Готові списки джерел за темами / Dynamic Cell Clustering (DCC)

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Добірка наукової літератури з теми "Dynamic Cell Clustering (DCC)"

Автор: Grafiati

Опубліковано: 2 листопада 2024

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Статті в журналах з теми "Dynamic Cell Clustering (DCC)"

Wang, Zheng, Lara M. Linden, Kaleb M. Naegeli, Joshua W. Ziel, Qiuyi Chi, Elliott J. Hagedorn, Natasha S. Savage, and David R. Sherwood. "UNC-6 (netrin) stabilizes oscillatory clustering of the UNC-40 (DCC) receptor to orient polarity." Journal of Cell Biology 206, no. 5 (August 25, 2014): 619–33. http://dx.doi.org/10.1083/jcb.201405026.

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The receptor deleted in colorectal cancer (DCC) directs dynamic polarizing activities in animals toward its extracellular ligand netrin. How DCC polarizes toward netrin is poorly understood. By performing live-cell imaging of the DCC orthologue UNC-40 during anchor cell invasion in Caenorhabditis elegans, we have found that UNC-40 clusters, recruits F-actin effectors, and generates F-actin in the absence of UNC-6 (netrin). Time-lapse analyses revealed that UNC-40 clusters assemble, disassemble, and reform at periodic intervals in different regions of the cell membrane. This oscillatory behavior indicates that UNC-40 clusters through a mechanism involving interlinked positive (formation) and negative (disassembly) feedback. We show that endogenous UNC-6 and ectopically provided UNC-6 orient and stabilize UNC-40 clustering. Furthermore, the UNC-40–binding protein MADD-2 (a TRIM family protein) promotes ligand-independent clustering and robust UNC-40 polarization toward UNC-6. Together, our data suggest that UNC-6 (netrin) directs polarized responses by stabilizing UNC-40 clustering. We propose that ligand-independent UNC-40 clustering provides a robust and adaptable mechanism to polarize toward netrin.

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Alammari, Amr A., Mohd Sharique, Athar Ali Moinuddin, and Mohammad Samar Ansari. "Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels." Electronics 11, no. 17 (September 1, 2022): 2757. http://dx.doi.org/10.3390/electronics11172757.

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Cell-free large-scale multi-user MIMO is a promising technology for the 5G-and-beyond mobile communication networks. Scalable signal processing is the key challenge in achieving the benefits of cell-free systems. This study examines a distributed approach for cell-free deployment with user-centric configuration and finite fronthaul capacity. Moreover, the impact of scaling the pilot length, the number of access points (APs), and the number of antennas per AP on the achievable average spectral efficiency are investigated. Using the dynamic cooperative clustering (DCC) technique and large-scale fading decoding process, we derive an approximation of the signal-to-interference-plus-noise ratio in the criteria of two local combining schemes: Local-Partial Regularized Zero Forcing (RZF) and Local Maximum Ratio (MR). The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity. The results also show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes because the computational complexity of the Local-Partial RZF is independent of the UTs. Therefore, it does not grow as the number of user terminals (UTs) increases.

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Peters, Georg, and Richard Weber. "DCC: a framework for dynamic granular clustering." Granular Computing 1, no. 1 (February 4, 2016): 1–11. http://dx.doi.org/10.1007/s41066-015-0012-z.

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FOWLER, ANNA, VILAS MENON, and NICHOLAS A. HEARD. "DYNAMIC BAYESIAN CLUSTERING." Journal of Bioinformatics and Computational Biology 11, no. 05 (October 2013): 1342001. http://dx.doi.org/10.1142/s0219720013420018.

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Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions.

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Dai, Yun, and Hao Liu. "Application of the R-Tree Clustering Model in Medical Information Retrieval." Mobile Information Systems 2022 (August 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/2887395.

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Hospitals produce a large amount of medical information every day. In the face of medical big data, the existing data processing methods cannot meet expectations and need to be continuously optimized. In the database system, when the stored objects are very large, and then the efficiency of data retrieval is a major bottleneck, therefore restricting the application of medical information. For that reason and to improve the efficiency of information retrieval, it is necessary to add an index to the information object and filter the dataset participating in the connection retrieval through the index. In this paper, an information retrieval technique grounded on the R-tree clustering model index is proposed for massive hospital information. The R-tree clustering model is constructed in massive hospital information by using the dynamic determination clustering center (DCC) algorithm. Finally, the superiority of the method is proved by simulations. The experiments and empirical evaluation show that the proposed R-tree clustering model index significantly improves data retrieval efficiency.

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Stavoe, Andrea K. H., and Daniel A. Colón-Ramos. "Netrin instructs synaptic vesicle clustering through Rac GTPase, MIG-10, and the actin cytoskeleton." Journal of Cell Biology 197, no. 1 (March 26, 2012): 75–88. http://dx.doi.org/10.1083/jcb.201110127.

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Netrin is a chemotrophic factor known to regulate a number of neurodevelopmental processes, including cell migration, axon guidance, and synaptogenesis. Although the role of Netrin in synaptogenesis is conserved throughout evolution, the mechanisms by which it instructs synapse assembly are not understood. Here we identify a mechanism by which the Netrin receptor UNC-40/DCC instructs synaptic vesicle clustering in vivo. UNC-40 localized to presynaptic regions in response to Netrin. We show that UNC-40 interacted with CED-5/DOCK180 and instructed CED-5 presynaptic localization. CED-5 in turn signaled through CED-10/Rac1 and MIG-10/Lamellipodin to organize the actin cytoskeleton in presynaptic regions. Localization of this signaling pathway to presynaptic regions was necessary for synaptic vesicle clustering during synapse assembly but not for the subcellular localization of active zone proteins. Thus, vesicle clustering and localization of active zone proteins are instructed by separate pathways downstream of Netrin. Our data indicate that signaling modules known to organize the actin cytoskeleton during guidance can be co-opted to instruct synaptic vesicle clustering.

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Kaneko, Kunihiko, and Tetsuya Yomo. "Cell division, differentiation and dynamic clustering." Physica D: Nonlinear Phenomena 75, no. 1-3 (August 1994): 89–102. http://dx.doi.org/10.1016/0167-2789(94)90277-1.

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Yan, Kejia, Huqin Yan, and Rakesh Gupta. "Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?" Journal of Risk and Financial Management 15, no. 3 (March 1, 2022): 113. http://dx.doi.org/10.3390/jrfm15030113.

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This paper examines the dynamic conditional correlations among 10 cryptocurrencies and the possibility of hedging investment strategies among multiple cryptocurrencies over the period affected by COVID-19 from 2017 to 2022. After studying the relationship between Bitcoin, Ethereum, and the other eight cryptocurrencies, four main results were obtained in this paper: first, from the pre-COVID-19 period to the COVID-19 period, almost all of the cryptocurrencies’ return growth rates increased, and COVID-19 had a positive effect on the returns of cryptocurrencies. Second, all of the cryptocurrencies’ return indices had features of volatility clustering and memory persistence in the long run; from pre-COVID-19 to COVID-19, these cryptocurrencies’ GARCH values decreased, but the correlations among the varying GARCH values increased. Third, the varying correlations between the return indices of Bitcoin, Ethereum, and the other cryptocurrencies were very strong; from pre-COVID-19 to COVID-19, the average dynamic correlations between Bitcoin and the others increased. Fourth, Tether can be used as a hedge cryptocurrency against the other cryptocurrencies as COVID-19 enhanced its hedging feature.

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Ahmad, Burhan, Ole Gjølberg, and Mubashir Mehdi. "Spatial Differences in Rice Price Volatility: A Case Study of Pakistan 1994–2011." Pakistan Development Review 56, no. 3 (September 1, 2017): 265–89. http://dx.doi.org/10.30541/v56i3pp.265-289.

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Prices of agricultural commodities tend to be more volatile in comparison to other commodities. Volatility can result in inefficient allocation of the resources by the farmers, traders and consumers. Rice is the second major staple and export item of Pakistan. This study presents the trends in volatility of regional rice markets of Pakistan and analyses spatial differences in volatility across regional rice markets in Pakistan from 1994 to 2011, and also draws comparison of volatility with the international market. ARCH-LM tests are applied to check the presence of volatility and volatility clustering is found in all the markets. Tests for equality of variance and dynamic conditional correlations (DCC) GARCH model are employed to analyse the spatial differences across the regional rice markets of Pakistan. The results indicate the presence of spatial differences in volatility. Positive conditional correlations in the dynamic conditional correlations (DCC) GARCH model are found which indicate positive association of volatility across markets. Spatial differences in volatility and its persistence reflect the differences in market forces, infrastructure and information flow which leads to varying degree of risk across markets and some regions are exposed to higher risk. The study found out that Hyderabad and Sukkur are the most volatile markets and their volatility levels are highly persistent and require highest time to return to its long-term mean which makes them the riskiest rice markets. Investments in infrastructure, particularly in transportation and controlling the market power of middlemen may reduce price risk across markets particularly in the most risky markets. JEL Classification: C22, C32, Q11, Q13, Q18 Keywords: Rice Prices Volatility, Regional Markets, Pakistan. DCC-GARCH-models

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Raya, Elia, Huiqin Koerkel-Qu, Laura Rudhart, Lisa-Marie Köhler, Anna Damboeck, Christoph Irlbeck, Catherine Botteron, Melanie Werner-Klein, Stephan Seitz, and Christoph Klein. "Abstract 3788: EpCAM+ DCCs isolated from the bone marrow of breast cancer patients display high stemness and potency scores." Cancer Research 84, no. 6_Supplement (March 22, 2024): 3788. http://dx.doi.org/10.1158/1538-7445.am2024-3788.

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Abstract Metastatic dissemination of cancer cells from primary to distant sites often occurs early and it has been shown that their detection is linked to poor outcomes. Eradication of disseminated cancer cells (DCC) has therefore become the primary goal of adjuvant therapies. Since the phenotype of DCC is largely unknown, we set out to search, isolate, and molecularly characterize these candidate metastasis founder cells from bone marrow of breast cancer patients, years before manifestation of metastasis. We screened more than 200 bone marrow samples of breast cancer patients with no evident metastasis (UICC stage M0) for EpCAM-positive cells. EpCAM positive cells in the bone marrow of healthy donors were used as a negative control. In addition, we isolated DCC from breast cancer patients with manifest metastasis (UICC stage M1). We then performed single cell RNA sequencing after whole transcriptome amplification of the collected DCC and the healthy donor (HD) control cells. To confirm the malignant origin of picked DCCs, we projected the transcriptome data into the bone marrow atlas, inferred copy number alterations, and searched for mutations shared with the matched primary tumor. EpCAM-positive cells from control patients mostly comprised plasma cells. In contrast, DCCs derived from M0-stage breast cancer patient formed a unique, non-overlapping cluster in the bone marrow atlas. UMAP-clustering placed M1-stage DCC clearly separate from M0 stage DCC. Among the collected M0-stage DCCs, we identified at least three separate subclusters. Strikingly, M0-stage DCC displayed much higher stemness and potency scores than M1-stage DCCs, reminiscent of embryonic cells. DCCs, identified by EpCAM, display very high transcriptional stemness scores when isolated before metastatic manifestation. Upon expansion and proliferation, stemness scores are reduced indicating epithelial re-differentiation. The embryonic phenotype of M0-stage DCCs may reflect an early adaptive mechanism that enables DCC to survive in an ectopic environment and may impact on the metastatic potential of the DCC. Citation Format: Elia Raya, Huiqin Koerkel-Qu, Laura Rudhart, Lisa-Marie Köhler, Anna Damboeck, Christoph Irlbeck, Catherine Botteron, Melanie Werner-Klein, Stephan Seitz, Christoph Klein. EpCAM+ DCCs isolated from the bone marrow of breast cancer patients display high stemness and potency scores [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3788.

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Більше джерел

Дисертації з теми "Dynamic Cell Clustering (DCC)"

Stock, Antoine. "Simulatiοn exaflοpique de la cοmbustiοn de sprays". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR18.

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La simulation numérique des grandes échelles (LES, pour Large Eddy Simulation) s'est imposée comme un outil numérique puissant pour la conception et l'analyse des brûleurs de sprays, permettant de capturer avec une grande précision les interactions complexes entre l'écoulement turbulent, la combustion et la dynamique des sprays. Cependant, cette précision accrue de la LES s'accompagne d'un coût CPU élevé. Ces simulations nécessitent souvent l'utilisation de maillages de grande taille afin de résoudre les turbulences à petite échelle et les fronts de flamme nets, ainsi que la gestion de nombreuses espèces chimiques et réactions. Ces exigences posent des défis importants en termes de ressources informatiques et de temps de calcul, ce qui peut freiner l'application pratique de la LES dans les processus de conception industrielle. Cette thèse s'attaque aux défis numériques associés à la LES des brûleurs de sprays en explorant et développant des approches numériques visant à réduire coût de calcul sans compromettre la précision des simulations. Trois stratégies principales sont étudiées : l'équilibrage de charge Euler-Lagrange, l'adaptation dynamique de maillage, et le clustering dynamique des termes source
Large Eddy Simulation (LES) has emerged as a powerful computational tool for the design and analysis of spray burners, offering the ability to capture the complex interactions between turbulent flow, combustion, and spray dynamics with high fidelity. However, the high accuracy of LES comes at a significant computational cost. The simulation of these systems often requires the use of large meshes in order to resolve fine-scale turbulence and sharp flame front as well as the handling of numerous species and chemical reactions. These demands pose substantial challenges in terms of the required computational resources and the time required for the simulation process, which can hinder the practical application of LES in industrial design processes. This thesis addresses the computational challenges associated with LES of spray burners by exploring and developing numerical approaches aimed at reducing the computational burden without compromising the accuracy of the simulations. Three primary strategies are investigated: Euler-Lagrange load balancing, Adaptive Mesh Refinement, and Dynamic Cell Clustering

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Putri, Givanna Haryono. "Revealing the Development of Immune Response through Temporal Dynamic Clustering." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26874.

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The immune system is an integral part of our body and is responsible for keeping us healthy. For instance, under pathogenic invasion, an immune response is mounted to identify and clear the pathogens. The immune response is not always successful: whilst some individuals recover from a given infection, others do not. This has spurred efforts to better understand the process in the hope of finding effective health-promoting interventions. Cytometry is a key technology for quantifying the immune response, creating datasets that capture measurements of numerous characteristics of each individual cell in a biological sample. It has become apparent that the immune response encompasses a great many distinct cell types that all interact in a coordinated fashion across many organs, and that the process is highly dynamic over time. Understanding the immune response, and how and when to intervene, has emerged as a very challenging task. Importantly, it is a task that can be framed as a data science problem. Cytometry datasets are created at several time-points post-infection, or at different disease severity stages, and the task is to identify which immune cell populations are present, how they each evolve or vary, and to map this onto time/disease-stage. These immune response maps, once created, can assist clinicians in improving health outcomes. We firstly propose a novel density-based clustering and cluster tracking algorithm, ChronoClust, for tracking the temporal changes of cell populations in time-series of discrete cytometry datasets. We conduct a comprehensive qualitative and quantitative evaluation of ChronoClust’s performance on: (i) a synthetic dataset capturing the characteristics of an immune response as observed through temporal cytometry data, and (ii) a real cytometry dataset elucidating the immune response development in the bone marrow of West Nile Virus (WNV)-infected mice (WNV dataset). Our results demonstrate the ability of ChronoClust to cluster cells into cell populations and track their evolutions in an unsupervised and automated manner. We then investigate the potential of dimensionality reduction techniques to ease the computational burden of clustering and tracking temporal cytometry data whilst minimally diminishing the clustering and cluster tracking performance. We explore 3 dimensionality reduction techniques in conjunction with ChronoClust. To obtain a broad sample of clustering performances, the full and reduced WNV datasets are independently clustered 400 times using400 unique ChronoClust hyperparameter value sets. We conclude that for large unwieldy datasets, dimensionality reduction can prove advantageous if the computational expense is otherwise prohibitive. Many clustering algorithms now exist for clustering cytometry data into discrete cell populations. Comparative algorithm evaluations on benchmark datasets rely on either a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasise different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding the optimal clustering algorithms. We propose ParetoBench, a Pareto fronts based framework as an integrative evaluation protocol, wherein individual performance metrics are leveraged as complementary perspectives, and a broad systematic sampling of algorithms’ hyperparameter values is used to reveal how meticulously must those algorithms be tuned to obtain good clustering performance. We exemplify the protocol by comparing 3 clustering algorithms (ChronoClust, FlowSOM and Phenograph) using 4 performance metrics applied across 4 cytometry datasets. Next, we present TrackSOM, a fast and effective clustering and cluster tracking algorithm which: (1) combines the excellent clustering quality and fast run time of FlowSOM with the tracking capability of ChronoClust, and (2) includes visualisation methods to assist in exploring the uncovered immune response dynamic. TrackSOM encompasses several modes of operation to suit a variety of experimental contexts, spanning users possessing exact know-ledge of how many cell phenotypes their data contains versus those who are engaged in unguided exploration. We demonstrate TrackSOM’s capacity on both synthetic and real-world datasets, provide usage advice to users, and exemplify novel discovery through its application. For our real-world use-case, we characterise the immune response to WNV infection in mice, uncovering heterogeneous sub-populations of immune cells and relating their functional evolution to disease severity. We perform a parameter sensitivity analysis and demonstrate TrackSOM to have both an improved performance and lower sensitivity to parameter value selections over ChronoClust. Importantly, TrackSOM verifies the robustness and generasibility of the cytometry-specific cluster tracking approach developed under ChronoClust. Finally, we propose SOMInsight, a novel technique which combines TrackSOM with other computational techniques to compute a set of temporally dynamic immune features that reveal the dynamics of cell populations and discriminate clinical outcomes. Our qualitative evaluation demonstrates SOMInsight’s ability to uncover cell population changes that are consistent with previous biological findings. Furthermore, it also discovers new biological insight which warrants further biological experiments to corroborate. All computational techniques are publicly available as open source software to support their use by the community and promote reproducible results.

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KC, Rabi. "Study of Some Biologically Relevant Dynamical System Models: (In)stability Regions of Cyclic Solutions in Cell Cycle Population Structure Model Under Negative Feedback and Random Connectivities in Multitype Neuronal Network Models." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou16049254273607.

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Частини книг з теми "Dynamic Cell Clustering (DCC)"

Dobrzyński, Maciej, Marc-Antoine Jacques, and Olivier Pertz. "Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering." In Methods in Molecular Biology, 183–206. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2277-3_13.

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Guo, Mengli, Gang Chuai, Weidong Gao, and Yuhan Zhang. "Dynamic TDD Interference Mitigation Using Graph Theory Based Cell Clustering in 5G Ultra-Dense Network." In Lecture Notes in Electrical Engineering, 694–705. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6571-2_85.

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Parwez, Md Salik, Hasan Farooq, Ali Imran, and Hazem Refai. "Spectral Efficiency Self-Optimization through Dynamic User Clustering and Beam Steering." In Research Anthology on Developing and Optimizing 5G Networks and the Impact on Society, 79–94. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7708-0.ch005.

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This paper presents a novel scheme for spectral efficiency (SE) optimization through clustering of users. By clustering users with respect to their geographical concentration we propose a solution for dynamic steering of antenna beam, i.e., antenna azimuth and tilt optimization with respect to the most focal point in a cell that would maximize overall SE in the system. The proposed framework thus introduces the notion of elastic cells that can be potential component of 5G networks. The proposed scheme decomposes large-scale system-wide optimization problem into small-scale local sub-problems and thus provides a low complexity solution for dynamic system wide optimization. Every sub-problem involves clustering of users to determine focal point of the cell for given user distribution in time and space, and determining new values of azimuth and tilt that would optimize the overall system SE performance. To this end, we propose three user clustering algorithms to transform a given user distribution into the focal points that can be used in optimization; the first is based on received signal to interference ratio (SIR) at the user; the second is based on received signal level (RSL) at the user; the third and final one is based on relative distances of users from the base stations. We also formulate and solve an optimization problem to determine optimal radii of clusters. The performances of proposed algorithms are evaluated through system level simulations. Performance comparison against benchmark where no elastic cell deployed, shows that a gain in spectral efficiency of up to 25% is possible depending upon user distribution in a cell.

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Тези доповідей конференцій з теми "Dynamic Cell Clustering (DCC)"

Liu, Yi, and Bo Li. "User Mobility-Based Dynamic Clustering Algorithm for Cell-Free Massive MIMO Systems." In 2024 7th International Conference on Electronics Technology (ICET), 778–83. IEEE, 2024. http://dx.doi.org/10.1109/icet61945.2024.10672692.

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Audivet Durán, Cinthia, and Marco E. Sanjuán. "On-Line Early Fault Detection of a Centrifugal Chiller Based on Data Driven Approach." In ASME 2016 10th International Conference on Energy Sustainability collocated with the ASME 2016 Power Conference and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/es2016-59291.

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A district cooling system (DCS) is a system that distributes thermal energy through chilled water from a central source to residential, commercial, or industrial consumers, designated to air conditioning purposes. It is one of the most important part of a heating, ventilation, air conditioning and refrigeration systems (HVAC), because a DCS is composed of: Cooling towers, central chiller plant, water distribution systems and clusters of consumer buildings. This research is focused on the central chiller plant, due to it accounts for a substantial portion of the total energy consume of DCS and HVAC systems. The performance of central chiller plant is often affected by multiple faults which could be caused during installation or developed in routine operation. These non-optimal conditions and faults may cause 20–30% waste of energy consumption of HVAC&R systems. Automated fault detection and diagnosis (AFDD) tools have potential to detect an incipient fault and help to reduce undesirable conditions and energy consumption, and optimize the facility maintenance. We propose an online data driven fault detection strategy for district cooling system. The main objective is to develop an automated fault detection tool based on historical process data, which can be applied in transient operation. The proposed hybrid strategy is based on unsupervised and supervised learning techniques, and multivariate statistic techniques. Its aim is to identify the operating states of the chiller and evaluate the fault occurrence depending of its current operating state. This strategy uses the K-means clustering method, Naive Bayes classifier and Principal Component Analysis (PCA). The developed strategy was evaluated using the performance data of a 90-ton water-cooled centrifugal chiller (ASHRAE RP-1043) and also evaluated using a dynamic model of a chiller (Simscape™.) under similar conditions. The results show the advantages of novel early fault detection technique compared to Conventional PCA method in terms of sensitivity to faults occurrence and reduction of missed detection rate.

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Savazzi, Pietro, and Lorenzo Favalli. "Dynamic Cell Sectorization Using Clustering Algorithms." In 2007 IEEE 65th Vehicular Technology Conference. IEEE, 2007. http://dx.doi.org/10.1109/vetecs.2007.135.

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Zimmermann, Hans-martin, Alexander Seitz, and Ruediger Halfmann. "Dynamic Cell Clustering in Cellular Multi-Hop Networks." In 2006 10th IEEE Singapore International Conference on Communication Systems. IEEE, 2006. http://dx.doi.org/10.1109/iccs.2006.301458.

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Huan Sun, Xiaobo Zhang, and Wei Fang. "Dynamic cell clustering design for realistic coordinated multipoint downlink transmission." In 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2011). IEEE, 2011. http://dx.doi.org/10.1109/pimrc.2011.6139718.

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Samarakoon, Sumudu, Mehdi Bennis, Walid Saad, and Matti Latva-aho. "Dynamic clustering and sleep mode strategies for small cell networks." In 2014 11th International Symposium on Wireless Communications Systems (ISWCS). IEEE, 2014. http://dx.doi.org/10.1109/iswcs.2014.6933487.

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Wang, Xiaoyu, Shi Jin, Xiqi Gao, Wen Zhong, Guo Li, and Tianle Deng. "User Rate Evaluation of Dynamic Clustering in Homogeneous Small Cell Networks." In 2013 IEEE 78th Vehicular Technology Conference (VTC Fall). IEEE, 2013. http://dx.doi.org/10.1109/vtcfall.2013.6692452.

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Dai, Wei, Siyuan Yang, Mondher Bouazizi, and Tomoaki Ohtsuki. "K-Means Clustering-Aided Dynamic Multi-Cell Optimization Algorithm for HAPS." In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437302.

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Yan, Yan, Kai Niu, Ping Gong, and Zhiqiang He. "Base station energy saving based on dynamic programming combined with cell clustering." In 2013 IEEE 5th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE). IEEE, 2013. http://dx.doi.org/10.1109/mape.2013.6689966.

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Dghais, Wael, Malek Souilem, Hao Ran Chi, Ayman Radwan, and Abd-Elhamid M. Taha. "Dynamic Clustering for Power Effective Small Cell Deployment in HetNet 5G Networks." In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE, 2020. http://dx.doi.org/10.1109/icc40277.2020.9149059.

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