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  1. Article ; Online: Paths to sustainable plastic waste recycling.

    Dai, Leilei / Ruan, Roger / You, Siming / Lei, Hanwu

    Science (New York, N.Y.)

    2022  Volume 377, Issue 6609, Page(s) 934

    MeSH term(s) Greenhouse Effect/prevention & control ; Plastics ; Recycling ; Waste Management
    Chemical Substances Plastics
    Language English
    Publishing date 2022-08-25
    Publishing country United States
    Document type Letter
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.ade2221
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  2. Article ; Online: Artificial intelligence and machine learning for smart bioprocesses.

    Khanal, Samir Kumar / Tarafdar, Ayon / You, Siming

    Bioresource technology

    2023  Volume 375, Page(s) 128826

    Abstract: In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable ... ...

    Abstract In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.
    MeSH term(s) Artificial Intelligence ; Machine Learning
    Language English
    Publishing date 2023-03-05
    Publishing country England
    Document type Editorial
    ZDB-ID 1065195-0
    ISSN 1873-2976 ; 0960-8524
    ISSN (online) 1873-2976
    ISSN 0960-8524
    DOI 10.1016/j.biortech.2023.128826
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  3. Article ; Online: Dynamic Carbon-Neutrality Assessment Needed to Tackle the Impacts of Global Crises.

    Jiang, Peng / Sonne, Christian / You, Siming

    Environmental science & technology

    2022  Volume 56, Issue 14, Page(s) 9851–9853

    MeSH term(s) Carbon ; Carbon Dioxide ; Economic Development ; Global Warming ; Greenhouse Effect
    Chemical Substances Carbon Dioxide (142M471B3J) ; Carbon (7440-44-0)
    Language English
    Publishing date 2022-07-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1520-5851
    ISSN (online) 1520-5851
    DOI 10.1021/acs.est.2c04412
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  4. Article ; Online: Minimize food loss and waste to prevent crises.

    You, Siming / Sonne, Christian / Ruan, Roger / Jiang, Peng

    Science (New York, N.Y.)

    2022  Volume 376, Issue 6600, Page(s) 1390

    MeSH term(s) Armed Conflicts ; Food Security ; Refuse Disposal ; Russia ; Ukraine ; Waste Management
    Language English
    Publishing date 2022-06-23
    Publishing country United States
    Document type Letter
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.add2008
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  5. Article ; Online: Review of biochar production via crop residue pyrolysis: Development and perspectives.

    Li, Yize / Gupta, Rohit / Zhang, Qiaozhi / You, Siming

    Bioresource technology

    2022  Volume 369, Page(s) 128423

    Abstract: Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole ...

    Abstract Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically summarises up-to-date knowledge of crop residue pyrolysis processes, influential factors, and biochar applications. Essential process design tools for biochar production e.g., cost-benefit analysis, life cycle assessment, and machine learning methods are also reviewed, which has often been overlooked in prior reviews. Important aspects include (a) correlating techno-economics of biochar production with crop residue compositions, (b) process operating conditions and management strategies, (c) biochar applications including soil amendment, fuel displacement, catalytic usage, etc., (d) data-driven modelling techniques, (e) properties of biochar, and (f) climate change mitigation. Overall, the review will support the development of application-oriented process pipelines for crop residue-based biochar.
    Language English
    Publishing date 2022-11-30
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1065195-0
    ISSN 1873-2976 ; 0960-8524
    ISSN (online) 1873-2976
    ISSN 0960-8524
    DOI 10.1016/j.biortech.2022.128423
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  6. Article ; Online: Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass.

    Li, Yize / Gupta, Rohit / You, Siming

    Bioresource technology

    2022  Volume 359, Page(s) 127511

    Abstract: Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, ...

    Abstract Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.
    MeSH term(s) Biomass ; Charcoal ; Machine Learning ; Pyrolysis
    Chemical Substances biochar ; Charcoal (16291-96-6)
    Language English
    Publishing date 2022-06-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 1065195-0
    ISSN 1873-2976 ; 0960-8524
    ISSN (online) 1873-2976
    ISSN 0960-8524
    DOI 10.1016/j.biortech.2022.127511
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  7. Article ; Online: Does climate change affect enterprises’ ability to sell their products?

    Siming Yu / Cheng Cai / Shuocheng Zhang / Peng You

    Frontiers in Ecology and Evolution, Vol

    2022  Volume 10

    Abstract: Based on local cities’ climate change data and the operating income data of Chinese listed enterprises from 2008 to 2019, we study the impact of climate change on enterprises’ sales ability and analyze its subsequent influence on their sales revenue from ...

    Abstract Based on local cities’ climate change data and the operating income data of Chinese listed enterprises from 2008 to 2019, we study the impact of climate change on enterprises’ sales ability and analyze its subsequent influence on their sales revenue from three dimensions: inside enterprises, the supply side and the demand side. The results show that climate change reduces enterprises’ sales revenue. From enterprises’ internal perspective, climate change reduces production efficiency, increases management costs and the impact on the cost of sales is not obvious. From the demand side, climate change increases business risks and reduces transactions between large customers and enterprises. The share of sales from large customers is reduced, but no obvious impact is observed for small customers. On the supply side, climate change reduces business credit levels, which in turn reduces sales revenue. A heterogeneity analysis shows that the impact of climate change on sales revenue is more obvious in private enterprises, enterprises with weak internal controls and enterprises in central and western regions of China.
    Keywords climate change ; rising temperatures ; sales power ; company ability ; product selling ; Evolution ; QH359-425 ; Ecology ; QH540-549.5
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Biomass to green hydrogen (BGH2-2022).

    Bhaskar, Thallada / Venkata Mohan, S / You, Siming / Kim, Sang-Hyoun / Porto de Souza Vandenberghe, Luciana

    Bioresource technology

    2023  Volume 376, Page(s) 128924

    MeSH term(s) Biomass ; Hydrogen
    Chemical Substances Hydrogen (7YNJ3PO35Z)
    Language English
    Publishing date 2023-03-21
    Publishing country England
    Document type Editorial
    ZDB-ID 1065195-0
    ISSN 1873-2976 ; 0960-8524
    ISSN (online) 1873-2976
    ISSN 0960-8524
    DOI 10.1016/j.biortech.2023.128924
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  9. Article ; Online: Green strategies for sustainable packaging.

    You, Siming / Li, Wangliang / Yan, Wei-Cheng / Sonne, Christian

    Science (New York, N.Y.)

    2021  Volume 372, Issue 6544, Page(s) 802

    Language English
    Publishing date 2021-05-20
    Publishing country United States
    Document type Letter
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.abj1042
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  10. Article ; Online: Mediating effect of inflammation on the relationship between sleep disruption and suicidal ideation in major depressive disorder.

    Zeng, Yexian / Li, Weicheng / Chen, Xiaoyu / You, Zerui / Mai, Siming / Lan, Xiaofeng / Ning, Yuping / Zhou, Yanling

    Journal of affective disorders

    2024  Volume 352, Page(s) 371–378

    Abstract: Background: Sleep disruption, particularly insomnia, is a notable characteristic of depression and is associated with an increased risk of suicide in patients diagnosed with major depressive disorder (MDD). Moreover, the pathophysiology of depression ... ...

    Abstract Background: Sleep disruption, particularly insomnia, is a notable characteristic of depression and is associated with an increased risk of suicide in patients diagnosed with major depressive disorder (MDD). Moreover, the pathophysiology of depression and suicide has been linked to inflammation, specifically proinflammatory cytokines. However, the complex interplay among these factors in individuals with MDD remains poorly understood. This study investigated the mediating role of inflammatory cytokines in the relationship between sleep disruption and suicidal ideation (SI), with a particular emphasis on gender differences.
    Methods: This study used a cross-sectional design in which 281 individuals diagnosed with MDD were recruited from psychiatric clinics. The main assessments included the evaluation of sleep disruption, inflammatory markers, and SI. The Beck Scale for Suicide Ideation (SSI) scores was employed to quantify SI, whereas HAMD-SLD, a component of the Hamilton Rating Scale (HAMD-17), was used to evaluate sleep disruption. Blood analysis was performed to measure inflammatory markers.
    Result: For females diagnosed with MDD, significant associations between sleep disruption and the levels of IL-6 (B = 0.994, p = 0.013) and TNF-α (B = 1.986, p = 0.016) were found when IL-6 or TNF-α were considered as mediators in the regression model. In addition, IL-6 (B = 5.689, p < 0.001) and TNF-α (B = 9.916, p = 0.006) exhibited strong correlation with SSI scores.
    Conclusions: The primary results of this study indicate that IL-6 and TNF-α could function as potential mediators in the relationship between sleep disruption and SI among female patients diagnosed with MDD.
    Clinical trial: Name of the registry: Zhejiang University Trial registration number: ChiCTR1800017626 Date of registration: 2018-08-07, 'Retrospectively registered' URL of trial registry record: https://www.chictr.org.cn/showproj.html?proj=27321.
    MeSH term(s) Humans ; Female ; Depressive Disorder, Major/psychology ; Suicidal Ideation ; Tumor Necrosis Factor-alpha ; Interleukin-6 ; Cross-Sectional Studies ; Sleep ; Inflammation
    Chemical Substances Tumor Necrosis Factor-alpha ; Interleukin-6
    Language English
    Publishing date 2024-02-22
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 135449-8
    ISSN 1573-2517 ; 0165-0327
    ISSN (online) 1573-2517
    ISSN 0165-0327
    DOI 10.1016/j.jad.2024.02.078
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