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Relevant bibliographies by topics / LR-ABC mechanism

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Journal articles

Academic literature on the topic 'LR-ABC mechanism'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "LR-ABC mechanism"

1

Nurul, Halimatul Asmak Ismail, A. B. Awwad Samer, and Hassan Rosilah. "6RLR-ABC: 6LOWPAN Routing Protocol with Local Repair using Bio Inspired Artificial Bee Colony." International Journal of Computer Networks & Communications (IJCNC) 12, no. 3 (2020): 21–39. https://doi.org/10.5281/zenodo.3898022.

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In recent years, Micro-Electro-Mechanical System (MEMS) has successfully enabled the development of IPv6 over Low power Wireless Personal Area Network (6LoWPAN). This network is equipped with lowcost, low-power, lightweight and varied functions devices. These devices are capable of amassing, storing, processing environmental information and conversing with neighbouring sensors. These requisites pose a new and interesting challenge for the development of IEEE 802.15.4 together with routing protocol. In this work, 6LoWPAN Routing Protocol with Local Repair Using Bio Inspired Artificial Bee Colon

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Liu, Mei, Junbo Peng, Xuncheng Wang, et al. "Transcriptomic Analysis of Resistant and Wild-Type Botrytis cinerea Isolates Revealed Fludioxonil-Resistance Mechanisms." International Journal of Molecular Sciences 24, no. 2 (2023): 988. http://dx.doi.org/10.3390/ijms24020988.

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Botrytis cinerea, the causal agent of gray mold, is one of the most destructive pathogens of cherry tomatoes, causing fruit decay and economic loss. Fludioxonil is an effective fungicide widely used for crop protection and is effective against tomato gray mold. The emergence of fungicide-resistant strains has made the control of B. cinerea more difficult. While the genome of B. cinerea is available, there are few reports regarding the large-scale functional annotation of the genome using expressed genes derived from transcriptomes, and the mechanism(s) underlying such fludioxonil resistance re

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3

Rojas Arias, Nicolás, Cesar René Blanco-Zúñiga, and Sebastian Felipe Rojas-Arias. "Influence of radon gas removal on the oxygenation of thermal water bodies in the Paipa-Boyacá area." Journal of Applied Research and Technology 20, no. 1 (2022): 84–91. http://dx.doi.org/10.22201/icat.24486736e.2022.20.1.1641.

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In this work, the effect of radon gas removal was studied for thermal water samples subjected to aeration and dissolved oxygen (DO) transfer processes. The prototype used in this work consisted of the application of a diffusion aeration system, usually used in water treatment processes, and the incorporation of a camera with the presence of LR-115 passive detectors, used for the measurement of radon gas. The Pylon ABC-6 active measurement method was used as a reference mechanism for radon gas concentration, while the dissolved oxygen measurement was measured simultaneously using a digital oxim

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4

Wander, Seth Andrew, Caroline M. Weipert, Jiemin Liao, et al. "Real-world (RW) outcomes of patients (pts) with advanced breast cancer (aBC) with and without resistance alterations (alts) detected in cell-free circulating tumor DNA (ctDNA) prior to CDK4/6 inhibitor (CDK4/6i) plus endocrine therapy (ET) treatment." Journal of Clinical Oncology 41, no. 16_suppl (2023): 1081. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.1081.

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1081 Background: CDK4/6i plus ET is standard of care first-line (1L) therapy for hormone receptor positive (HR+), HER2- aBC. While molecular analyses from trials and pts have identified several mechanisms of resistance to CDK4/6i plus ET, currently, no biomarkers (outside of hormone status) are used for therapy selection. We utilized RW data to assess impact of putative baseline CDK4/6i+ET resistance alts (CDK4/6i+ET-R) on clinical outcomes among pts with aBC treated with CDK4/6i. Methods: Pts with aBC were identified via the Guardant INFORM database; those with a ctDNA test within 90 days pri

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5

Rahim, Asif, Yanru Zhong, Tariq Ahmad, Sadique Ahmad, Paweł Pławiak, and Mohamed Hammad. "Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models." Sensors 23, no. 15 (2023): 6979. http://dx.doi.org/10.3390/s23156979.

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Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their

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6

Zhang, Tianqi, Zhaoyang Wang, Liang Wu, et al. "The Effects of Disinfection Byproduct 2,6-Dichloro-1,4-benzoquinone on the Cyanobacterium Microcystis aeruginosa: From the Perspectives of Biochemistry and Non-Targeted Metabolomics." Toxics 13, no. 1 (2025): 64. https://doi.org/10.3390/toxics13010064.

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2,6-Dichloro-1,4-benzoquinone (2,6-DCBQ) is an emerging chlorinated disinfection byproduct (DBP) in bodies of water. However, this compound poses an unknown toxic effect on cyanobacteria. In this study, the toxicological mechanisms of 2,6-DCBQ in Microcystis aeruginosa (M. aeruginosa) were investigated through physiological and nontargeted metabolomic assessments. The results show that 2,6-DCBQ inhibited the growth of M. aeruginosa, reduced its photosynthetic pigment and protein contents, increased the levels of reactive oxygen species, damaged the antioxidant defense system, and aggravated th

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7

Motarwar, Shivani, and Dixshant Kumar Jha. "Assessment of Factors Influencing the Survival of Breast Cancer Patients using a Machine Learning Approach." International Journal of Innovative Technology and Exploring Engineering 11, no. 3 (2022): 80–84. http://dx.doi.org/10.35940/ijitee.c9713.0111322.

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Breast cancer is one of the deadliest diseases, claiming approximately 627,000 lives worldwide in 2018–2019. Therefore, early detection of breast cancer through automation in the prediction of the disease will help the medical industry to cure this disease at an early stage and thereby reduce the risk of death drastically. In the present study, the Breast Cancer Wisconsin (Diagnostic) Data Set has been taken from the University of California Irvine (UCI) Machine Learning Repository. The dataset (n=699) contained a total of 30 predictor parameters and one dependent parameter. The dependent vari

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