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The development of large language models (LLMs) has promoted a transformation of human–computer interaction (HCI) models and has attracted the attention of scholars to the evaluation of personality traits of LLMs. As an important interface for the HCI and human–machine interface (HMI) in the future, the intelligent cockpit has become one of LLM’s most important application scenarios. When in-vehicle intelligent systems based on in-vehicle LLMs begin to become human assistants or even partners, it has become important to study the “personality” of in-vehicle LLMs. Referring to the relevant research on personality traits of LLMs, this study selected the psychological scales Big Five Inventory-2 (BFI-2), Myers–Briggs Type Indicator (MBTI), and Short Dark Triad (SD-3) to establish a personality traits evaluation framework for in-vehicle LLMs. Then, we used this framework to evaluate the personality of three in-vehicle LLMs. The results showed that psychological scales can be used to measure the personality traits of in-vehicle LLMs. In-vehicle LLMs showed commonalities in extroversion, agreeableness, conscientiousness, and action patterns, yet differences in openness, perception, decision-making, information acquisition methods, and psychopathy. According to the results, we established anthropomorphic personality personas of different in-vehicle LLMs. This study represents a novel attempt to evaluate the personalities of in-vehicle LLMs. The experimental results deepen our understanding of in-vehicle LLMs and contribute to the further exploration of personalized fine-tuning of in-vehicle LLMs and the improvement in the user experience of the automobile in the future.

Time Series Analysis has been given a great amount of study in which many useful tests were developed. The phenomenal work of Engle and Granger in 1987 and Johansen in 1988 has paved the way for the most commonly used cointegration tests so far. Even though cointegrating relationships focus on long-term behaviour and correlation of multiple nonstationary time series, oftentimes we encounter statistical data with limited sample sizes and other information. Thus other tests with empirical advantages may also be of considerable importance. In this paper, we provide an optimisation approach motivated by the Blind Source Separation, or also known as Independent Component Analysis, for cointegration between financial time series. Two methods for cointegration tests are introduced, namely decorrelation for the bivariate case and maximisation of nongaussianity for higher-dimensions. We highlight the empirical preponderances of independent components and also the computational simplicity, compared to common practices of cointegration such as the Johansen's Cointegration Test. The advantages of our methods, especially the better performances in limited sample size, enable a wider range of application and accessibility for researchers and practitioners to identify cointegrating relationships.

In this paper, we provide an optimisation approach motivated by the Blind Source Separation, or also known as Independent Component Analysis, for cointegration between financial time series. Two methods for cointegration tests are introduced, namely decorrelation for the bivariate case and maximisation of nongaussianity for higher-dimensions. The advantages of our methods, especially the better performances in limited sample size, enable a wider range of application and accessibility for researchers and practitioners to identify cointegrating relationships.

With the development of end-to-end control based on deep learning, it is important to study new system modeling techniques to realize dynamics modeling with high-dimensional inputs. In this paper, a novel Koopman-based deep convolutional network, called CKNet, is proposed to identify latent dynamics from raw pixels. CKNet learns an encoder and decoder to play the role of the Koopman eigenfunctions and modes, respectively. The Koopman eigenvalues can be approximated by eigenvalues of the learned state transition matrix. The deterministic convolutional Koopman network (DCKNet) and the variational convolutional Koopman network (VCKNet) are proposed to span some subspace for approximating the Koopman operator respectively. Because CKNet is trained under the constraints of the Koopman theory, the identified latent dynamics is in a linear form and has good interpretability. Besides, the state transition and control matrices are trained as trainable tensors so that the identified dynamics is also time-invariant. We also design an auxiliary weight term for reducing multi-step linearity and prediction losses. Experiments were conducted on two offline trained and four online trained nonlinear forced dynamical systems with continuous action spaces in Gym and Mujoco environment respectively, and the results show that identified dynamics are adequate for approximating the latent dynamics and generating clear images. Especially for offline trained cases, this work confirms CKNet from a novel perspective that we visualize the evolutionary processes of the latent states and the Koopman eigenfunctions with DCKNet and VCKNet separately to each task based on the same episode and results demonstrate that different approaches learn similar features in shapes.

SO2 is an important gas in atmosphere with great environmental and climate implications. SO2 emission in China has been receiving great attention as the economy grows and the amount of coal consumption has increased in the past few decades. SO2 has been observed from July 2005 to June 2010 at Linan WMO GAW regional station （30.3 °N, 119.73 °E, 138 m a.s.l.） located in the Yangtze Delta region in eastern China. These observation data are analyzed to understand the trend of regional SO2 background concentration. Strict quality controls are conducted to ensure the temporal comparability of the data. Significant downward trend with -2.4 ppb/yr （P 〈 0.0001） of surface SO2 is observed from 2005 to 2010, especially after 2008. The average concentration of SO2 from July 2005 to June 2008 is 14.2±3.1 ppb, which is slightly higher than the mean values of 13.5±5.1 ppb during 1999-2000 and is two folds of the average value （7.1±3.1 ppb） from July 2008 to June 2010. More than 50% of the SO2has been cut down after 2008 in the Yangtze Delta region due to the implementation of stricter emission control measures. The peak SO2 concentration appears around 10 o＇clock in the morning after 2009 while appears at night before 2009. These diurnal variations of SO2 might indicate that after 2009, more SO2 is from the vertical exchange process than from the local accumulation.

Scaling laws characterize diverse complex systems in a broad range of fields, including physics, biology, finance, and social science. The human language is another example of a complex system of words organization. Studies on written texts have shown that scaling laws characterize the occurrence frequency of words, words rank, and the growth of distinct words with increasing text length. However, these studies have mainly concentrated on the western linguistic systems, and the laws that govern the lexical organization, structure and dynamics of the Chinese language remain not well understood. Here we study a database of Chinese and English language books. We report that three distinct scaling laws characterize words organization in the Chinese language. We find that these scaling laws have different exponents and crossover behaviors compared to English texts, indicating different words organization and dynamics of words in the process of text growth. We propose a stochastic feedback model of words organization and text growth, which successfully accounts for the empirically observed scaling laws with their corresponding scaling exponents and characteristic crossover regimes. Further, by varying key model parameters, we reproduce differences in the organization and scaling laws of words between the Chinese and English language. We also identify functional relationships between model parameters and the empirically observed scaling exponents, thus providing new insights into the words organization and growth dynamics in the Chinese and English language.

Breeding of stress-tolerant plants is able to improve crop yield under stress conditions, whereas CRISPR/Cas9 genome editing has been shown to be an efficient way for molecular breeding to improve agronomic traits including stress tolerance in crops. However, genes can be targeted for genome editing to enhance crop abiotic stress tolerance remained largely unidentified. We have previously identified abscisic acid (ABA)-induced transcription repressors ( AITRs ) as a novel family of transcription factors that are involved in the regulation of ABA signaling, and we found that knockout of the entire family of AITR genes in Arabidopsis enhanced drought and salinity tolerance without fitness costs. Considering that AITRs are conserved in angiosperms, AITRs in crops may be targeted for genome editing to improve abiotic stress tolerance. We report here that mutation of GmAITR genes by CRISPR/Cas9 genome editing leads to enhanced salinity tolerance in soybean. By using quantitative RT-PCR analysis, we found that the expression levels of GmAITRs were increased in response to ABA and salt treatments. Transfection assays in soybean protoplasts show that GmAITRs are nucleus proteins, and have transcriptional repression activities. By using CRISPR/Cas9 to target the six GmAITRs simultaneously, we successfully generated Cas9-free gmaitr36 double and gmaitr23456 quintuple mutants. We found that ABA sensitivity in these mutants was increased. Consistent with this, ABA responses of some ABA signaling key regulator genes in the gmaitr mutants were altered. In both seed germination and seedling growth assays, the gmaitr mutants showed enhanced salt tolerance. Most importantly, enhanced salinity tolerance in the mutant plants was also observed in the field experiments. These results suggest that mutation of GmAITR genes by CRISPR/Cas9 is an efficient way to improve salinity tolerance in soybean.

Background: The Xiao Zhong Zhi Tong Patch (XZZTP) has been extensively utilized in China to alleviate many diseases associated with bacterial inflammation. However, its pharmacological mechanism and active components remain unclear. Methods: The anti-inflammatory effects of XZZTP were evaluated in vivo and in vitro models. The characterization of XZZTP and its transdermal components was performed using LC-MS/MS. The underlying pharmacological mechanism was predicted through network pharmacology using the identified transdermal components and verified by Western blotting. Molecular docking and molecular dynamics simulations were performed on key targets to screen active components. Results: XZZTP showed a swelling inhibition rate of 45.96% in xylene-induced ear edema mice in vivo. In vitro, the inflammatory mediators NO, TNF-α, and PGE2 were concentration-dependently reduced by XZZTP in the LPS-induced RAW 264.7 macrophages model, with inhibition rates of 56.53%, 53.75%, and 48.49% at 200 µg/mL, respectively. LC-MS/MS identified 126 chemical components (97 newly reported) in XZZTP, including 52 transdermal potential active components, among which a new iridoid and its isomer were reported for the first time. Network pharmacology analysis demonstrated that XZZTP mainly downregulated the PI3K/AKT/HIF-1 signaling pathway to alleviate bacterial inflammation. The protein expression of core targets p-PI3K, p-AKT, and HIF-1α in the LPS-induced RAW 264.7 macrophages was significantly reduced after XZZTP intervention. Eight active components were screened via molecular docking, and molecular dynamics simulations of three representative complexes validated stable binding interactions, supporting their therapeutic potential. Conclusions: These findings provide a theoretical basis for XZZTP as a potential agent to ameliorate bacterial inflammation-related diseases, serving as a reference for its further application.

Nation-wide hepatitis B vaccination coverage among healthcare workers (HCWs) is not well researched in China. This study aims to investigate the self-reported hepatitis B vaccination status among HCWs in China. We conducted a cross-sectional survey of health_care workers' vaccination statuses in 120 hospitals in China by collecting demographic and vaccination data. Univariate and multivariate logistic regression analysis were used to assess factors associated with hepatitis B vaccination coverage. Eighty-six percent (2,666/3,104) of respondents reported having received at least one dose of the hepatitis B vaccination and 60% (1,853/3,104) reported having completed ≥3 doses of the hepatitis B vaccination. Factors associated with completing ≥3 doses of the hepatitis B vaccination included workplaces offering free hepatitis B vaccination with vaccination management, age, medical occupation, hospital level, acceptable hepatitis B knowledge and having received training on hepatitis B. HCWs in workplaces offering a free hepatitis B vaccine with vaccination management were 1.4 times more likely (OR = 1.4, 95% CI: 1.1-1.8) to complete their hepatitis B vaccination compared to HCWs in workplaces that did not offer a free hepatitis B vaccine. Either the possession of acceptable hepatitis B knowledge or an age of 30-39 years increased the odds of complete hepatitis B vaccination by 1.3-fold (95% CIs: 1.1-1.5 and 1.1-1.7, respectively) over their referent category. The receipt of training on hepatitis B was also associated with a higher percentage of completing the hepatitis B vaccination (OR = 1.5, 95% CI: 1.2-1.8). The main self-reported reason for incomplete hepatitis B vaccination was \"forgot to complete follow-up doses\" among 43% (234/547) of respondents. Among those who never received any hepatitis B vaccination, only 30% (131/438) intended to be vaccinated. Obtaining immunity from work (40%) and hospitals that did not provide hepatitis B vaccination activities (40%) were the top reasons mentioned for refusing hepatitis B vaccination. The complete hepatitis B vaccination rate among HCWs in China is low, and the desire of HCWs for vaccination is indifferent; therefore, education campaigns are needed. In addition, a free national hepatitis B vaccination policy for HCWs that includes vaccination management should be prioritized to improve hepatitis B coverage among HCWs who are at-risk for HBV infection.

Background. Inconsistent data have been reported for the effectiveness of intramuscular botulinum toxin type A (BTXA) in patients with limb spasticity after stroke. This meta-analysis of available randomized controlled trials (RCTs) aimed to determine the efficacy and safety of BTXA in adult patients with upper and lower limb spasticity after stroke. Methods. An electronic search was performed to select eligible RCTs in PubMed, Embase, and the Cochrane library through December 2018. Summary standard mean differences (SMDs) and relative risk (RR) values with corresponding 95% confidence intervals (CIs) were employed to assess effectiveness and safety outcomes, respectively. Results. Twenty-seven RCTs involving a total of 2,793 patients met the inclusion criteria, including 16 and 9 trials assessing upper and lower limb spasticity cases, respectively. For upper limb spasticity, BTXA therapy significantly improved the levels of muscle tone (SMD=-0.76; 95% CI -0.97 to -0.55; P<0.001), physician global assessment (SMD=0.51; 95% CI 0.35-0.67; P<0.001), and disability assessment scale (SMD=-0.30; 95% CI -0.40 to -0.20; P<0.001), with no significant effects on active upper limb function (SMD=0.49; 95% CI -0.08 to 1.07; P=0.093) and adverse events (RR=1.18; 95% CI 0.72-1.93; P=0.509). For lower limb spasticity, BTXA therapy was associated with higher Fugl-Meyer score (SMD=5.09; 95%CI 2.16-8.01; P=0.001), but had no significant effects on muscle tone (SMD=-0.12; 95% CI -0.83 to 0.59; P=0.736), gait speed (SMD=0.06; 95% CI -0.02 to 0.15; P=0.116), and adverse events (RR=1.01; 95% CI 0.71-1.45; P=0.949). Conclusions. BTXA improves muscle tone, physician global assessment, and disability assessment scale in upper limb spasticity and increases the Fugl-Meyer score in lower limb spasticity.