Displaying publications 1 - 20 of 202 in total

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  1. Liu H, Liu Y, Zhang R, Wu X
    Front Neurorobot, 2021;15:675827.
    PMID: 34393749 DOI: 10.3389/fnbot.2021.675827
    The study of student behavior analysis in class plays a key role in teaching and educational reforms that can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages, such as poor robustness and low efficiency. From a heterogeneous view perception point of view, it introduces the students' behavior recognition. Therefore, we propose a 3-D multiscale residual dense network from heterogeneous view perception for analysis of student behavior recognition in class. First, the proposed method adopts 3-D multiscale residual dense blocks as the basic module of the network, and the module extracts the hierarchical features of students' behavior through the densely connected convolutional layer. Second, the local dense feature of student behavior is to learn adaptively. Third, the residual connection module is used to improve the training efficiency. Finally, experimental results show that the proposed algorithm has good robustness and transfer learning ability compared with the state-of-the-art behavior recognition algorithms, and it can effectively handle multiple video behavior recognition tasks. The design of an intelligent human behavior recognition algorithm has great practical significance to analyze the learning and teaching of students in the class.
  2. Zhang T, Liu H, Lu Y, Wang Q
    PMID: 36834432 DOI: 10.3390/ijerph20043737
    Physical inactivity has increased globally, particularly in developed nations. A high proportion of the human population is unable to meet the physical activity recommendation of the World Health Organisation due to hypertension, metabolic syndrome, obesity, and other medical conditions. Non-communicable diseases and mental health problems are becoming more prevalent, particularly in low and middle-income nations. This study aimed to determine the effectivenessof a mentorship programmeon university students' mental health and physical fitness. The intervention comprised the effects of sports-based development and education on physical fitness and mental health. A total of 196 and 234 students from two universities were randomly assigned to the intervention and control groups, respectively. The primary outcomes were engagement in physical activities (number of push-ups for 1 min, the strength of hand grip (kg), and the Jump test while standing (cm)), body fat proportion and psychological resilience, self-efficacy, and relationship with family and schoolmates. Participants in the control group had access to a web-based health education game, whereas the intervention group wassubjected to intensive interventional activities for one month based on the eight principles of the National Research Council and Institute of Medicine. Data were analysed using Analysis of Variance (ANOVA) to compare the physical and mental components between the intervention and control groups. Relative to baseline, all the physical health components (push-ups, sit-ups, and jump tests), psychological resilience, relationship with family members, and self-efficacy increased significantly in the intervention compared to the control group. Body fat composition was significantly reduced in the intervention when compared tothe control group. In conclusion, the mentorship programme effectively improved the participants' physical and psychological health and could be developed further for application in a larger population.
  3. Liu H, Zhang X, Liu H, Chong ST
    Int J Public Health, 2023;68:1605322.
    PMID: 36798738 DOI: 10.3389/ijph.2023.1605322
    Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.
  4. Liu H, Zhang X, Liu H, Chong ST
    Int J Public Health, 2023;68:1606127.
    PMID: 37273772 DOI: 10.3389/ijph.2023.1606127
    [This corrects the article DOI: 10.3389/ijph.2023.1605322.].
  5. Guo B, Liu H, Niu L
    Front Neurosci, 2023;17:1266771.
    PMID: 37732304 DOI: 10.3389/fnins.2023.1266771
    INTRODUCTION: Medical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.

    METHODS: In order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.

    RESULTS AND DISCUSSION: We conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data.

  6. Guo B, Liu H, Niu L
    Front Neurorobot, 2023;17:1265936.
    PMID: 38111712 DOI: 10.3389/fnbot.2023.1265936
    Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services.
  7. Mustaffa-Kamal F, Liu H, Pedersen NC, Sparger EE
    BMC Vet Res, 2019 May 22;15(1):165.
    PMID: 31118053 DOI: 10.1186/s12917-019-1909-6
    BACKGROUND: Feline infectious peritonitis (FIP) is considered highly fatal in its naturally occurring form, although up to 36% of cats resist disease after experimental infection, suggesting that cats in nature may also resist development of FIP in the face of infection with FIP virus (FIPV). Previous experimental FIPV infection studies suggested a role for cell-mediated immunity in resistance to development of FIP. This experimental FIPV infection study in specific pathogen free (SPF) kittens describes longitudinal antiviral T cell responses and clinical outcomes ranging from rapid progression, slow progression, and resistance to disease.

    RESULTS: Differences in disease outcome provided an opportunity to investigate the role of T cell immunity to FIP determined by T cell subset proliferation after stimulation with different viral antigens. Reduced total white blood cell (WBC), lymphocyte and T cell counts in blood were observed during primary acute infection for all experimental groups including cats that survived without clinical FIP. Antiviral T cell responses during early primary infection were also similar between cats that developed FIP and cats remaining healthy. Recovery of antiviral T cell responses during the later phase of acute infection was observed in a subset of cats that survived longer or resisted disease compared to cats showing rapid disease progression. More robust T cell responses at terminal time points were observed in lymph nodes compared to blood in cats that developed FIP. Cats that survived primary infection were challenged a second time to pathogenic FIPV and tested for antiviral T cell responses over a four week period. Nine of ten rechallenged cats did not develop FIP or T cell depletion and all cats demonstrated antiviral T cell responses at multiple time points after rechallenge.

    CONCLUSIONS: In summary, definitive adaptive T cell responses predictive of disease outcome were not detected during the early phase of primary FIPV infection. However emergence of antiviral T cell responses after a second exposure to FIPV, implicated cellular immunity in the control of FIPV infection and disease progression. Virus host interactions during very early stages of FIPV infection warrant further investigation to elucidate host resistance to FIP.

  8. Liu H, Liu Y, Dong X, Liu H, Han B
    Front Psychol, 2021;12:755635.
    PMID: 34925159 DOI: 10.3389/fpsyg.2021.755635
    Studies investigating age-related positivity effects during facial emotion processing have yielded contradictory results. The present study aimed to elucidate the mechanisms of cognitive control during attentional processing of emotional faces among older adults. We used go/no-go detection tasks combined with event-related potentials and source localization to examine the effects of response inhibition on age-related positivity effects. Data were obtained from 23 older and 23 younger healthy participants. Behavioral results showed that the discriminability index (d') of older adults on fear trials was significantly greater than that of younger adults [t(44)=2.37, p=0.024, Cohen's d=0.70], whereas an opposite pattern was found in happy trials [t(44)=2.56, p=0.014, Cohen's d=0.75]. The electroencephalography results on the amplitude of the N170 at the left electrode positions showed that the fear-neutral face pairs were larger than the happy-neutral ones for the younger adults [t(22)=2.32, p=0.030, Cohen's d=0.48]; the older group's right hemisphere presented similar tendency, although the results were not statistically significant [t(22)=1.97, p=0.061, Cohen's d=0.41]. Further, the brain activity of the two hemispheres in older adults showed asymmetrical decrement. Our study demonstrated that the age-related "positivity effect" was not observed owing to the depletion of available cognitive resources at the early attentional stage. Moreover, bilateral activation of the two hemispheres may be important signals of normal aging.
  9. Kumar R, Singh L, Wahid ZA, Mahapatra DM, Liu H
    Bioresour Technol, 2018 Apr;254:1-6.
    PMID: 29413909 DOI: 10.1016/j.biortech.2018.01.053
    The aim of this work was to evaluate the comparative performance of hybrid metal oxide nanorods i.e. MnCo2O4 nanorods (MCON) and single metal oxide nanorods i.e. Co3O4 nanorods (CON) as oxygen reduction catalyst in microbial fuel cells (MFC). Compared to the single metal oxide, the hybrid MCON exhibited a higher BET surface area and provided additional positively charged ions, i.e., Co2+/Co3+ and Mn3+/Mn4+ on its surfaces, which increased the electro-conductivity of the cathode and improved the oxygen reduction kinetics significantly, achieved an io of 6.01 A/m2 that was 12.4% higher than CON. Moreover, the porous architecture of MCON facilitated the diffusion of electrolyte, reactants and electrons during the oxygen reduction, suggested by lower diffusion (Rd), activation (Ract) and ohmic resistance (Rohm) values. This enhanced oxygen reduction by MCON boosted the power generation in MFC, achieving a maximum power density of 587 mW/m2 that was ∼29% higher than CON.
  10. Liu H, Khan AR, Aslam S, Rasheed AK, Mohsin M
    PMID: 34705201 DOI: 10.1007/s11356-021-16882-z
    This research examines how financial transformative power sector reforms affect energy efficiency and the economy in a sample of economies from South Asia, the Middle East, and Europe. We applied two stages of OLS, Bayesian VAR, and Data Envelopment Analysis (DEA) methods to a panel data set from 1995 to 2018. According to empirical findings, institutional deficiency has a negative effect on electricity reforms, implying that the greater the impact of reforms on electricity performance, the higher the institutional efficiency, A collection of reform initiatives involving a variety of reform agencies will boost energy efficiency by up to 13% and per capita electricity access by 62%. Despite recent reforms and regulatory measures, the electricity sector continues to face challenges in terms of private investment and structural flaws such as energy inefficiency, significant technological and financial losses, low power quality, and outdated transmission and network infrastructure. Interestingly 13.2% increases can be found in energy efficiency after electricity reforms. Unlike previous studies, our findings reveal a conflict between the broader economic effects and the welfare impact on electricity consumers.
  11. Liu H, Liu H, Li F, Han B, Wang C
    Front Aging Neurosci, 2021;13:644379.
    PMID: 33994995 DOI: 10.3389/fnagi.2021.644379
    Background: Although numerous studies have suggested that the gradually increasing selective preference for positive information over negative information in older adults depends on cognitive control processes, few have reported the characteristics of different attention stages in the emotional processing of older individuals. The present study used a real-time eye-tracking technique to disentangle the attentional engagement and disengagement processes involved in age-related positivity effect (PE). Methods: Eye movement data from a spatial-cueing task were obtained for 32 older and 32 younger healthy participants. The spatial-cueing task with varied cognitive loads appeared to be an effective way to explore the role of cognitive control during the attention engagement and disengagement stages of emotion processing. Results: Compared with younger adults, older participants showed more positive gaze preferences when cognitive resources were sufficient for face processing at the attention engagement stage. However, the age-related PE was not observed at the attention disengagement stage because older adults had more difficulty disengaging from fearful faces than did the younger adults due to the consumption of attention by the explicit target judgment. Conclusion: The present study highlights how cognitive control moderates positive gaze preferences at different attention processing stages. These findings may have far-reaching implications for understanding, preventing, and intervening in unsuccessful aging and, thus, in promoting active and healthy aging.
  12. Liu H, Soh KG, Samsudin S, Rattanakoses W, Qi F
    Front Psychol, 2022;13:1021285.
    PMID: 36275318 DOI: 10.3389/fpsyg.2022.1021285
    BACKGROUND AND AIMS: Among the large number of studies on smartphone addiction, only a few randomized controlled trials on exercise and psychological interventions for smartphone addiction by university students have been published. This study aims to systematically investigate the impact of exercise and psychological interventions on smartphone addiction among university students.

    METHODS: The PRISMA guidelines were adopted for this systematic literature review. Prominent academic databases such as Web of Science, PubMed, ProQuest, Cochrane Library, China National Knowledge Infrastructure (CNKI) and PsycINFO were searched to find eligible studies published before Aug 2021. The overall quality of the articles was checked using the "QualSyst" tool by Kmet et al.

    RESULTS: From among 600 papers, 23 met the inclusion criteria and were incorporated into our systematic review. All of the studies were randomized controlled trials. The following thematic areas emerged as a result of the content analysis: study selection and design, as well as study characteristics (participants, intervention, comparisons, and outcomes).

    DISCUSSION AND CONCLUSION: The literature on exercise and psychological interventions for smartphone addiction is scarce. There is a need to introduce new interventions and to validate the effectiveness of combined interventions. Our findings suggest that exercise and psychological interventions may help to reduce smartphone addiction. This combination was more effective compare to exercise or psychological intervention on mental health and addiction among university students. Future research should combine exercise and psychological interventions, focusing on university students, especially females, who are vulnerable to smartphone addiction. Further studies should focus on the cross-section of neuropsychology, cognitive psychology, and sports science to provide combined interventions in physiological and psychological direction.

    SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero, identifier: CRD42021278037.

  13. Liu H, Ding Z, Fan Y, Luo Y, Yang Y
    Polymers (Basel), 2023 Sep 26;15(19).
    PMID: 37835940 DOI: 10.3390/polym15193892
    The bonding properties of BFRP composites have been demonstrated in previous studies, satisfying the strength and durability criteria. In this paper, a further in-depth study is carried out to bond Basalt Fibre Reinforced Polymer (BFRP) to Aluminum Alloy 5052 using two bonding agents, Aralite® 2012 and Aralite® 2015, respectively. The salt sprays under 80 °C, 3.5% NaCl environment; 80 °C, 5% NaCl environment; and pure water environment are also considered for comparison. Experimental results show that joints created with Araldite® 2012 adhesives show higher average breaking strength (10.66 MPa at 720 h) and better ductility in a 5% NaCl environment. While the Araldite® 2015 adhesive joint exhibits a combination of tear failure and interface failure, along with thin-layer cohesion failure. In the SEM images of the two adhesive joints' failure, fiber pullout due to tension and damage at the interface between fiber and resin is apparent. To validate the experimental outcomes, water absorption testing, DSC, TGA-DTG, and FTIR experiments were conducted on dog-bone-shaped adhesive specimens to elucidate the results.
  14. Li C, Yi T, Zhang S, Ma C, Liu H
    Front Psychol, 2022;13:989581.
    PMID: 36186311 DOI: 10.3389/fpsyg.2022.989581
    Teacher beliefs are a pivotal psychological quality for sustainable teacher development. Previous studies have mainly focused on the beliefs of English-as-a-second/foreign-language (ESL/EFL) teachers, while little attention has been paid to those of Chinese-as-an-additional-language (CAL) teachers. Particularly, there is a paucity of effort made to develop and validate instrument for measuring pre-service CAL teacher beliefs. Therefore, to further quantify the beliefs of CAL teachers is increasingly called for as an essential means to help teachers sensitize their beliefs system and promote teacher development as a sustainable goal. To be specific, the present study aims to construct a scale for gauging beliefs of pre-service CAL teachers. It firstly conceptualizes the dimensions of pre-service CAL teacher beliefs by means of semantic analysis with ROST CM6, and then cross-validates the reliability and validity of the scale with psychometric methods. Two independent samples composed of 221 and 222 pre-service CAL teachers participated in a questionnaire survey. The two samples were utilized for later Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), respectively. The two data sets have satisfactory psychometric results, all confirming that the scale subsumes three factors: Beliefs about Chinese Language Teaching (BCLT), Beliefs about Chinese Language (BCL), and Beliefs about Chinese Language Learners (BCLL). The scale validated in the present study contributes to research on pre-service CAL teacher beliefs, and provides implications for sustainable pre-service CAL teacher training.
  15. Lu W, Liu H, Ma H, Tan TP, Xia L
    Front Hum Neurosci, 2023;17:1280241.
    PMID: 38034069 DOI: 10.3389/fnhum.2023.1280241
    Emotion recognition constitutes a pivotal research topic within affective computing, owing to its potential applications across various domains. Currently, emotion recognition methods based on deep learning frameworks utilizing electroencephalogram (EEG) signals have demonstrated effective application and achieved impressive performance. However, in EEG-based emotion recognition, there exists a significant performance drop in cross-subject EEG Emotion recognition due to inter-individual differences among subjects. In order to address this challenge, a hybrid transfer learning strategy is proposed, and the Domain Adaptation with a Few-shot Fine-tuning Network (DFF-Net) is designed for cross-subject EEG emotion recognition. The first step involves the design of a domain adaptive learning module specialized for EEG emotion recognition, known as the Emo-DA module. Following this, the Emo-DA module is utilized to pre-train a model on both the source and target domains. Subsequently, fine-tuning is performed on the target domain specifically for the purpose of cross-subject EEG emotion recognition testing. This comprehensive approach effectively harnesses the attributes of domain adaptation and fine-tuning, resulting in a noteworthy improvement in the accuracy of the model for the challenging task of cross-subject EEG emotion recognition. The proposed DFF-Net surpasses the state-of-the-art methods in the cross-subject EEG emotion recognition task, achieving an average recognition accuracy of 93.37% on the SEED dataset and 82.32% on the SEED-IV dataset.
  16. Liu H, Huang J, Li Q, Guan X, Tseng M
    Artif Intell Med, 2024 Feb;148:102776.
    PMID: 38325925 DOI: 10.1016/j.artmed.2024.102776
    This study proposes a deep convolutional neural network for the automatic segmentation of glioblastoma brain tumors, aiming sat replacing the manual segmentation method that is both time-consuming and labor-intensive. There are many challenges for automatic segmentation to finely segment sub-regions from multi-sequence magnetic resonance images because of the complexity and variability of glioblastomas, such as the loss of boundary information, misclassified regions, and subregion size. To overcome these challenges, this study introduces a spatial pyramid module and attention mechanism to the automatic segmentation algorithm, which focuses on multi-scale spatial details and context information. The proposed method has been tested in the public benchmarks BraTS 2018, BraTS 2019, BraTS 2020 and BraTS 2021 datasets. The Dice score on the enhanced tumor, whole tumor, and tumor core were respectively 79.90 %, 89.63 %, and 85.89 % on the BraTS 2018 dataset, respectively 77.14 %, 89.58 %, and 83.33 % on the BraTS 2019 dataset, and respectively 77.80 %, 90.04 %, and 83.18 % on the BraTS 2020 dataset, and respectively 83.48 %, 90.70 %, and 88.94 % on the BraTS 2021 dataset offering performance on par with that of state-of-the-art methods with only 1.90 M parameters. In addition, our approach significantly reduced the requirements for experimental equipment, and the average time taken to segment one case was only 1.48 s; these two benefits rendered the proposed network intensely competitive for clinical practice.
  17. Gao X, Liu H, Wang H, Fu S, Guo Z, Liang G
    PLoS Negl Trop Dis, 2013;7(9):e2459.
    PMID: 24069502 DOI: 10.1371/journal.pntd.0002459
    Although a previous study predicted that Japanese encephalitis virus (JEV) originated in the Malaysia/Indonesia region, the virus is known to circulate mainly on the Asian continent. However, there are no reported systematic studies that adequately define how JEV then dispersed throughout Asia.
  18. Basar NB, Liu H, Negi D, Sirat HM, Morris GA, Thomas EJ
    Org Biomol Chem, 2012 Mar 7;10(9):1743-5.
    PMID: 22274635 DOI: 10.1039/c2ob06906g
    The stereoselective reaction of an allyl bromide with an aldehyde mediated by a low valency bismuth species was the key reaction in stereoselective syntheses of (4S,6R,8R,10S,16S)- and (4S,6R,8R,10S,16R)-4,6,8,10,16-pentamethyldocosanes. (13)C NMR data for these compounds confirmed that the cuticular hydrocarbon isolated from the cane beetle Antitrogus parvulus was the (4S,6R,8R,10S,16S)-stereoisomer.
  19. Zhang KJ, Liu L, Rong X, Zhang GH, Liu H, Liu YH
    Mitochondrial DNA A DNA Mapp Seq Anal, 2016 11;27(6):4314-4315.
    PMID: 26462416
    We sequenced and annotated the complete mitochondrial genome (mitogenome) of Bactrocera diaphora (Diptera: Tephtitidae), which is an economically important pest in the southwest area of China, India, Sri Lanka, Vietnam and Malaysia. This mitogenome is 15 890 bp in length with an A + T content of 74.103%, and contains 37 typical animal mitochondrial genes that are arranged in the same order as that of the inferred ancestral insects. All protein-coding genes (PCGs) start with a typical ATN codon, except cox1 that begins with TCG. Ten PCGs stop with termination codon TAA or TAG, whereas cox1, nad1 and nad5 have single T-- as the incomplete stop codon. All of the transfer RNA genes present the typical clover leaf secondary structure except trnS1 (AGN) with a looping D-arm. The A + T-rich region is located between rrnS and trnI with a length of 946 bp, and contains a 20 bp poly-T stretch and 22 bp poly-A stretch. Except the control region, the longest intergenic spacer is located between trnR and trnN that is 94 bp long with an excessive high A + T content (95.74%) and a microsatellite-like region (TA)13.
  20. Diao J, Feng Z, Huang R, Liu H, Hamid SB, Su DS
    ChemSusChem, 2016 Apr 7;9(7):662-6.
    PMID: 26871428 DOI: 10.1002/cssc.201501516
    For the first time, significant improvement of the catalytic performance of nanodiamonds was achieved for the dehydrogenation of ethylbenzene to styrene under oxygen-lean conditions. We demonstrated that the combination of direct dehydrogenation and oxidative dehydrogenation indeed occurred on the nanodiamond surface throughout the reaction system. It was found that the active sp(2) -sp(3) hybridized nanostructure was well maintained after the long-term test and the active ketonic carbonyl groups could be generated in situ. A high reactivity with 40 % ethylbenzene conversion and 92 % styrene selectivity was obtained over the nanodiamond catalyst under oxygen-lean conditions even after a 240 h test, demonstrating the potential of this procedure for application as a promising industrial process for the ethylbenzene dehydrogenation to styrene without steam protection.
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