The developments of connected vehicles are heavily influenced by information and communications technologies, which have fueled a plethora of innovations in various areas, including networking, ...caching, and computing. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem, where the gains of not only networking but also caching and computing are taken into consideration in the proposed framework. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.
Growing research has revealed that interpreters’ individual cognitive differences impact interpreting. In this article, I examined how an interpreter’s language proficiency, working memory, and ...anxiety level impact speech disfluencies in target language delivery. Fifty-three student interpreters took part in three cognitive tests, respectively, of their proficiency in English (their non-native language), working memory, and anxiety level. Then they consecutively interpreted an English speech into Mandarin (their native language); their target language output was coded for different types of disfluencies (pauses, fillers, repetitions, and articulatory disfluency). It was found that anxiety level, but not language proficiency and working memory, impacted the occurrence of disfluencies in general. In particular, more anxious interpreters tended to have more fillers, such as
er
and
um
, and more repetitions of words and phrases. I discuss these findings in terms of how anxiety may impact the cognitive processes of interpreting and how to reduce student interpreters’ anxiety level in interpreting teaching and learning.
The intestinal microbiota is well known to have multiple benefits on human health, including cancer prevention and treatment. The effects are partially mediated by microbiota-produced short chain ...fatty acids (SCFAs) such as butyrate, propionate and acetate. The anti-cancer effect of butyrate has been demonstrated in cancer cell cultures and animal models of cancer. Butyrate, as a signaling molecule, has effects on multiple signaling pathways. The most studied effect is its inhibition on histone deacetylase (HDAC), which leads to alterations of several important oncogenic signaling pathways such as JAK2/STAT3, VEGF. Butyrate can interfere with both mitochondrial apoptotic and extrinsic apoptotic pathways. In addition, butyrate also reduces gut inflammation by promoting T-regulatory cell differentiation with decreased activities of the NF-κB and STAT3 pathways. Through PKC and Wnt pathways, butyrate increases cancer cell differentiation. Furthermore, butyrate regulates oncogenic signaling molecules through microRNAs and methylation. Therefore, butyrate has the potential to be incorporated into cancer prevention and treatment regimens. In this review we summarize recent progress in butyrate research and discuss the future development of butyrate as an anti-cancer agent with emphasis on its effects on oncogenic signaling pathways. The low bioavailability of butyrate is a problem, which precludes clinical application. The disadvantage of butyrate for medicinal applications may be overcome by several approaches including nano-delivery, analogue development and combination use with other anti-cancer agents or phytochemicals.
Background
Informed by the differential susceptibility to media effects model (DSMM), the current study aims to investigate associations of COVID‐19‐related social media use with mental health ...outcomes and to uncover potential mechanisms underlying the links.
Methods
A sample of 512 (62.5% women; Mage = 22.12 years, SD = 2.47) Chinese college students participated in this study from 24 March to 1 April 2020 via online questionnaire. They completed measures of social media use, the COVID‐19 stressor, negative affect, secondary traumatic stress (STS), depression, and anxiety as well as covariates.
Results
As expected, results from regression analyses indicated that a higher level of social media use was associated with worse mental health. More exposure to disaster news via social media was associated with greater depression for participants with high (but not low) levels of the disaster stressor. Moreover, path analysis showed negative affect mediated the relationship of social media use and mental health.
Conclusions
These findings suggest that the disaster stressor may be a risk factor that amplifies the deleterious impact of social media use on depression. In addition, excessive exposure to disaster on social media may trigger negative affect, which may in turn contribute to mental health problems. Future interventions to improve mental health should consider elements of both disaster stressor and negative affect.
COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people's mental health, ...to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.
The ongoing COVID-19 pandemic is likely to enhance the risk of addictive social media use (SMU) as people spend more time online maintaining connectivity when face-to-face communication is limited. ...Stress is assumed to be a critical predictor of addictive SMU. However, the mechanisms underlying the association between stress and addictive SMU in crises like the current COVID-19 situation remain unclear. The present study aimed to understand the relationship between COVID-19 stress and addictive SMU by examining the mediating role of active use and social media flow (i.e., an intensive, enjoyable experience generated by SMU that perpetuates media use behaviors). A sample of 512 Chinese college students (
= 22.12 years,
= 2.47; 62.5% women) provided self-report data on COVID-19 stress and SMU variables (i.e., time, active use, flow, addictive behavior) via an online survey from March 24 to April 1, 2020. The results showed that COVID-19 stress was positively associated with tendencies toward addictive SMU. Path analyses revealed that this relationship was significantly serially mediated by active use and social media flow, with SMU time being controlled. Our findings suggest that individuals who experience more COVID-19 stress are at increased risk of addictive SMU that may be fostered by active use and flow experience. Specific attention should be paid to these high-risk populations and future interventions to reduce addictive SMU could consider targeting factors of both active use and social media flow.
In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the ...electroencephalogram (EEG), which is clinically effective on the brain. There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained. The simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.
In order to improve photocatalytic activity and maximize solar energy use, a new composite material Fe2O3/P2Mo18 was prepared by combining polyoxometalates (P2Mo18) with Fe2O3 nanosheets. FT-IR, XRD, ...XPS, SEM, TEM, UV-vis, EIS, and PL were used to characterize the composite material, and nano-Fe2O3 of different sizes and morphologies with a controllable absorption range was prepared by adjusting the reaction time, and, when combined with P2Mo18, a composite photocatalyst with efficient visible light response and photocatalytic activity was constructed. The EIS, Bode, and PL spectra analysis results show that the Fe2O3/P2Mo18 composite material has outstanding interfacial charge transfer efficiency and potential photocatalytic application possibilities. Model reactions of methylene blue (MB) and Cr (VI) photodegradation were used to evaluate the redox activity of Fe2O3/P2Mo18 composites under simulated visible light. The photocatalytic degradation rate was as high as 98.98% for MB and 96.86% for Cr (VI) when the composite ratio was Fe2O3/P2Mo18-5%. This research opens up a new avenue for the development of high-performance photocatalysts.
Background and Aims
Aristolochic acid (AA) exposure has been statistically associated with human liver cancers. However, direct evidence of AA exposure–induced liver cancer is absent. This study aims ...to establish a direct causal relationship between AA exposure and liver cancers based on a mouse model and then explores the AA‐mediated genomic alterations that could be implicated in human cancers with AA‐associated mutational signature.
Approach and Results
We subjected mice, including phosphatase and tensin homolog (Pten)‐deficient ones, to aristolochic acid I (AAI) alone or a combination of AAI and CCl4. Significantly, AAI exposure induced mouse liver cancers, including hepatocellular carcinoma (HCC) and combined HCC and intrahepatic cholangiocarcinoma, in a dose‐dependent manner. Moreover, AAI exposure also enhanced tumorigenesis in these CCl4‐treated or Pten‐deficient mice. AAI led to DNA damage and AAI‐DNA adduct that could initiate liver cancers through characteristic adenine‐to‐thymine transversions, as indicated by comprehensive genomic analysis, which revealed recurrent mutations in Harvey rat sarcoma virus oncogene. Interestingly, an AA‐associated mutational signature was mainly implicated in human liver cancers, especially from China. Moreover, we detected the AAI‐DNA adduct in 25.8% (16/62) of paratumor liver tissues from randomly selected Chinese patients with HCC. Furthermore, based on phylogenetic analysis, the characteristic mutations were found in the initiating malignant clones in the AA‐implicated mouse and human liver cancers where the mutations of tumor protein p53 and Janus kinase 1 were prone to be significantly enriched in the AA‐affected human tumors.
Conclusions
This study provides evidence for AA‐induced liver cancer with the featured mutational processes during malignant clonal evolution, laying a solid foundation for the prevention and diagnosis of AA‐associated human cancers, especially liver cancers.
In this paper, a voltage balance control strategy based on dual active bridge (DAB) dc/dc converters in a power electronic traction transformer (PETT) is proposed. Based on this strategy, the ...output-parallel DAB converters can be equivalent to an input-series-output-parallel system. Furthermore, a PETT starting control method is put forward, which can effectively avoid risks of overcurrent and overvoltage in the PETT starting process. In order to carry out the controller design and system stability analysis, three different kinds of mathematical models of DAB converters are set up. The first model is related to a single DAB converter, the second model reflects the equivalent relation between an output-parallel DAB system and a single DAB converter in terms of the output-voltage control loop, and the third model indicates that the voltage balance control system based on DAB converters is a multiinput-multioutput system. Due to the nonzero off-diagonal elements of the controlled plant, there is a mutual effect between different control loops, which is defined as "interaction" in the multivariable feedback control theory. The stability of the voltage balance control system is made up of two parts, including the stability of each single-input-single-output (SISO) control loop and the influence of the interaction on the system stability. The research is carried out to measure the intensity of the interaction in this paper, and a criterion directly based on the controlled plant is proposed to predict the influence of the interaction, which can obviously simplify the system stability analysis. Considering the particular traction onboard application, a new control structure toward the voltage balance controller is introduced. Based on the new structure, the controller is designed and the stability of the SISO system is analyzed. Finally, a five-cell PETT prototype with rated power of 30 kW is taken to carry out further research, and the experiment results verify the effectiveness and correctness of the proposed algorithms.
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