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  1. Article ; Online: Fostering dialogue: a phenomenological approach to bridging the gap between the "voice of medicine" and the "voice of the lifeworld".

    Zhang, Junguo

    Medicine, health care, and philosophy

    2024  

    Abstract: This article adopts Husserl's transcendental phenomenology to explore the complex relationship between patients and physicians. It delves into the coexistence of two distinct voices in the realm of medicine and health: the "voice of medicine" and the " ... ...

    Abstract This article adopts Husserl's transcendental phenomenology to explore the complex relationship between patients and physicians. It delves into the coexistence of two distinct voices in the realm of medicine and health: the "voice of medicine" and the "voice of life-world." Divided into three sections, the article emphasizes the importance of shifting from a scientific-medical attitude to a more personalistic approach in physician-patient interactions. This shift aims to prevent depersonalization and desubjectification. Additionally, it highlights the equal and irreducible nature of patients while acknowledging the vital role physicians hold in the realm of illness. The article stresses the need for a balanced and equitable relationship between both parties, rooted in the shared life-world. Moreover, empathy is underscored as a crucial element in fostering meaningful dialogue, wherein understanding diverse perspectives and attitudes towards illness is paramount. The article argues that differences between patients and physicians are necessary for empathy, while shared similarities form its foundation. Ultimately, a harmonious relationship facilitates empathy and enables the constitution of a new sense of life for both patients and physicians.
    Language English
    Publishing date 2024-01-29
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1440052-2
    ISSN 1572-8633 ; 1386-7423
    ISSN (online) 1572-8633
    ISSN 1386-7423
    DOI 10.1007/s11019-024-10195-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5054: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  2. Article ; Online: Reframing Anorexia Nervosa: A Phenomenological Exploration of the Self-Other Relationship with Husserl's Intersubjective Theory.

    Zhang, Junguo

    Psychopathology

    2023  , Page(s) 1–7

    Abstract: This paper explores the overlooked contributions of Husserl's Phenomenology of intersubjectivity in understanding anorexia nervosa. It highlights the intricate relationship between the self and others, emphasizing their mutual constitution while ... ...

    Abstract This paper explores the overlooked contributions of Husserl's Phenomenology of intersubjectivity in understanding anorexia nervosa. It highlights the intricate relationship between the self and others, emphasizing their mutual constitution while acknowledging inherent differences. The distorted body image approach often overlooks this perspective, leading to psychopathological issues in individuals with anorexia nervosa. By integrating subjective experience and external observation, a more balanced and equal intersubjective relationship can be established. Utilizing this philosophical framework allows for a deeper understanding of the disorder's dynamics and sheds new light on the subjective experiences of individuals with anorexia nervosa in relation to others.
    Language English
    Publishing date 2023-09-26
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 605604-0
    ISSN 1423-033X ; 0254-4962
    ISSN (online) 1423-033X
    ISSN 0254-4962
    DOI 10.1159/000533989
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 561: Show issues Location:
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  3. Article ; Online: A case of cholesterol crystals detected in 37-year pleural effusion.

    Hu, Yahong / Zhang, Junguo

    QJM : monthly journal of the Association of Physicians

    2024  

    Language English
    Publishing date 2024-04-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 1199985-8
    ISSN 1460-2393 ; 0033-5622 ; 1460-2725
    ISSN (online) 1460-2393
    ISSN 0033-5622 ; 1460-2725
    DOI 10.1093/qjmed/hcae066
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  4. Article ; Online: DJAN

    Changchun Zhang / Junguo Zhang

    Animals, Vol 13, Iss 21, p

    Deep Joint Adaptation Network for Wildlife Image Recognition

    2023  Volume 3333

    Abstract: Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream ... ...

    Abstract Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream research direction. However, wildlife image recognition tasks face the challenge of weak generalization in open environments. In this paper, a Deep Joint Adaptation Network (DJAN) for wildlife image recognition is proposed to deal with the above issue by taking a transfer learning paradigm into consideration. To alleviate the distribution discrepancy between the known dataset and the target task dataset while enhancing the transferability of the model’s generated features, we introduce a correlation alignment constraint and a strategy of conditional adversarial training, which enhance the capability of individual domain adaptation modules. In addition, a transformer unit is utilized to capture the long-range relationships between the local and global feature representations, which facilitates better understanding of the overall structure and relationships within the image. The proposed approach is evaluated on a wildlife dataset; a series of experimental results testify that the DJAN model yields state-of-the-art results, and, compared to the best results obtained by the baseline methods, the average accuracy of identifying the eleven wildlife species improves by 3.6 percentage points.
    Keywords transfer learning ; wildlife recognition ; distribution discrepancy ; domain adaptation ; deep learning ; Veterinary medicine ; SF600-1100 ; Zoology ; QL1-991
    Subject code 006
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition.

    Zhang, Changchun / Zhang, Junguo

    Animals : an open access journal from MDPI

    2023  Volume 13, Issue 21

    Abstract: Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream ... ...

    Abstract Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream research direction. However, wildlife image recognition tasks face the challenge of weak generalization in open environments. In this paper, a Deep Joint Adaptation Network (DJAN) for wildlife image recognition is proposed to deal with the above issue by taking a transfer learning paradigm into consideration. To alleviate the distribution discrepancy between the known dataset and the target task dataset while enhancing the transferability of the model's generated features, we introduce a correlation alignment constraint and a strategy of conditional adversarial training, which enhance the capability of individual domain adaptation modules. In addition, a transformer unit is utilized to capture the long-range relationships between the local and global feature representations, which facilitates better understanding of the overall structure and relationships within the image. The proposed approach is evaluated on a wildlife dataset; a series of experimental results testify that the DJAN model yields state-of-the-art results, and, compared to the best results obtained by the baseline methods, the average accuracy of identifying the eleven wildlife species improves by 3.6 percentage points.
    Language English
    Publishing date 2023-10-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606558-7
    ISSN 2076-2615
    ISSN 2076-2615
    DOI 10.3390/ani13213333
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Temporal Trend in Suicide Mortality for Chinese Adolescents, 2008 to 2021.

    Tian, Youping / Luan, Min / Chen, Huimin / Zhang, Junguo

    JAMA pediatrics

    2023  Volume 177, Issue 11, Page(s) 1224–1226

    MeSH term(s) Adolescent ; Humans ; East Asian People/psychology ; East Asian People/statistics & numerical data ; Sex Distribution ; Suicide/statistics & numerical data ; Suicide/trends
    Language English
    Publishing date 2023-08-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2701223-2
    ISSN 2168-6211 ; 2168-6203
    ISSN (online) 2168-6211
    ISSN 2168-6203
    DOI 10.1001/jamapediatrics.2023.3062
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Um IV Zs.74: Show issues Location:
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  7. Article ; Online: A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning

    Yujie Zhong / Xiao Li / Jiangjian Xie / Junguo Zhang

    Animals, Vol 13, Iss 838, p

    2023  Volume 838

    Abstract: Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are ...

    Abstract Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing background information. This strategy alleviates the model’s focus on the background, guiding it to focus on the wildlife in order to improve the model’s generality, resulting in better recognition performance. Furthermore, to offer a lightweight recognition model for deep learning-based real-time wildlife monitoring on edge devices, we develop a model compression strategy that combines adaptive pruning and knowledge distillation. Specifically, a student model is built using a genetic algorithm-based pruning technique and adaptive batch normalization (GA-ABN). A mean square error (MSE) loss-based knowledge distillation method is then used to fine-tune the student model so as to generate a lightweight recognition model. The produced lightweight model can reduce the computational effort of wildlife recognition with only a 4.73% loss in accuracy. Extensive experiments have demonstrated the advantages of our method, which is beneficial for real-time wildlife monitoring with edge intelligence.
    Keywords automatic wildlife recognition ; shortcut learning ; data augmentation ; model compression ; Veterinary medicine ; SF600-1100 ; Zoology ; QL1-991
    Subject code 006
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Effect of granular activated carbon and chloroform on chain elongation with simple substrate ethanol and acetate.

    Li, Lin / Xu, Linji / He, Junguo / He, Qiang / Zhang, Jie

    Environmental research

    2023  Volume 221, Page(s) 115324

    Abstract: Chain elongation is a promising technology for production of medium-chain fatty acids (MCFAs). Granular activated carbon (GAC) is commonly used in anaerobic fermentation. Low level ... ...

    Abstract Chain elongation is a promising technology for production of medium-chain fatty acids (MCFAs). Granular activated carbon (GAC) is commonly used in anaerobic fermentation. Low level CHCl
    MeSH term(s) Caproates ; Ethanol ; Chloroform ; Charcoal ; Fermentation ; Acetates ; Fatty Acids ; Bioreactors
    Chemical Substances hexanoic acid (1F8SN134MX) ; Caproates ; Ethanol (3K9958V90M) ; Chloroform (7V31YC746X) ; Charcoal (16291-96-6) ; Acetates ; Fatty Acids
    Language English
    Publishing date 2023-01-18
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 205699-9
    ISSN 1096-0953 ; 0013-9351
    ISSN (online) 1096-0953
    ISSN 0013-9351
    DOI 10.1016/j.envres.2023.115324
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 726: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (1.OG)
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  9. Article: A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning.

    Zhong, Yujie / Li, Xiao / Xie, Jiangjian / Zhang, Junguo

    Animals : an open access journal from MDPI

    2023  Volume 13, Issue 5

    Abstract: Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are ...

    Abstract Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing background information. This strategy alleviates the model's focus on the background, guiding it to focus on the wildlife in order to improve the model's generality, resulting in better recognition performance. Furthermore, to offer a lightweight recognition model for deep learning-based real-time wildlife monitoring on edge devices, we develop a model compression strategy that combines adaptive pruning and knowledge distillation. Specifically, a student model is built using a genetic algorithm-based pruning technique and adaptive batch normalization (GA-ABN). A mean square error (MSE) loss-based knowledge distillation method is then used to fine-tune the student model so as to generate a lightweight recognition model. The produced lightweight model can reduce the computational effort of wildlife recognition with only a 4.73% loss in accuracy. Extensive experiments have demonstrated the advantages of our method, which is beneficial for real-time wildlife monitoring with edge intelligence.
    Language English
    Publishing date 2023-02-25
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606558-7
    ISSN 2076-2615
    ISSN 2076-2615
    DOI 10.3390/ani13050838
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Editorial

    Junguo Liu / Laixiang Sun / Zhan Tian / Qinghua Ye / Shiqiang Wu / Shuyu Zhang

    Frontiers in Environmental Science, Vol

    Nature-based solutions for urban water management

    2023  Volume 11

    Keywords green infrastructure ; rainwater ; water resources ; human-natural systems ; urban water ; Environmental sciences ; GE1-350
    Language English
    Publishing date 2023-07-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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