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Book ; Online: Advances in the Processing and Application of Polymer and Its Composites

Wu, Wei / Mi, Hao-Yang / Huang, Chongxing / Zhao, Hui / Liu, Tao

2022

Keywords	Technology: general issues ; History of engineering & technology ; Materials science ; waterborne polyurethane ; self-healing ; dynamic disulfide bond ; perovskite solar cell ; hole transport layer ; carbon materials ; polymeric composites ; solar energy materials ; PBAT ; MXene ; nanocomposite ; gas barrier properties ; biaxial stretching ; longan ; fruit ; polymeric films ; antioxidant activity ; enzymatic browning ; neem ; propyl disulfide ; microbial decay ; essential oil ; thickener ; dispersant ; graphene ; lignocellulose nanofibers ; adsorption ; deep eutectic solvents ; cationization ; dissolved and colloidal substances removal ; polyetheretherketone ; short fiber-reinforced ; material property ; lapping machinability ; cellulose nanofiber ; silica ; polypropylene ; composite ; hybrid filler ; thermoplastic silicone rubber ; backscattered electrons ; compatibility layer ; scanning electron microscope ; dynamic vulcanization ; cyclic loading ; deflection ; BFRP-RC beams ; steel fiber ; analytical model ; rosin-based composite membranes ; dencichine ; electrostatic spinning technology ; notoginseng extracts ; chrysin ; molecular imprinting ; adsorption performance ; binary functional monomers
Language	0\|e
Size	1 electronic resource (194 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021621183
ISBN	9783036554143 ; 3036554149
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: An empirical analysis on spatial effects of environmental protection

Hao Yang

Journal of King Saud University: Science, Vol 33, Iss 6, Pp 101525- (2021)

2021

Abstract: Objectives: The main objective is to provide policy recommendations on the enhancement of ecological environmental protection. Methods: Combined with spatial correlation analysis and spatial dynamic panel models, this paper estimates the spatial effect ... ...

Abstract	Objectives: The main objective is to provide policy recommendations on the enhancement of ecological environmental protection. Methods: Combined with spatial correlation analysis and spatial dynamic panel models, this paper estimates the spatial effect of the local government’s environmental protection expenditure on the improvement of the ecological environment based on China’s 30 provinces as samples from 2007 to 2017. Results: We find a significant impact of the environmental protection expenditure of the local government on the ecological environment improvement of neighbouring regions rather than the local region. It reveals a “free-riding” phenomenon in environmental governance. In addition, increasing the levels of economic development, urbanization, environmental regulations and industrial structure in the local region can effectively mitigate environmental pollution in the local region on one hand and contribute to the deterioration of the ecological environment in neighbouring regions on the other. Conclusions: The present study suggests that environmental regulations can significantly improve the local environment. The local governments at all levels must reinforce the supervision of the ecological protection areas to ensure the implementation of environmental regulations.
Keywords	Spatial effect ; Empirical analysis ; Spatial dynamics ; Environmental protection ; Expenditure ; Science (General) ; Q1-390
Subject code	333
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Train Distance Estimation for Virtual Coupling Based on Monocular Vision.

Hao, Yang / Tang, Tao / Gao, Chunhai

Sensors (Basel, Switzerland)

2024 Volume 24, Issue 4

Abstract: By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk ... ...

Abstract	By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.
Language	English
Publishing date	2024-02-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24041179
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Formula Graph Self-Attention Network for Representation-Domain Independent Materials Discovery.

Ihalage, Achintha / Hao, Yang

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

2022 Volume 9, Issue 18, Page(s) e2200164

Abstract: The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the ...

Abstract	The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the other that only uses stoichiometric information with the hope of discovering new materials. Graph neural networks (GNNs) in particular have excelled in predicting material properties within chemical accuracy. However, current GNNs are limited to only one of the above two avenues owing to the little overlap between respective material representations. Here, a new concept of formula graph which unifies stoichiometry-only and structure-based material descriptors is introduced. A self-attention integrated GNN that assimilates a formula graph is further developed and it is found that the proposed architecture produces material embeddings transferable between the two domains. The proposed model can outperform some previously reported structure-agnostic models and their structure-based counterparts while exhibiting better sample efficiency and faster convergence. Finally, the model is applied in a challenging exemplar to predict the complex dielectric function of materials and nominate new substances that potentially exhibit epsilon-near-zero phenomena.
MeSH term(s)	Machine Learning ; Models, Chemical ; Neural Networks, Computer ; Structure-Activity Relationship
Language	English
Publishing date	2022-04-27
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2808093-2
ISSN	2198-3844 ; 2198-3844
ISSN (online)	2198-3844
ISSN	2198-3844
DOI	10.1002/advs.202200164
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Diosgenin protects retinal pigment epithelial cells from inflammatory damage and oxidative stress induced by high glucose by activating AMPK/Nrf2/HO-1 pathway.

Hao, Yang / Gao, Xuefeng

Immunity, inflammation and disease

2022 Volume 10, Issue 12, Page(s) e698

Abstract: Introduction: Diosgenin is a natural steroidal compound with reported antidiabetic and many other protective properties. This study aimed to explore the protective effect of diosgenin on high-glucose (HG)-induced retinal pigment epithelial cells.: ... ...

Abstract	Introduction: Diosgenin is a natural steroidal compound with reported antidiabetic and many other protective properties. This study aimed to explore the protective effect of diosgenin on high-glucose (HG)-induced retinal pigment epithelial cells. Methods: HG-induced ARPE-19 cells were considered as a cell model of diabetic retinopathy (DR). The viability and apoptosis of ARPE-19 cells induced by HG treated with either diosgenin or Compound C (CC; dorsomorphin) were detected by Cell Counting Kit-8 assay and flow cytometric analysis. The expression of apoptosis-related proteins, inflammation-related proteins, and AMPK/Nrf2/HO-1 pathway-related proteins was detected by western blotting. The levels of inflammatory cytokines and detection of oxidative stress indexes were performed using the appropriate assay kits. The messenger RNA expression of inflammatory cytokines was detected by real-time quantitative polymerase chain reaction. Results: There was no obvious effect of diosgenin on the viability of ARPE-19 cells and the viability of ARPE-19 cells was significantly reduced after HG induction. However, diosgenin increased the viability, inhibited the apoptosis, and reduced the inflammatory response and oxidative stress of ARPE-19 cells induced by HG. In addition, diosgenin could activate the AMPK/Nrf2/HO-1 pathway. CC, an AMPK inhibitor, could reverse the above changes caused by diosgenin treatment in ARPE-19 cells induced by HG. Conclusions: Diosgenin could protect ARPE-19 cells from inflammatory damage and oxidative stress induced by HG, by activating the AMPK/Nrf2/HO-1 pathway.
MeSH term(s)	NF-E2-Related Factor 2/genetics ; Diosgenin/pharmacology ; AMP-Activated Protein Kinases ; Oxidative Stress ; Cytokines ; Epithelial Cells ; Retinal Pigments ; Glucose/toxicity
Chemical Substances	NF-E2-Related Factor 2 ; Diosgenin (K49P2K8WLX) ; AMP-Activated Protein Kinases (EC 2.7.11.31) ; Cytokines ; Retinal Pigments ; Glucose (IY9XDZ35W2)
Language	English
Publishing date	2022-11-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2740382-8
ISSN	2050-4527 ; 2050-4527
ISSN (online)	2050-4527
ISSN	2050-4527
DOI	10.1002/iid3.698
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Attention and feature transfer based knowledge distillation

Guoliang Yang / Shuaiying Yu / Yangyang Sheng / Hao Yang

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract Existing knowledge distillation (KD) methods are mainly based on features, logic, or attention, where features and logic represent the results of reasoning at different stages of a convolutional neural network, and attention maps symbolize the ... ...

Abstract	Abstract Existing knowledge distillation (KD) methods are mainly based on features, logic, or attention, where features and logic represent the results of reasoning at different stages of a convolutional neural network, and attention maps symbolize the reasoning process. Because of the continuity of the two in time, transferring only one of them to the student network will lead to unsatisfactory results. We study the knowledge transfer between the teacher-student network to different degrees, revealing the importance of simultaneously transferring knowledge related to the reasoning process and reasoning results to the student network, providing a new perspective for the study of KD. On this basis, we proposed the knowledge distillation method based on attention and feature transfer (AFT-KD). First, we use transformation structures to transform intermediate features into attentional and feature block (AFB) that contain both inference process information and inference outcome information, and force students to learn the knowledge in AFBs. To save computation in the learning process, we use block operations to align the teacher-student network. In addition, in order to balance the attenuation ratio between different losses, we design an adaptive loss function based on the loss optimization rate. Experiments have shown that AFT-KD achieves state-of-the-art performance in multiple benchmark tests.
Keywords	Medicine ; R ; Science ; Q
Subject code	004
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Factors influencing home care workers’ loyalty in long-term nursing services

Wei Hsu / Yen-Chi Chao / Chih-Hao Yang

Humanities & Social Sciences Communications, Vol 10, Iss 1, Pp 1-

2023 Volume 13

Abstract: Abstract Given the high prevalence of clinical disease and disability among elderly individuals, there is an ever-greater demand for social care services. Despite this demand, the elder care sector has the largest labor shortage levels among all front- ... ...

Abstract	Abstract Given the high prevalence of clinical disease and disability among elderly individuals, there is an ever-greater demand for social care services. Despite this demand, the elder care sector has the largest labor shortage levels among all front-line providers of long-term care services. Strategies to reduce turnover and improve employee loyalty have therefore become an important issue. The purpose of this study is to identify the factors that affect the loyalty of home care workers. Following a literature review, wedetermined four independent variables—job satisfaction, work engagement, organizational citizenship behavior (OCB) and transformational leadership—and investigated their relevance to, and ability to predict, home care workers’ employee loyalty. A total of 455 home care workers participated in the anonymous survey. The results of multiple regression analysis indicate that the adjusted coefficient of determination (R 2) of the model explained 65.6% of the variance of the dependent variable, showing high explanatory capacity. The influences of the four independent variables on employee loyalty were all significant and positive, with the greatest impact on employee loyalty being exerted by OCB (Adjusted ß = 0.400), followed by job satisfaction and then transformational leadership. The three hierarchical regression models provided evidence for the partial mediating effect of job satisfaction, work engagement and OCB between transformational leadership and employee loyalty. Our findings suggest that managers of home care institutions should adopt a transformational leadership style to motivate home care workers’ job satisfaction, work engagement and OCB. Such an environment not only help retain employee but also attract more young people to join the ranks of the home care profession, which will help solve fundamental short-staffing problems.
Keywords	History of scholarship and learning. The humanities ; AZ20-999 ; Social Sciences ; H
Subject code	650 ; 360
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Springer Nature
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Disentangling the cultural evolution of ancient China

Siyu Duan / Jun Wang / Hao Yang / Qi Su

Humanities & Social Sciences Communications, Vol 10, Iss 1, Pp 1-

a digital humanities perspective

2023 Volume 15

Abstract: Abstract Being recognized among the cradles of human civilization, ancient China nurtured the longest continuous academic traditions and humanistic spirits, which continue to impact today’s society. With an unprecedented large-scale corpus spanning 3000 ... ...

Abstract	Abstract Being recognized among the cradles of human civilization, ancient China nurtured the longest continuous academic traditions and humanistic spirits, which continue to impact today’s society. With an unprecedented large-scale corpus spanning 3000 years, this paper presents a quantitative analysis of cultural evolution in ancient China. Millions of intertextual associations are identified and modelled with a hierarchical framework via deep neural network and graph computation, thus allowing us to answer three progressive questions quantitatively: (1) What is the interaction between individual scholars and philosophical schools? (2) What are the vicissitudes of schools in ancient Chinese history? (3) How did ancient China develop a cross-cultural exchange with an externally introduced religion such as Buddhism? The results suggest that the proposed hierarchical framework for intertextuality modelling can provide sound suggestions for large-scale quantitative studies of ancient literature. An online platform is developed for custom data analysis within this corpus, which encourages researchers and enthusiasts to gain insight into this work. This interdisciplinary study inspires the re-understanding of ancient Chinese culture from a digital humanities perspective and prompts the collaboration between humanities and computer science.
Keywords	History of scholarship and learning. The humanities ; AZ20-999 ; Social Sciences ; H
Subject code	930
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Springer Nature
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Detection of 1,1 dimethylhydrazine by graphene oxide: first principles study.

Hao-Yang, Wang / Ying, Jia / Jing-Xin, Xiao

Journal of molecular modeling

2021 Volume 27, Issue 9, Page(s) 250

Abstract: The surface of graphene oxide (GO) with different oxidation levels is widely used in gas sensing applications. 1,1-Dimethylhydrazine (unsymmetrical dimethylhydrazine, UDMH) as a highly toxic and volatile pollution gas has long been investigated and ... ...

Abstract	The surface of graphene oxide (GO) with different oxidation levels is widely used in gas sensing applications. 1,1-Dimethylhydrazine (unsymmetrical dimethylhydrazine, UDMH) as a highly toxic and volatile pollution gas has long been investigated and discussed. The research reported here examined the stable structure of GO surface by first principles calculation. Furthermore, the adsorption mechanism of UDMH on the stable GO surface was explored and the optimal adsorption distance and upper limit of adsorption quantity were determined with their adsorption energy calculated. The results reveal that the hydroxyl group on GO did a great service to the UDMH adsorption and the UDMH tends to approach GO from the direction of -NH
Language	English
Publishing date	2021-08-15
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1284729-X
ISSN	0948-5023 ; 1610-2940
ISSN (online)	0948-5023
ISSN	1610-2940
DOI	10.1007/s00894-021-04873-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

Zhu, Bei / Yu, Hao-Yang / Du, Bing-Xue / Shi, Jian-Yu

Methods (San Diego, Calif.)

2024 Volume 222, Page(s) 51–56

Abstract: The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to ... ...

Abstract	The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the screening of MDAs at a low cost. Most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. However, different calculation methods result in distinct similarity matrices, and message passing in graph neural networks (GNNs) induces phenomena of over-smoothing and over-squashing, thereby impacting the performance of the model. To address these issues, we proposed a novel graph representation learning model, dual-modal graph learning for microbe-drug association prediction (DMGL-MDA). It comprises a dual-modal embedding module, a bipartite graph network embedding module, and a predictor module. To assess the performance of DMGL-MDA, we compared it against state-of-the-art methods using two benchmark datasets. Through cross-validation, we illustrated the superiority of DMGL-MDA. Furthermore, we conducted ablation experiments and case studies to validate the effective performance of the model.
MeSH term(s)	Humans ; Benchmarking ; Neural Networks, Computer ; Research Design
Language	English
Publishing date	2024-01-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2023.12.005
Database	MEDical Literature Analysis and Retrieval System OnLINE

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