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Article ; Online: It starts at home: non-economic factors influencing consumer acceptance of battery storage in Australia.

McCarthy, Breda / Liu, Hongbo

Environmental science and pollution research international

2024

Abstract: Homeowners play a critical role in the uptake of low-carbon technologies, yet little is known about the factors that underlie market acceptance of residential battery storage. This research integrates social-psychological, demographic and behavioural ... ...

Abstract	Homeowners play a critical role in the uptake of low-carbon technologies, yet little is known about the factors that underlie market acceptance of residential battery storage. This research integrates social-psychological, demographic and behavioural factors into a holistic model that predicts market acceptance. Previous research has indicated that social factors play a crucial role in the adoption of rooftop solar. Still, the influence of subjective norms on battery storage, a relatively invisible technology, has yet to be fully understood. An online survey from homeowners in Australia, a mature renewable energy market, is used to provide insights into market acceptance that are relevant to international energy markets. A two-step econometric model, using factor analysis and ordered logistic regression, was used for data analysis. The results show that subjective norms, moral emotions and an environmental self-identity are positively associated with market acceptance. Demographic factors, such as younger age and higher levels of education, predict market acceptance. Motives such as technical interest, autarky and load-shifting behaviours are also relevant. Several recommendations for policymakers and practitioners are offered to improve the acceptance of battery storage, including interventions that exploit social parameters and appeal to consumer psychology.
Language	English
Publishing date	2024-02-27
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-024-32614-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Topology-based multi-jammer localization in wireless networks

Liu Hongbo / Chen Yingying / Xu Wenyuan / Liu Zhenhua / Su Yuchen

Security and Safety, Vol 3, p

2024 Volume 2023025

Abstract: Jamming attacks and unintentional radio interference are some of the most urgent threats harming the dependability of wireless communication and endangering the successful deployment of pervasive applications built on top of wireless networks. Unlike the ...

Abstract	Jamming attacks and unintentional radio interference are some of the most urgent threats harming the dependability of wireless communication and endangering the successful deployment of pervasive applications built on top of wireless networks. Unlike the traditional approaches focusing on developing jamming defense techniques without considering the location of jammers, we take a different viewpoint that the jammers’ position should be identified and exploited for building a wide range of defense strategies to alleviate jamming. In this paper, we address the problem of localizing multiple jamming attackers coexisting in wireless networks by leveraging the network topology changes caused by jamming. We systematically analyze the jamming effects and develop a framework that can partition network topology into clusters and can successfully estimate the positions of multiple jammers even when their jamming areas are overlapping. Our experiments on a multi-hop network setup using MicaZ sensor nodes validate the feasibility of real-time collection of network topology changes under jamming and our extensive simulation results demonstrate that our approach is highly effective in localizing multiple attackers with or without the prior knowledge of the order that the jammers are turned on.
Keywords	jammer localization ; wireless networks ; topology ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	000
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	EDP Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model

Liu Hongbo / Xie Liyan / Xing Cong

Open Life Sciences, Vol 18, Iss 1, Pp 753-

2023 Volume 60

Abstract: This study analyzes the distribution of pathogenic bacteria and their antimicrobial susceptibilities in elderly patients with cardiovascular diseases to identify risk factors for pulmonary infections. A risk prediction model is established, aiming to ... ...

Abstract	This study analyzes the distribution of pathogenic bacteria and their antimicrobial susceptibilities in elderly patients with cardiovascular diseases to identify risk factors for pulmonary infections. A risk prediction model is established, aiming to serve as a clinical tool for early prevention and management of pulmonary infections in this vulnerable population. A total of 600 patients were categorized into infected and uninfected groups. Independent risk factors such as older age, diabetes history, hypoproteinemia, invasive procedures, high cardiac function grade, and a hospital stay of ≥10 days were identified through logistic regression. A predictive model was constructed, with a Hosmer–Lemeshow goodness of fit (P = 0.236) and an area under the receiver operating characteristic curve of 0.795, demonstrating good discriminative ability. The model had 63.40% sensitivity and 82.80% specificity, with a cut-off value of 0.13. Our findings indicate that the risk score model is valid for identifying high-risk groups for pulmonary infection among elderly cardiovascular patients. The study contributes to the early prevention and control of pulmonary infections, potentially reducing infection rates in this vulnerable population.
Keywords	cardiovascular disease ; pulmonary infection ; pathogenic bacteria ; drug resistance ; risk prediction model ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Discussion on the Rise of Healthy Chinese Sports Industry and the Training Mode of Compound Sports Talents.

Liu, Hongbo / Li, Ming

Journal of healthcare engineering

2022 Volume 2022, Page(s) 6943285

Abstract: With the improvement of people's living standards and quality, sports charm stimulates people's awareness of sports consumption and makes sports industry develop rapidly into one of the important industries for the sustainable development of national ... ...

Abstract	With the improvement of people's living standards and quality, sports charm stimulates people's awareness of sports consumption and makes sports industry develop rapidly into one of the important industries for the sustainable development of national economy. In modern society, sports industry has become a new industry with huge business opportunities. Especially, when the social economy develops to a certain level and sports form a certain scale, the interdependence, mutual support, and mutual promotion between sports and economy become more inseparable. This paper studies and discusses the training strategy of "compound" talents in sports industry under the background of healthy China strategy, in order to promote the development and innovation of China's sports industry. In terms of training sports talents, we should not only pay attention to personal technical, professional, practical, and understanding contents but also introduce some humanistic contents and social hot contents to improve the comprehensive quality of talents.
MeSH term(s)	China ; Health Status ; Humans ; Industry ; Sports
Language	English
Publishing date	2022-03-07
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2545054-2
ISSN	2040-2309 ; 2040-2295
ISSN (online)	2040-2309
ISSN	2040-2295
DOI	10.1155/2022/6943285
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Research on Monitoring of Gymnastics Facilities and Intelligent Optimal Distribution of Gymnastics Venues Based on Internet of Things.

Liu, Hongbo / Wang, Yuzhen

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 6164448

Abstract: In view of the low level of gymnastics facilities monitoring and intelligent management of gymnastics venues, which cannot effectively manage gymnastics venues in real time, this paper proposes a method of gymnastics facilities monitoring and intelligent ...

Abstract	In view of the low level of gymnastics facilities monitoring and intelligent management of gymnastics venues, which cannot effectively manage gymnastics venues in real time, this paper proposes a method of gymnastics facilities monitoring and intelligent optimization distribution of gymnastics venues in the Internet of Things. This paper will build an information monitoring model, introduce a particle swarm optimization algorithm to participate in the location layout, and explore the actual effect of gymnastics and the Internet of Things. The research results show that (1) the system can measure the mechanical error of facilities and ensure that the controllable fault tolerance rate is less than 1%. (2) The quality of system monitoring is evaluated in accuracy, time, delay, and satisfaction, and the results are basically satisfactory, but the accuracy and time still need to be improved. (3) Using the evaluation system of sports facilities to test the temperature, humidity, facility pressure, and energy consumption suitable for gymnastics and to verify the injury tendency of athletes. When the damage tendency is between 70% and 100%, the actual damage rate is 1. (4) The speed of the PSO algorithm is faster than other methods, which is used to optimize the layout of gymnastics venues and has a certain role in promoting the construction of gymnastics venues. The system model designed in this paper performs well in gymnastics and needs to be further improved and optimized.
MeSH term(s)	Algorithms ; Gymnastics ; Humans ; Internet ; Internet of Things
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/6164448
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Article ; Online: Identification of potential immune/diagnosis related gene-immunocyte subtype networks in extracellular immune response to respiratory syncytial virus infection.

Wang, Baohong / Liu, Hongbo

Virus research

2022 Volume 321, Page(s) 198906

Abstract: Introduction: Respiratory syncytial virus (RSV) is one of the important pathogenic agents of pediatric respiratory tract infection. Weighted gene co-expression network analysis (WGCNA) is used to study autoimmune diseases, which can find potential hub ... ...

Abstract	Introduction: Respiratory syncytial virus (RSV) is one of the important pathogenic agents of pediatric respiratory tract infection. Weighted gene co-expression network analysis (WGCNA) is used to study autoimmune diseases, which can find potential hub genes. The diagnostic model based on hub genes and machine learning makes it possible to diagnose the extracellular immune response to RSV infection early. Objective: The aim of the present study was to identify potential immune, diagnose and treatment related genes expressed in RSV-infected cells. Methods: Firstly, gene expression data were downloaded from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Secondly, WGCNA was performed based on DEGs to obtain hub genes related to immunity score. Thirdly, protein-protein interaction (PPI) and the immune infiltration analysis of hub immune related genes were performed. Finally, diagnostic and immune related genes were identified by machine learning, followed by functional analysis. Results: Totally, 2063 DEGs were identified in the extracellular immune response to RSV infection. Among which, 10 key immune and diagnosis related genes were identified, including ITGA2B, GP9, ITGB3, SELP, PPBP, MPL, CXCL8, NFE2, PTGS1 and LY6G6F. Several immune/diagnosis related gene-immunocyte subtype networks were identified, such as CXCL8-Type 17 T helper cell, LY6G6F-CD56 bright natural killer cell, PPBP-activated CD4 T cell/T follicular helper cell, NFE2/PTGS1/SELP-activated dendritic cell, GP9/ITGA2B/MPL-activated CD8 T cell. ITGB3, MPL and PTGS1 could be considered as therapeutic targets. Some significantly enriched signaling pathways were identified, including hematopoietic cell lineage (involving GP9 and ITGA2B), cytokine-cytokine receptor interaction (involving MPL), chemokine signaling pathway (involving PPBP) and arachidonic acid metabolism (involving PTGS1). Conclusions: The 10-immune related gene signature may be used as potential diagnostic markers for the extracellular immune response to RSV infection, which may provide a new field in searching for diagnostic and therapeutic molecules in the extracellular immune response to RSV infection.
MeSH term(s)	Arachidonic Acid ; Chemokines ; Child ; Computational Biology ; Cytokines/genetics ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Immunity ; Receptors, Cytokine ; Respiratory Syncytial Virus Infections/diagnosis ; Respiratory Syncytial Virus Infections/genetics ; Respiratory Syncytial Virus, Human/genetics
Chemical Substances	Chemokines ; Cytokines ; Receptors, Cytokine ; Arachidonic Acid (27YG812J1I)
Language	English
Publishing date	2022-08-28
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	605780-9
ISSN	1872-7492 ; 0168-1702
ISSN (online)	1872-7492
ISSN	0168-1702
DOI	10.1016/j.virusres.2022.198906
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Article: An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines.

Zhou, Wei / Liu, Hongbo / Zhou, Rui / Li, Jiafu / Ahmadi, Sina

Heliyon

2024 Volume 10, Issue 9, Page(s) e30363

Abstract: Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With ...

Abstract	Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.
Language	English
Publishing date	2024-04-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e30363
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Article: Correction: Nian et al. Coping Strategies for Pertussis Resurgence.

Nian, Xuanxuan / Liu, Hongbo / Cai, Mengyao / Duan, Kai / Yang, Xiaoming

Vaccines

2024 Volume 12, Issue 2

Abstract: The authors would like to make the following corrections to this published paper [ ... ]. ...

Abstract	The authors would like to make the following corrections to this published paper [...].
Language	English
Publishing date	2024-01-31
Publishing country	Switzerland
Document type	Published Erratum
ZDB-ID	2703319-3
ISSN	2076-393X
ISSN	2076-393X
DOI	10.3390/vaccines12020151
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Self-distillation framework for document-level relation extraction in low-resource environments.

Wu, Hao / Zhou, Gang / Xia, Yi / Liu, Hongbo / Zhang, Tianzhi

PeerJ. Computer science

2024 Volume 10, Page(s) e1930

Abstract: The objective of document-level relation extraction is to retrieve the relations existing between entities within a document. Currently, deep learning methods have demonstrated superior performance in document-level relation extraction tasks. However, to ...

Abstract	The objective of document-level relation extraction is to retrieve the relations existing between entities within a document. Currently, deep learning methods have demonstrated superior performance in document-level relation extraction tasks. However, to enhance the model's performance, various methods directly introduce additional modules into the backbone model, which often increases the number of parameters in the overall model. Consequently, deploying these deep models in resource-limited environments presents a challenge. In this article, we introduce a self-distillation framework for document-level relational extraction. We partition the document-level relation extraction model into two distinct modules, namely, the entity embedding representation module and the entity pair embedding representation module. Subsequently, we apply separate distillation techniques to each module to reduce the model's size. In order to evaluate the proposed framework's performance, two benchmark datasets for document-level relation extraction, namely GDA and DocRED are used in this study. The results demonstrate that our model effectively enhances performance and significantly reduces the model's size.
Language	English
Publishing date	2024-03-29
Publishing country	United States
Document type	Journal Article
ISSN	2376-5992
ISSN (online)	2376-5992
DOI	10.7717/peerj-cs.1930
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Study on catalytic removal of toluene by metal oxides derived from hydrotalcite

Liu Hongbo / Huang Zhiyong

E3S Web of Conferences, Vol 272, p

2021 Volume 01008

Abstract: Hydrotalcite (HT) precursor was synthesized by coprecipitation method, and the surface of HT precursor was modified. The mixed metal oxide (MO) catalyst was prepared by calcination of HT precursor, which was used to remove toluene from VOCs assisted by ... ...

Abstract	Hydrotalcite (HT) precursor was synthesized by coprecipitation method, and the surface of HT precursor was modified. The mixed metal oxide (MO) catalyst was prepared by calcination of HT precursor, which was used to remove toluene from VOCs assisted by NTP technology. The catalytic performance of MO catalyst was investigated. The results show that the MO catalyst with good structure can be obtained after calcination of HT precursor before and after modification. The results of catalytic performance test showed that the initial concentration of toluene was 700 ppm, the gas flow rate was 600 mL/min, and the reaction time was 30 min, SIE of NTP was above 3.0 kJ/L, the toluene conversion rate reached above 90%. MO assisted NTP had better catalytic performance with lower energy consumption. The toluene treatment capacity per unit energy consumption increased from 15.3% to 20.6%, which had an increase of 34.6%.
Keywords	Environmental sciences ; GE1-350
Subject code	660
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	EDP Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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