selection and specification of a novel weighting scheme. The novel weighting scheme consists of both C/N0 and elevation angle and NLOS probability. Experimental results show that the best LOS/NLOS classification algorithm is the random forest. The best QI set for NLOS classification is the first three QIs mentioned above and the difference of azimuth angle. The classification accuracy obtained from this proposed algorithm can reach 93.430%, with 2.810% false positives. The proposed signal classifier and weighting scheme improved the positioning accuracy by 69.000% and 40.700% in the horizontal direction, 79.361% and 75.322% in the vertical direction, and 75.963% and 67.824% in the 3D direction.

With the development of smart cities and smart electric vehicles (EVs), the problem of improving the performance of Vehicular Ad-hoc Networks (VANETs) is gradually being emphasized. To improve the network performance of VANETs, some scholars have considered parked vehicles as roadside units, but have not paid attention to the energy consumption characteristics of vehicles, especially electric vehicles. Therefore, in this paper, we propose a QL-mRSU series artificial intelligence energy saving method to optimize the energy consumption of parked electric vehicles during communication. The method is based on electric vehicle self-organizing networks (E-VANETs), which dynamically cluster electric vehicles parked in parking lots by parameters such as traffic flow, number of service demands, and charging index in reinforcement learning, select the most suitable vehicles as mobile roadside units (mRSUs), and adjust the working mode according to environmental changes such as the number of service demands to achieve the effects of self-learning and energy saving. The simulation experimental results show that compared with other energy-based routing algorithms, the method is able to make optimal choices through self-learning with guaranteed communication quality and is more adaptable to traffic flow changes on the road, thus ensuring the stability of energy-saving efficiency. In addition, the method significantly improves the energy structure of electric vehicle parking clusters.

The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety.

With the rapid development of China’s economy and the increasing demand for resources in various regions, the speed and volume of railway operations are increasing, and diseases such as mud pumping and mud pumping in railway tunnels are becoming more and more serious. Although various construction units have taken diverse measures to rectify the diseases, they often have poor results, repeated rectification, and repeated failure, which consumes a lot of financial and material resources but cannot cure the mud pumping disease, allowing the disease to seriously endanger the driving safety of the line. This paper is based on the long-established loess tunnel base mud pumping disease treatment project in the Lanzhou Railway Bureau of the China Railway. Firstly, the influence of train load, structural fracture, and tunnel drainage systems on the mechanism of disease is expounded. Then, the key technologies and processes of the commonly used disease treatment schemes are sorted out and summarized, and the advantages and disadvantages of the commonly used schemes and the additional drainage tunnel and self-priming pump technology proposed in this paper are compared in detail. The analysis shows that the universally used treatment technology applies to the treatment of local mud pumping disease in the tunnel, and there are shortcomings in these measures for the long-established tunnel. The drainage tunnel and self-priming pump technology can reduce and maintain the small content of groundwater in the surrounding rock of the tunnel basement and can resolve the mud pumping disease of the loess tunnel basement from the root. At present, this technology has been applied to many tunnel projects and has achieved certain comprehensive benefits, which could provide a reference for similar projects.

The unique topology-driven versatility of natural biological systems has motivated the material science research community to design and synthesize architected cellular materials for different engineering disciplines. However, the lattice cell design of architected cellular materials is highly arbitrary, making the design of architected cellular materials very difficult. In order to overcome these problems, an innovative triple period minimal surface (TPMS) lattice type distribution algorithm based on the maximum strain energy principle is proposed in this paper to optimize the stiffness of the structure. The algorithm establishes the mapping relationship between the relative density of TPMS lattice cells and the surface bias parameter t by generating a voxel model, obtains the equivalent mechanical properties of lattice cells as a function of relative density by homogenization algorithm and function fitting, establishes a TPMS lattice database, and innovatively distributes the TPMS lattice types by selecting the maximum strain energy lattice cells on the basis of the topology optimization results. The experimental results show that the stiffness of the multi-TPMS lattice structure is improved by 55.89% and 30.15%, respectively, compared with the two single lattice structures.

We report on a dynamic range enhanced optical frequency domain reflectometry distributed backscattering interrogator based on dual-loop composite optical phase-locked loop (OPLL). Exploiting simultaneously an acousto-optic frequency shifter based an external modulation loop and a piezo based direct modulation loop, the proposed composite OPLL allows offering a larger loop bandwidth and gain, permitting a more efficient coherence enhancement as well as sweep linearization. A high fidelity frequency sweep of ${\rm{\sim}} \text{8.2 GHz}$ at ${\text{164 GHz/s}}$ sweep rate is generated with a peak-to-peak frequency error as low as ${\rm{\sim}} \text{120 kHz}$. It leads to a dynamic range enhancement of more than ${\text{3 dB}}$ for the measured power loss compared to the case when only piezo loop is applied. This corresponds to ${\rm{\sim}} \text{15 km}$ extension for the measurement range of Rayleigh backscattering without any spatial resolution penalties. Fourier transform-limited spatial resolution has been demonstrated at a range window more than about $\text{28}$ times of the intrinsic coherence length of the adopted fiber laser. The proposed method provides a straightforward optimization of the real-time sweep control and is expected to be a useful tool in industrial and commercial applications.

Understanding the effects of laser phase and frequency noise on laser interferometry is significant for evaluating the system performance. To precisely study the performance limit caused by laser frequency noise, here we propose and demonstrate a versatile model based on the Fourier and inverse Fourier transform (FIFT) method. This model, capable of estimating the beat note spectra of different delayed self-interferometry (DSI) with laser sources of arbitrary frequency noise properties, allows for accurate evaluations of the noise performance in a variety of interferometry based systems. Such a model has been experimentally validated using lasers with irregular frequency noise properties such as cavity stabilized fiber laser or laser under optical phase-locking, providing more detailed insight into the evolution of the frequency noise dynamics at different interferometric conditions. With average estimation goodness (AEG) of 0.9716 and computation complexity of O(NlogN), this model offers greater accuracy and lower complexity than conventional methods. It has also been confirmed that this model permits to distinguish the contributions from the laser frequency stability and other noise sources, which could be helpful for the noise analysis and performance optimization of the system.

All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

Rheumatoid arthritis (RA) is an autoimmune disease mainly characterized by chronic polyarthritis. Many types of cells play pivotal roles in the pathogenicity of RA, such as T cells, B cells, macrophages, dendritic cells (DCs), osteoclasts (OCs), and fibroblast-like synoviocytes (FLS). Tripterygium wilfordii Hook f. (TwHf) and its ingredients are able to control disease activity by regulating the functions of cells mentioned above, and the clinical studies have highlighted the importance of TwHf ingredients in RA treatment. They have been demonstrated to improve the RA symptoms of animal models and patients. In this review, we discussed the effect of TwHf ingredients on pathogenicity cells, including disease/cell phenotypes and molecular mechanisms. Here, we constructed a cell-cell interaction network to visualize the effect of TwHf ingredients. We found that TwHf ingredients could inhibit the differentiation and proliferation of the pathogenicity cells. Besides, the components could decrease the levels of pathogenicity cytokines [i.e., interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α)]. Many signaling pathways are involved in the underlying mechanisms, such as PI3K, NF-κB, and MAPK signaling pathways.

In order to ensure the safety of shore-based hydrogen bunkering operations, this paper takes a 2000-ton bulk hydrogen powered ship as an example. Firstly, the HAZID method is used to identify the hazards of hydrogen bunkering, then the probability of each scenario is analyzed, and then the consequences of scenarios with high risk based on FLACS software is simulated. Finally, the personal risk of bunkering operation is evaluated and the bunkering restriction area is defined. The results show that the personal risk of shore-based bunkering operation of hydrogen powered ship is acceptable, but the following risk control measures should be taken: (1) The bunkering restriction area shall be delineated, and only the necessary operators are allowed to enter the area and control the any form of potential ignition source; (2) The hose is the high risk hazards during bunkering. The design form of bunkering arm and bunkering hose is considered to shorten the length of the hose as far as possible; (3) A safe distance between shore-based hydrogenation station and the building outside the station should be guaranteed. The results have a guiding role in effectively reducing the risk of hydrogen bunkering operation.

The development of information technology has brought great convenience to our lives, but at the same time, the unfairness and privacy issues brought about by traditional centralized systems cannot be ignored. Blockchain is a peer-to-peer and decentralized ledger technology that has the characteristics of transparency, consistency, traceability and fairness, but it reveals private information in some scenarios. Secure multi-party computation (MPC) guarantees enhanced privacy and correctness, so many researchers have been trying to combine secure MPC with blockchain to deal with privacy and trust issues. In this paper, we used homomorphic encryption, secret sharing and zero-knowledge proofs to construct a publicly verifiable secure MPC protocol consisting of two parts—an on-chain computation phase and an off-chain preprocessing phase—and we integrated the protocol as part of the chaincode in Hyperledger Fabric to protect the privacy of transaction data. Experiments showed that our solution performed well on a permissioned blockchain. Most of the time taken to complete the protocol was spent on communication, so the performance has a great deal of room to grow.

The objectives of the present study were to characterize the chemical composition, antioxidant activity and hepatoprotective effect of the polysaccharides from Taishan Pinus massoniana pollen (TPPPS). HPLC analysis showed that TPPPS was an acidic heteropolysaccharide with glucose and arabinose as the main component monosaccharides (79.6%, molar percentage). Fourier transform-infrared spectroscopy (FT-IR) analysis indicated that the spectra of TPPPS displayed infrared absorption peaks characteristic of polysaccharides. In in vitro assays TPPPS exhibited different degrees of dose-dependent antioxidant activities , and this was further verified by suppression of CCl4-induced oxidative stress in the liver with three tested doses of TPPPS (100, 200, and 400 mg/kg bw) in rats. Pretreatment with TPPPS significantly decreased the levels of alanine aminotransferase (AST), aspartate aminotransferase (ALT), alkaline phosphatase (ALP), lactic dehydrogenase (LDH) and malondialdehyde (MDA) against CCl4 injuries, and elevated the activities of superoxide dismutase (SOD) as well as glutathione peroxidase (GSH-Px). Histopathological observation further confirmed that TPPPS could protect the liver tissues from CCl4-induced histological alternation. These results suggest that TPPPS has strong antioxidant activities and significant protective effect against acute hepatotoxicity induced by CCl4. The hepatoprotective effect may partly be related to its free radical scavenging effect, increasing antioxidant activity and inhibiting lipid peroxidation.

Theoretical models have predicted a positive association between anxiety and Internet use disorders. However, the findings of previous studies are conflicting, with some reporting a positive association and others proposing no relationship between the two. To explore the true relationship between the two and analyze the reasons for the differences, 100 primary studies involving 108,539 subjects were entered into a meta-analysis. The results showed that (1) there was a significant positive correlation between students' anxiety and Internet use disorder (r = 0.330); (2) the moderating effect of anxiety type was significant. (3) The moderating effects of the measurement instrument for Internet use disorder, the measurement instrument for anxiety, the subject's grade level, the subject's region, and the type of publication were not significant. The question of how the different anxiety types of students and IUD have different mechanisms of action needs to be further explored. [ABSTRACT FROM AUTHOR]