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Journal articles on the topic 'Zonal clustering'
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CHEN, YUANZHU PETER, and ARTHUR L. LIESTMAN. "A ZONAL ALGORITHM FOR CLUSTERING AN HOC NETWORKS." International Journal of Foundations of Computer Science 14, no. 02 (2003): 305–22. http://dx.doi.org/10.1142/s0129054103001741.
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A Mobile Ad Hoc Network (MANET) is an infrastructureless wireless network that can support highly dynamic mobile units. The multi-hop feature of a MANET suggests the use of clustering to simplify routing. Graph domination can be used in defining clusters in MANETs. A variant of dominating set which is more suitable for clustering MANETs is the weakly-connected dominating set. A cluster is defined to be the set of vertices dominated by a particular vertex in the dominating set. As it is NP-complete to determine whether a given graph has a weakly-connected dominating set of a particular size, we present a zonal distributed algorithm for finding small weakly-connected dominating sets. In this new approach, we divide the graph into regions, construct a weakly-connected dominating set for each region, and make adjustments along the borders of the regions to produce a weakly-connected dominating set of the entire graph. We present experimental evidence that this zonal algorithm has similar performance to and provides better cluster connectivity than previous algorithms.
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Mehra, Pawan Singh, M. N. Doja, and Bashir Alam. "Zonal based approach for clustering in heterogeneous WSN." International Journal of Information Technology 11, no. 3 (2017): 507–15. http://dx.doi.org/10.1007/s41870-017-0071-2.
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Jaffri, Zain ul Abidin, Muhammad Asif, Wali Ullah Khan, et al. "TEZEM: A new energy-efficient routing protocol for next-generation wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 6 (2022): 155013292211072. http://dx.doi.org/10.1177/15501329221107246.
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The design and implementation of energy-efficient routing protocols for next-generation wireless sensor networks is always a challenge due to limited power resource capabilities. Hierarchical (clustering) routing protocols appeared to be a remarkable solution for extending the lifetime of wireless sensor networks, particularly in application-aware (threshold-sensitive) and heterogeneity-aware cluster-based routing protocols. In this article, we propose a protocol, namely, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol. It is a heterogeneity-aware and threshold-based protocol that provides a better solution to existing problems in next-generation wireless sensor networks. During execution, the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol splits the entire network area into several zones to manage network traffic efficiently. In the first step, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is designed for a homogeneous network where the initial energy of all the nodes is the same. Thereafter, we bring in heterogeneity in the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol execution environment to optimize its energy consumption. By investigating the performance of the various numbers of divisions, it is proved that the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol with 9 zonal divisions has higher stability and throughput. The performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is compared with those of Stable Election Protocol, Low-Energy Adaptive Clustering Hierarchy, Modified Low-Energy Adaptive Clustering Hierarchy, and Gateway-Based Energy-Efficient Routing Protocol through computer simulations. Simulation results verify the improved performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol in terms of network stability, lifetime, and throughput.
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Kaur, Prabhleen, and Rajdeep Singh. "Zonal based Deterministic Energy Efficient Clustering Protocol for WSNs." International Journal of Computer Applications 109, no. 10 (2015): 1–5. http://dx.doi.org/10.5120/19221-0571.
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Harbouche, Ahmed, Djamal Djabour, and Amine Saiah. "Z-MSP: Zonal-Max Stable Protocol for Wireless Sensor Networks." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18036–41. https://doi.org/10.48084/etasr.8691.
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Clustering is a well-known energy enhancement approach used to prolong the lifetime of Wireless Sensor Networks (WSNs). However, it introduces another issue, which is the selection of the optimum number of clusters along with the appropriate cluster heads. In this paper, we study in detail the clustering approach and its impact on enhancing WSN lifetime. We provide a mathematical study that discusses the impact of clustering, where the WSN is divided into multiple zones, each zone functioning as an independent cluster. The WSN topology consists of 10 zones, all similar in area and density but differing in their distances to the base station. To prolong the WSN’s lifetime, we developed Z-MSP, an extension of MSP for Zonal WSNs. It maintains the highest stable period of MSP for the Z-WSN. Z-MSP prolongs the network's stable period by 315.625%, 315.625%, and 287.258%, and the lifetime by 245.340%, 237.277%, and 232.475%, with a very high throughput level compared to FBECS, E-CAFL, and LEACH-FC, respectively.
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Poyrazoglu, Gokturk. "Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey." Energies 14, no. 4 (2021): 1014. http://dx.doi.org/10.3390/en14041014.
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In the electricity market, different pricing models can be applied to increase market competitiveness. Different electricity systems use different market structures. Uniform marginal pricing, zonal marginal pricing, and nodal marginal pricing methods are commonly used market structures. For markets wishing to move from a uniform pricing structure to a more competitive zonal pricing structure, the determination of price zones is critical for achieving a competitive market that generates accurate price signals. Three different pricing zone detection algorithms are analyzed in this paper including the k-means clustering and queen/rook spatially constraint clustering. Finally, the results of a case study for the Turkish electricity system are shared to compare each method.
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Ngaruyintwari, Severin, and Isabelle zuzu Iragena Marie. "Enhanced Clustering Protocol in Zonal-Stable Election Protocol for WSN." International Journal of Scientific Research and Modern Technology 2, no. 2 (2022): 5. https://doi.org/10.5281/zenodo.6373115.
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Wireless sensors network is an environment where is deployed by wireless sensor nodes and base station, those sensor nodes are eligible to receive and transmit data to the base station. Base station is able to reconvert data from sensor nodes into readable data. This phenomenon of sensing data packet needs energy and the energy resource are limited thus reduce network lifetime, because of that, researcher are continue to invent the way to improve energy efficient in wireless sensor nodes. In this paper we proposed an enhanced clustering protocol, we based on clustered routing protocol where in network field there are normal nodes and advanced nodes, normal nodes send data directly to base station while advanced nodes use clustering technique to send data to base station. The residual energy in cluster election is also enhanced in order to increase the lifetime of network. In Z-SEP there are three zones, with three zones some of sensor nodes will be deployed far away from base station and during rounds at least two cluster head will work simultaneously, thus resulting with much energy consumption, we solved this problem by reducing zones and we design network field with two zones, this lengthen a little more network life time.
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Ranjith Kumar, Vijayakumar Varadarajan, Bishwajeet Kumar,. "ZSGEEHCP: Zonal Stable Gateway Based Energy Efficient Heterogeneous Clustering Protocol For WSN- Based IoT." Journal of Electrical Systems 20, no. 5s (2024): 39–54. http://dx.doi.org/10.52783/jes.1831.
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In future most of the devices on the earth connected to the internet for making world of intelligent networks. The internet of things (IoT) and Industrial IoT play a very significant role for implementing such kind of smart networks. So, in that case, the transmission of packets of data sent to the base station from sensor nodes with minimum energy consumption in a homogeneous or heterogeneous environment to hike the stability of the network appears as a big challenge for IoT-based networks. But, such kind of problem can be overcome by using a proper clustering algorithm. In this work, different kinds of classical clustering algorithms are studied precisely. Most of the traditional clustering algorithm protocols presume that all the nodes have the same amount of energy, they are unable to fully exploit the presence of node heterogeneity and the same problem will be faced with IoT-based networks. In this specific piece of work, we recommend a gateway based heterogeneous energy-based clustering algorithm which is crucial for many wireless sensor networks along with IoT-based applications. In this concept geographical area is divided into three zone, Advanced, Intermediate and normal zone. Here, advance nodes send data through gateway to the base station while intermediate node use clustering algorithm for transferring data and normal nodes communicate data to the base station directly. ZSGHCP is driven by the mounded election protocol probabilities of each node elected as a cluster head depending on the rest of the energy. By simulation, we show that ZSGHCP invariably extends the stability time as compared to current clustering procedures. Finally, we investigate the sensitivity of our ZSGHCP protocol to network heterogeneity characteristics that capture energy imbalance, throughput, and no of alive nodes. We construct that ZSGHCP payout extends the durability region for excessive values of additional energy brought by extra higher-powered nodes.
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Raoofat, Mahdi, and Navid Eghtedarpour. "A modified fuzzy clustering algorithm for market zonal partitioning in electricity markets." International Transactions on Electrical Energy Systems 23, no. 4 (2012): 526–38. http://dx.doi.org/10.1002/etep.1610.
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Blachnik, Marcin, Karol Wawrzyniak, and Marcin Jakubek. "Partitioning Power Grid for the Design of the Zonal Energy Market While Preserving Control Area Constraints." Electronics 10, no. 5 (2021): 610. http://dx.doi.org/10.3390/electronics10050610.
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The use of a zonal structure for energy markets across the globe is expanding; however the debate on how to effectively partition the grid into bidding zones is still open for discussion. One of the factors that needs to be addressed in the process of bidding zones’ delimitation is the transmission system operators control areas. Merging parts of different control areas into one bidding zone can lead to multiple problems, ranging from political, through grid security concerns, to reserve control issues. To address it, this paper presents a novel grid partitioning method aimed at bidding zones delimitation that is based on clustering the power grid using an extended version of the standard agglomerative clustering. The proposed solution adds additional clustering rules when constructing the dendrogram in order to take into account the control areas. The algorithm is applied to the data which represents the locational marginal prices obtained from optimal power flow analysis.
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Rajabhushanam, C. "Clustering data analytics of urban land use for change detection." Computing and Artificial Intelligence 2, no. 2 (2024): 570. http://dx.doi.org/10.59400/cai.v2i2.570.
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In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.
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Rajabhushanam, C. "Clustering data analytics of urban land use for change detection." Computing and Artificial Intelligence 2, no. 1 (2024): 570. http://dx.doi.org/10.59400/cai.v2i1.570.
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In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.
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Xavier, Anupama K., Jonathan Demaeyer, and Stéphane Vannitsem. "Variability and predictability of a reduced-order land–atmosphere coupled model." Earth System Dynamics 15, no. 4 (2024): 893–912. http://dx.doi.org/10.5194/esd-15-893-2024.
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Abstract. This study delves into the predictability of atmospheric blocking, zonal, and transition patterns utilizing a simplified coupled model. This model, implemented in Python, emulates midlatitude atmospheric dynamics with a two-layer quasi-geostrophic channel atmosphere on a β plane, encompassing simplified land effects. Initially, we comprehensively scrutinize the model's responses to environmental parameters like solar radiation, surface friction, and atmosphere–ground heat exchange. Our findings confirm that the model faithfully replicates real-world Earth-like flow regimes, establishing a robust foundation for further analysis. Subsequently, employing Gaussian mixture clustering, we successfully delineate distinct blocking, zonal, and transition flow regimes, unveiling their dependencies on surface friction. To gauge predictability and persistence, we compute the averaged local Lyapunov exponents for each regime. Our investigation uncovers the presence of zonal, blocking, and transition regimes, particularly under conditions of reduced surface friction. As surface friction increases further, the system transitions to a state characterized by two blocking regimes and a transition regime. Intriguingly, periodic behavior emerges under specific surface friction values, returning to patterns observed under low friction coefficients. A model resolution increase impacts the system in such a way that only two regimes are then obtained with the clustering: the transition phase disappears and the predictability drops to roughly 2 d for both of the remaining regimes. In accordance with previous research findings, our study underscores the fact that when all three regimes coexist, zonal patterns exhibit a more extended predictability horizon compared to blocking patterns. Remarkably, transition patterns exhibit reduced predictability when coexisting with the other regimes. In addition, within a specified range of surface friction values where two blocking regimes are found, it is observed that blocked atmospheric situations in the west of the applied topography are marked by instabilities and reduced predictability in contrast to the blockings appearing on the eastern side of the topography.
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He, Jin Guo. "A Novel Threshold Selection Method Based on Iterative Clustering Strategy." Applied Mechanics and Materials 433-435 (October 2013): 288–96. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.288.
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This paper extends our previous algorithm for clustering. This previous algorithm works fine on simulated data. It can acquire satisfactory clustering results even with annular or zonal simulated data by causing the data to shrink within a cluster. To make use of the advantages of the previous algorithm, a one-dimensional (1D) histogram is mapped to a two-dimensional (2D) image and can be clustered by the previous algorithm, thus leading to stable results of histogram thresholds. The shrinking procedures of the 2D image or the 1D histogram are given, and a new parameter strategy is discussed.
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Maier, Jonas, and Hans-Christian Reuss. "Design of Zonal E/E Architectures in Vehicles Using a Coupled Approach of k-Means Clustering and Dijkstra’s Algorithm." Energies 16, no. 19 (2023): 6884. http://dx.doi.org/10.3390/en16196884.
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Electromobility and autonomous driving has started a transformation in the automotive industry, resulting in new requirements for vehicle systems. Due to its functions, the electrical/electronic (E/E) architecture is one of the essential systems. Zonal E/E architecture is a promising approach to tackle this issue. The research presented in this paper describes a methodology for determining the optimal number of zones, the position of the zone control units (ZCU), and the assignment of electric components to these zones and ZCUs. Therefore, the design of the power supply and the wiring harness is essential. This approach aims to identify the most suitable system architecture for a given vehicle geometry and a set of electric components. For this purpose, the assignment of electric components is accomplished by k-means clustering, and Dijkstra’s algorithm is used to optimize the cable routing. As ZCUs will be the hubs for the in-vehicle data and information transport in zonal architectures, their position and their number are crucial for the architecture and wiring harness development. Simulations show a suitable zonal architecture reduces wiring harness length as well as weight and brings functional benefits. However, the number of zones must be chosen with care, as there may also be functional limitations.
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Sivaraman, Perumal, Dr. Prabaharan G, and Dr Senthil Kumar R. "The Natural Language Processing Axioms in Classical Tamil for Zonal Dialects using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 14, no. 6 (2025): 36–44. https://doi.org/10.35940/ijitee.f1096.14060525.
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Studying Natural Language Processing (NLP) for Classical Tamil and its Zonal Dialects using Machine Learning (ML) involves unique challenges and opportunities. Classical Tamil, one of the oldest languages with a rich literary heritage, differs significantly in syntax, semantics, and phonetics from its modern dialects. Addressing these differences requires incorporating linguistic axioms and cultural nuances into NLP systems. This deals with Tamil letters, Challenges, Future directions, and Lexical Differences. It also includes parsers and tokenisation. Lists the differences between Morphemes, Bounded Morphemes in terms of Tamil as a Natural Language processing—dictionary form of the words used in Lemmatizations. Stemming reduces the words, and Tamil is represented as a short sentence, comparing the differences in the Tamil dialect taken and represented. The methodology used is Clustering algorithms, which can group zonal dialects based on phonetic and semantic similarities using a Naïve Bayes classifier. We are using speechto-text to identify the Tamil dialect. This zonal dialect is essential in entertainment, education, information, and business. More Exploration can be done using Zonal dialects in Classical Tamil. Machine learning plays a role in classification, Grouping, and Segmenting Natural Language processing. We have different dialects in the Single Language Tamil for a single word in Natural Language Processing. Encourages local people to communicate fluently in terms of transactions. Preserving local traditions and customs is the advantage of Zonal Dialects. It can be used in interviews, recordings, written and spoken texts, and debates. Linguistic Diversity, preservation of History, and Cultural Identity are the significant concerns in Zonal dialects using classical Tamil.
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Perumal, Sivaraman. "The Natural Language Processing Axioms in Classical Tamil for Zonal Dialects using Machine Learning." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 6 (2025): 36–44. https://doi.org/10.35940/ijitee.F1096.14060525/.
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<strong>Abstract:</strong> Studying Natural Language Processing (NLP) for Classical Tamil and its Zonal Dialects using Machine Learning (ML) involves unique challenges and opportunities. Classical Tamil, one of the oldest languages with a rich literary heritage, differs significantly in syntax, semantics, and phonetics from its modern dialects. Addressing these differences requires incorporating linguistic axioms and cultural nuances into NLP systems. This deals with Tamil letters, Challenges, Future directions, and Lexical Differences. It also includes parsers and tokenisation. Lists the differences between Morphemes, Bounded Morphemes in terms of Tamil as a Natural Language processing—dictionary form of the words used in Lemmatizations. Stemming reduces the words, and Tamil is represented as a short sentence, comparing the differences in the Tamil dialect taken and represented. The methodology used is Clustering algorithms, which can group zonal dialects based on phonetic and semantic similarities using a Naïve Bayes classifier. We are using speechto-text to identify the Tamil dialect. This zonal dialect is essential in entertainment, education, information, and business. More Exploration can be done using Zonal dialects in Classical Tamil. Machine learning plays a role in classification, Grouping, and Segmenting Natural Language processing. We have different dialects in the Single Language Tamil for a single word in Natural Language Processing. Encourages local people to communicate fluently in terms of transactions. Preserving local traditions and customs is the advantage of Zonal Dialects. It can be used in interviews, recordings, written and spoken texts, and debates. Linguistic Diversity, preservation of History, and Cultural Identity are the significant concerns in Zonal dialects using classical Tamil.
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Benelhouri, Abdelkader, Hafida Idrissi-Saba, and Jilali Antarir. "Three Level Heterogeneous Zonal Stable Election Protocol for Energy Efficiency in Wireless Sensor Networks." E3S Web of Conferences 297 (2021): 01075. http://dx.doi.org/10.1051/e3sconf/202129701075.
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Sensor battery limitation has always been the most challenging hurdle for wireless sensor networks. Many energy effcient routing protocols have been proposed to overcome this issue in homogeneous networks where sensor nodes start with the same initial energy. When sensor nodes have different amount of initial energy, the network is heterogeneous and it becomes complicated to design an energy effcient routing protocol to save nodes energy and prolong network lifetime. Herein, we propose a three level heterogeneous routing protocol to boost network stability period of wireless sensor networks. The network model splits up into five zones according to nodes initial energies and distance to base station. For data communication, the proposed model relies on two types of communications: Direct and Multi-Hop. The choice of the type of communication is made according to nodes initial energy and their distance to the base station. The clustering scheme is used just in the zones that contains nodes with higher energies. The simulation of our proposed scheme is done using Matlab simulator and the results are compared to the conventional heterogeneous routing protocols 3-level heterogeneous Stable Election Protocol and 3-level Modified Low Energy Adaptive Clustering Hierarchy.
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Perumal, Sivaraman. "The Natural Language Processing Axioms in Classical Tamil for Zonal Dialects using Machine Learning." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 6 (2025): 36–44. https://doi.org/10.35940/ijitee.F1096.14060525.
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<strong>Abstract:</strong> Studying Natural Language Processing (NLP) for Classical Tamil and its Zonal Dialects using Machine Learning (ML) involves unique challenges and opportunities. Classical Tamil, being one of the oldest languages with a rich literary heritage, differs significantly in syntax, semantics, and phonetics from its modern dialects. Addressing these differences requires incorporating linguistic axioms and cultural nuances into NLP systems. This deals with Tamil letters, Challenges, Future directions, and Lexical Differences. It also includes parsers, tokenisation. Lists the differences between Morphemes, Bounded Morphemes in terms of Tamil as a Natural Language processing—dictionary form of the words used in Lemmatizations. Stemming is used to reduce the words and Tamil represented as a short sentence. Comparison of differences in the dialect of Tamil taken and represented. The methodology used is Clustering algorithms can group zonal dialects based on phonetic and semantic similarities using a Naïve Bayes classifier. We are using Speech to Text for identifying the Tamil dialect. This zonal dialect plays an important role in Entertainment, education, Information, and Business purposes. More Exploration can be done using Zonal dialects in Classical Tamil. Machine learning plays a role in classification, Grouping, and Segmenting Natural Language processing. For a single word in Natural Language processing, we have different dialects in the Single Language Tamil. Encourages local people to communicate fluently in terms of transactions. Preserving local traditions and customs is the advantage of Zonal Dialects. It can be used in interviews, recordings, written and spoken texts, and debates. Linguistic Diversity, preservation of History, and Cultural Identity are the major concerns in the field of Zonal dialects using classical Tamil.
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Akhtar, Fazia, Ijaz Ahmed, Ahmed F. Youssef, Idris H. Smaili, Mohamed Mostafa Ramdan Ahmed, and Ali M. El-Rifaie. "Advance zonal rectangular low energy adaptive clustering hierarchy algorithm for optimal routing in wireless sensors network." PLOS One 20, no. 5 (2025): e0321938. https://doi.org/10.1371/journal.pone.0321938.
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In wireless sensor networks (WSNs), power consumption is a recurring issue. Compared to other modern routing approaches that aim to reduce power consumption, cluster-based forwarding algorithms have been shown to be more energy efficient. Static clustering optimization is the main emphasis of this study on energy-efficient advanced zonal rectangular low energy adaptive clustering hierarchy (EE-AZR-LEACH) optimum routing, which takes a modern technique. To extend the lifespan of the cluster units and the system, we suggest using the multi-hopping approach. The proposed protocol significantly improves the network life time and energy efficiency of WSNs by optimizing static clustering and incorporating multi-hopping techniques. It can outperform existing protocols in power consumption, data transfer and stability, makes it a robust solution for large-scale and energy constrained environment. To help the Cluster Heads (CHs) with data transmission, EE-AZR-LEACH chose a Collaborator(CL) close to the central cluster. To increase the effectiveness of communication between the CHs located in the rectangular region and a central base station, these units took on the role of cluster leaders. The resilience, data transmission rate, power consumption, network endurance, and number of CHs of the system were clearly improved as a consequence. Our suggested routing system performs more effectively than AZR-LEACH, LEACH, MH-LEACH, and SEP in substantial areas. Furthermore, the proposed approach exhibits better convergence within 600 rounds when compared to AZR-LEACH, LEACH, MH-LEACH, and SEP. The findings indicate that after 1500 simulation cycles, the stability intervals for LEACH, MH-LEACH, SEP, and AZR-LEACH are 2.7%, 7.2%, 4.14%, and 5.34%, respectively. The simulation is run using MATLAB. The EE-AZR-LCH optimum routing, on the other hand, has a 6.8% survival rate. The MH-LEACH optimum routing has smaller total network tenure even if it provides a higher stability period than the EE-AZR-LEACH.
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Shi, Zhiwei, Guangzeng You, Linfu Miu, et al. "Zonal Planning for a Large-Scale Distribution Network Considering Reliability." Processes 13, no. 2 (2025): 354. https://doi.org/10.3390/pr13020354.
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To achieve the optimal planning of grid resource storage for a large-scale distribution network (DN), a cluster partition-based zonal planning method for the DN, considering reliability, is proposed. Firstly, a comprehensive clustering partition index is proposed, which includes the modularity index, power balance index, and node affiliation index. A hybrid genetic–simulated annealing algorithm is employed to perform the cluster partition. Secondly, a three-layer joint expansion planning model based on cluster partitioning is proposed. At the upper level, a route planning model is established to optimize the routing of the cluster. At the intermediate level, a location and capacity planning model for distributed photovoltaics and energy storage is formulated, taking uncertainties into account. By introducing uncertainty parameters, the range of uncertainty for sources and loads is characterized. At the lower level, reliability indices within the clusters are calculated to ensure operational reliability while reducing the conservatism of the optimization outcomes. Finally, the proposed method is applied to a real distribution network in China, demonstrating its effectiveness in improving the economic efficiency of DN planning.
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Rao, Sanapala Shanmukha, Shilpi, and Arvind Kumar. "Performance Evaluation of a Zone-based Three-level Heterogeneous Clustering Protocol for WSNs." Journal of Telecommunications and Information Technology, no. 3 (September 11, 2023): 85–93. http://dx.doi.org/10.26636/jtit.2023.3.1381.
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This paper proposes a zone-based three-level heterogeneous clustering protocol (ZB-TLHCP) for heterogeneous WSNs. In ZB-TLHCP, the sensor field/region is divided into zones where super, advance, and normal nodes are deployed uniformly and randomly. The performance of the proposed ZB-TLHCP system is compared with that of zonal-stable election protocol (Z-SEP), distributed energy efficient clustering (DEEC), and threshold-based DEEC (TDEEC) protocol by varying the number of super and advance nodes, their energy levels for the fixed sensor field, and the total number of nodes. Matlab simulation results revealed that the proposed ZB-TLHCP solution performed better than Z-SEP, DEEC, and TDEEC protocols, as it increased the instability period, prolonged the network's lifetime, and achieved higher throughput values.
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Esterl, Katharina, Carlos Andrés Epia Realpe, and Ulf Philipp Müller. "Avoiding false inter-zonal meshing in the clustering of a large-scale German power grid." Energy Strategy Reviews 56 (November 2024): 101569. http://dx.doi.org/10.1016/j.esr.2024.101569.
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Alom, Md Khorshed, Arif Hossan, and Pallab K. Choudhury. "Improved Zonal Stable Election Protocol (IZ-SEP) for hierarchical clustering in heterogeneous wireless sensor networks." e-Prime - Advances in Electrical Engineering, Electronics and Energy 2 (2022): 100048. http://dx.doi.org/10.1016/j.prime.2022.100048.
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Rogers, A. W., S. E. Cisewski, and C. B. Kern. "The Zonal Architecture of the Mandibular Condyle Requires ADAMTS5." Journal of Dental Research 97, no. 12 (2018): 1383–90. http://dx.doi.org/10.1177/0022034518777751.
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Temporomandibular joint (TMJ) osteoarthritis (TMJOA) disrupts extracellular matrix (ECM) homeostasis, leading to cartilage degradation. Upregulated a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)–5 leads to cleavage of its substrate aggrecan (Acan) and is considered a hallmark of TMJOA. However, most research on ADAMTS5-Acan turnover has focused on hyaline cartilage, not fibrocartilage, which comprises the TMJ. The mandibular condylar cartilage (MCC) of the TMJ is organized in zones, and chondrocytes are arranged in axial rows, yet the molecular mechanisms required to generate the MCC zonal architecture have not been elucidated. Here, we test the hypothesis that ADAMTS5 is required for development of the TMJ MCC. Adamts5+/+ and Adamts5–/– murine TMJs were harvested at postnatal day 7 (P7), P21, 2 mo, and 6 mo of age; histomorphometrics indicated increased ECM. Immunohistochemistry and Western blots demonstrated the expanded ECM correlated with increased Acan localization in Adamts5–/– compared to Adamts5+/+. Cell volume was also decreased in the MCC of Adamts5–/– due to both a reduction in cell size and less mature hypertrophic chondrocytes. Analysis of chondrogenic maturation markers by quantitative real-time polymerase chain reaction indicated Col2a1, Col10a1, and Sox9 were significantly reduced in Adamts5–/– MCC compared to that of Adamts5+/+. The older (6 mo) Adamts5–/– MCC exhibited changes in chondrogenic cell arrangements, including clustering and chondrogenic atrophy, that correlated with early stages of TMJOA using modified Mankin scoring. These data indicate a potentially novel and critical role of ADAMTS5 for maturation of hypertrophic chondrocytes and establishment of the zonal architecture that, when disrupted, may lead to early onset of TMJOA.
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Sudnik-Wójcikowska, Barbara, and Ivan Moysiyenko. "Zonal character of the flora of kurgans in central and southern Ukraine." Biodiversity: Research and Conservation 17, no. 1 (2010): 47–52. http://dx.doi.org/10.2478/v10119-010-0002-6.
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Zonal character of the flora of kurgans in central and southern UkraineIn the years 2004-2010 floristic studies were carried out on kurgans in 4 different climatic/vegetation zones: 3 steppe zones (west and central Pontic desert steppe, west Pontic grass steppe, west and central Pontic herb-rich grass-steppe) and a forest steppe zone. Among the 450 kurgans surveyed, 106 barrows met the selection criteria. The species composition and the contribution of different life forms to the flora of kurgans changed along the north-south gradient. Jaccard's similarity coefficient and Wards clustering method were used to compare the qualitative composition of flora in particular zones. The results of this comparison confirmed floristic differences between kurgans located in the four zones. Kurgans in the desert steppe zone proved to have the most distinct flora among the floras considered. The biggest similarities were found between the flora of kurgans in the forest steppe zone and that of barrows in the west and central Pontic herb rich grass steppe.
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V, Bharathi, Chandrasekhar Reddy Atla, and M. R. Shivakumar. "A New Multistage Clustering Algorithm for Optimal VAr Planning For Dynamic Voltage Stability Analysis." U.Porto Journal of Engineering 9, no. 3 (2023): 55–71. http://dx.doi.org/10.24840/2183-6493_009-003_001790.
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Power systems need reactive power support to withstand voltage instability issues. The Volt Ampere reactive (VAr) resources are to be optimally determined for location and size. Generally, candidate locations are determined based on the ranking of buses according to sensitivity indices, and the Trajectory Sensitivity Index (TSI) is the most widely used sensitivity index. However, the locations identified by ranking the buses at the system level using the TSI method do not guarantee an optimal VAr solution. The recent methods based on dividing the power system into zones address the issue of optimality. The prevailing zoning methods are based on the computation of the electrical distance between buses and do not address the zoning scenario where the electrical distance between the buses is indeterminable. This paper introduces a new multistage algorithm for grouping the buses into zones even when the electrical distance is indeterminable. The new proposed strategy encompasses applying proposed analytical and parametric techniques after the standard clustering steps. A new index, namely the Bilateral Sensitivity Index (BSI), is introduced to quantify the proximity of buses. The TSI-based zonal level ranking of buses is introduced. The proposed strategy demonstrates that when TSI is employed to rank the buses at the zonal level rather than at the system level, it helps towards an optimal VAr solution. The new multistage clustering strategy is integrated into the generalized Dynamic Voltage Stability Analysis (DVSA) framework for optimal VAr determination and applied to study the voltage collapse phenomenon on a practical 24-bus system in the southern part of India. The results demonstrate the proposed approach’s efficacy in mitigating voltage instability issues through optimal VAr support.
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Egan, Alexander T., and Leonard C. Ferrington. "Zonal stratification and geographic clustering of a species-rich chironomid community in freshwater coastal rock pools." Hydrobiologia 751, no. 1 (2015): 147–58. http://dx.doi.org/10.1007/s10750-015-2183-7.
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Reljin, Irini, Branimir Reljin, and Gordana Jovanovic. "Clustering and mapping spatial-temporal datasets using SOM neural networks." Journal of Automatic Control 13, no. 1 (2003): 55–60. http://dx.doi.org/10.2298/jac0301055r.
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Large datasets can be analyzed through different linear and nonlinear methods. Most frequently used linear method Is Principal Component Analysis (PCA) known also as EOF (Empirical Orthogonal Function) analysis, permitting both clustering and visualizing high-dimensional data Items. However, many problems are nonlinear In nature, so, for analyzing such a problems some nonlinear methods will be more appropriate. The SOM (Self-Organizing Map) neural network is very promising tool for clustering and mapping spatial-temporal datasets describing nonlinear phenomena. The SOM network is applied on the precipitation and temperature data observed in the region of Serbia and Montenegro during 48 years period (1951-1998) and the zonal maps of homogeneous geographical units are derived. These maps are compared with those recently derived via EOF analysis. Significant similarity of results derived from the two methods confirms high efficiency of the SOM network in analyzing spatial-temporal fields. Moreover, the SOM neural network is more appropriate in analyzing climate data since both climate data and the SOM analyzing method are nonlinear in nature.
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Doetsch, Joseph, Niklas Linde, Ilaria Coscia, Stewart A. Greenhalgh, and Alan G. Green. "Zonation for 3D aquifer characterization based on joint inversions of multimethod crosshole geophysical data." GEOPHYSICS 75, no. 6 (2010): G53—G64. http://dx.doi.org/10.1190/1.3496476.
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Predictive groundwater modeling requires accurate information about aquifer characteristics. Geophysical imaging is a powerful tool for delineating aquifer properties at an appropriate scale and resolution, but it suffers from problems of ambiguity. One way to overcome such limitations is to adopt a simultaneous multitechnique inversion strategy. We have developed a methodology for aquifer characterization based on structural joint inversion of multiple geophysical data sets followed by clustering to form zones and subsequent inversion for zonal parameters. Joint inversions based on cross-gradient structural constraints require less restrictive assumptions than, say, applying predefined petrophysical relationships and generally yield superior results. This approach has, for the first time, been applied to three geophysical data types in three dimensions. A classification scheme using maximum likelihood estimation is used to determine theparameters of a Gaussian mixture model that defines zonal geometries from joint-inversion tomograms. The resulting zones are used to estimate representative geophysical parameters of each zone, which are then used for field-scale petrophysical analysis. A synthetic study demonstrated how joint inversion of seismic and radar traveltimes and electrical resistance tomography (ERT) data greatly reduces misclassification of zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved zonal parameters (from 1.8% to 0.3%) compared to individual inversions. We applied our scheme to a data set collected in northeastern Switzerland to delineate lithologic subunits within a gravel aquifer. The inversion models resolve three principal subhorizontal units along with some important 3D heterogeneity. Petrophysical analysis of the zonal parameters indicated approximately 30% variation in porosity within the gravel aquifer and an increasing fraction of finer sediments with depth.
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Chen, Yiwei, Yi He, Jing Wang, et al. "Automated Cone Cell Identification on Adaptive Optics Scanning Laser Ophthalmoscope Images Based on TV-L1 Optical Flow Registration and K-Means Clustering." Applied Sciences 11, no. 5 (2021): 2259. http://dx.doi.org/10.3390/app11052259.
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Cone cell identification is essential for diagnosing and studying eye diseases. In this paper, we propose an automated cone cell identification method that involves TV-L1 optical flow estimation and K-means clustering. The proposed algorithm consists of the following steps: image denoising based on TV-L1 optical flow registration, bias field correction, cone cell identification based on K-means clustering, duplicate identification removal, identification based on threshold segmentation, and merging of closed identified cone cells. Compared with manually labelled ground-truth images, the proposed method shows high effectiveness with precision, recall, and F1 scores of 93.10%, 94.97%, and 94.03%, respectively. The method performance is further evaluated on adaptive optics scanning laser ophthalmoscope images obtained from a healthy subject with low cone cell density and subjects with either diabetic retinopathy or acute zonal occult outer retinopathy. The evaluation results demonstrate that the proposed method can accurately identify cone cells in subjects with healthy retinas and retinal diseases.
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Hornberger, Zachary, Bruce Cox, and Raymond R. Hill. "Analysis of the effects of spatiotemporal demand data aggregation methods on distance and volume errors." Journal of Defense Analytics and Logistics 5, no. 1 (2021): 29–45. http://dx.doi.org/10.1108/jdal-03-2020-0003.
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Purpose Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors. Design/methodology/approach This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering. Findings As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands. Originality/value This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.
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Wolf, Frederik, Aiko Voigt, and Reik V. Donner. "A climate network perspective on the intertropical convergence zone." Earth System Dynamics 12, no. 1 (2021): 353–66. http://dx.doi.org/10.5194/esd-12-353-2021.
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Abstract. The intertropical convergence zone (ITCZ) is an important component of the tropical rain belt. Climate models continue to struggle to adequately represent the ITCZ and differ substantially in its simulated response to climate change. Here we employ complex network approaches, which extract spatiotemporal variability patterns from climate data, to better understand differences in the dynamics of the ITCZ in state-of-the-art global circulation models (GCMs). For this purpose, we study simulations with 14 GCMs in an idealized slab-ocean aquaplanet setup from TRACMIP – the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project. We construct network representations based on the spatial correlation patterns of monthly surface temperature anomalies and study the zonal-mean patterns of different topological and spatial network characteristics. Specifically, we cluster the GCMs by means of the distributions of their zonal network measures utilizing hierarchical clustering. We find that in the control simulation, the distributions of the zonal network measures are able to pick up model differences in the tropical sea surface temperature (SST) contrast, the ITCZ position, and the strength of the Southern Hemisphere Hadley cell. Although we do not find evidence for consistent modifications in the network structure tracing the response of the ITCZ to global warming in the considered model ensemble, our analysis demonstrates that coherent variations of the global SST field are linked to ITCZ dynamics. This suggests that climate networks can provide a new perspective on ITCZ dynamics and model differences therein.
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Chattopadhyay, Rajib, Augustin Vintzileos, and Chidong Zhang. "A Description of the Madden–Julian Oscillation Based on a Self-Organizing Map." Journal of Climate 26, no. 5 (2013): 1716–32. http://dx.doi.org/10.1175/jcli-d-12-00123.1.
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Abstract This study introduces a nonlinear clustering technique based on a self-organizing map (SOM) algorithm to identify horizontal and vertical structures of the Madden–Julian oscillation (MJO) through its life cycle. The SOM description of the MJO does not need intraseasonal bandpass filtering or selection of leading modes. MJO phases are defined by SOM based on state similarities in chosen variables. Spatial patterns of rainfall-related variables in a given MJO phase defined by SOM are distinct from those in other phases. The structural evolution of the MJO derived from SOM agrees with those from other methods in certain aspects and differs in others. SOM reveals that the dominant longitudinal structure in the diabatic heating and related fields of the MJO is a dipole or tripole pattern with a zonal scale close to that of zonal wavenumber 2, as opposed to zonal wavenumber 1 suggested by other methods. Results from SOM suggest that the MJO life cycle may be composed of quasi-stationary stages of strong, coherent spatial patterns with relatively fast transition in between that is less coherent and weaker. The utility of SOM to isolate signals of an individual MJO event in a case study is illustrated. The results from this study show that some known gross features of the MJO are independent of diagnostic methods, but other properties of the MJO may be sensitive to the choice of diagnosis method.
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Aziz, Mahboub, Mokhtar En-Naimi El, Arioua Mounir, Barkouk Hamid, El Assari Younes, and El Oualkadi Ahmed. "An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4192–203. https://doi.org/10.11591/ijece.v9i5.pp4192-4203.
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Wireless sensor networks have become an emerging research area due to their impor- tance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ- SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period.
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Mahboub, Aziz, El Mokhtar En-Naimi, Mounir Arioua, Hamid Barkouk, Younes El Assari, and Ahmed El Oualkadi. "An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4192. http://dx.doi.org/10.11591/ijece.v9i5.pp4192-4203.
Full textAbstract:
Wireless sensor networks have become an emerging research area due to their importance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ-SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period.
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Ni, Lili, Jinglun Li, and Aihemaiti Namaiti. "Classification and Planning Strategies of Multidimensional Resilience Units for Urban Waterlogging: A Case Study of the Old City District in Shijiazhuang, China." Sustainability 16, no. 7 (2024): 2717. http://dx.doi.org/10.3390/su16072717.
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The frequency of urban disasters such as waterlogging has markedly increased, highlighting the urgent need to strengthen urban disaster prevention capabilities and resilience. This research, anchored in the resilience characteristics of robustness, redundancy, resource deploy ability, and rapid response, devised a resilience clustering factor system specifically designed for older urban districts. The old city district of Shijiazhuang, China, was selected as the empirical case study area. This research employs the K-Means++ clustering method to analyze the region’s resilience units against waterlogging. Furthermore, it utilizes the method of pedigree classification to categorize the identified ten types of resilience. Secondly, these were subsequently divided into three primary categories based on a spectrum of strengths and weaknesses within each unit: dominant, mixed, and disadvantaged clustering. This categorization unveiled the unique resilience distribution patterns within the area. The findings of this study reveal a pronounced differentiation in resilience types among the units in Shijiazhuang’s old city district. This spatial analysis highlighted a significant heterogeneity, with a tendency towards cluster formation. The spatial distribution of different resilience unit types was found to be uneven, leading to the emergence of clustered, patch-like, and zonal agglomerations. Combined with the unit clustering classification and the mean clustering performance of each factor, the response unit of waterlogging control resilience planning is determined for the study area, and the strategy of resilience waterlogging control and linkage is proposed. By mapping the spectrum of rainwater resilience types across the studied area, this research broadens the scope of resilience evaluation from a traditional vertical-level assessment to a more comprehensive horizontal typological analysis, offering empirical, theoretical insights for future resilience-building endeavors in older urban districts.
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Nikolopoulos, E. I., M. Borga, F. Marra, S. Crema, and L. Marchi. "Debris flows in the Eastern Italian Alps: seasonality and atmospheric circulation patterns." Natural Hazards and Earth System Sciences Discussions 2, no. 12 (2014): 7197–224. http://dx.doi.org/10.5194/nhessd-2-7197-2014.
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Abstract. The work examines the seasonality and large-scale atmospheric circulation patterns of debris flows in the Trentino-Alto Adige region (Eastern Italian Alps). Analysis is based on classification algorithms applied on a uniquely dense archive of debris flows and hourly rain gauge precipitation series covering the period 2000–2009. Results highlight the seasonal and synoptic forcing patterns linked to debris flows in the study area. Summer and fall season account for 92% of the debris flows in the record, while atmospheric circulation characterized by Zonal West, Mixed and Meridional South, Southeast patterns account for 80%. Both seasonal and circulation patterns exhibit geographical preference. In the case of seasonality, there is a strong north–south separation of summer–fall dominance while spatial distribution of dominant circulation patterns exhibits clustering, with both Zonal West and Mixed prevailing in the northwest and central east part of the region, while the southern part relates to Meridional South, Southeast pattern. Seasonal and synoptic pattern dependence is pronounced also on the debris flow triggering rainfall properties. Examination of rainfall intensity–duration thresholds derived for different data classes (according to season and synoptic pattern) revealed a distinct variability in estimated thresholds. These findings imply a certain control on debris-flow events and can therefore be used to improve existing alert systems.
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Pancheva, D., N. J. Mitchell, and P. T. Younger. "Meteor radar observations of atmospheric waves in the equatorial mesosphere/lower thermosphere over Ascension Island." Annales Geophysicae 22, no. 2 (2004): 387–404. http://dx.doi.org/10.5194/angeo-22-387-2004.
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Abstract. Some preliminary results about the planetary wave characteristics observed during the first seven months (October 2001-April 2002) of observations over Ascension Island (7.9°S, 14.4°W) are reported in this study. The zonal wind is dominated by the 3–7-day waves, while the meridional component – by the quasi-2-day wave. Two wave events in the zonal wind are studied in detail: a 3–4-day wave observed in the end of October/November and the 3–6-day wave in January/February. The moderate 3- and 3.2-day waves are interpreted as an ultra-fast Kelvin wave, while for the strong 4-day wave we are not able to make a firm decision. The 6-day wave is interpreted as a Doppler-shifted 5-day normal mode, due to its very large vertical wavelength (79km). The quasi-2-day wave seems to be present almost continuously in the meridional wind, but the strongest bursts are observed mainly in December and January. The observed period range is large, from 34 to 68h, with some clustering around 43–44 and 50h. The estimated vertical wavelengths indicate shorter lengths during the equinoxes, in the range of 25-30km, and longer ones, ∼40–50km, in January/February, when the 48-h wave is strongest. Key words. Meteorology and atmospheric dynamics middle atmosphere dynamics, waves and tides)
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Nikolopoulos, E. I., M. Borga, F. Marra, S. Crema, and L. Marchi. "Debris flows in the eastern Italian Alps: seasonality and atmospheric circulation patterns." Natural Hazards and Earth System Sciences 15, no. 3 (2015): 647–56. http://dx.doi.org/10.5194/nhess-15-647-2015.
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Abstract. The work examines the seasonality and large-scale atmospheric circulation patterns associated with debris-flow occurrence in the Trentino–Alto Adige region (eastern Italian Alps). Analysis is based on classification algorithms applied to a uniquely dense archive of debris flows and hourly rain gauge precipitation series covering the period 2000–2009. Results highlight the seasonal and synoptic forcing patterns linked to debris flows in the study area. Summer and fall season account for 92% of the debris flows in the record, while atmospheric circulation characterized by zonal west, mixed and meridional south and southeast (SE–S) patterns account for 80%. Both seasonal and circulation patterns exhibit geographical preference. In the case of seasonality, there is a strong north–south separation of summer–fall dominance, while spatial distribution of dominant circulation patterns exhibits clustering, with both zonal west and mixed patterns prevailing in the northwest and central east part of the region, while the southern part relates to meridional south and southeast pattern. Seasonal and synoptic pattern dependence is pronounced also on the debris-flow-triggering rainfall properties. Examination of rainfall intensity–duration thresholds derived for different data classes (according to season and synoptic pattern) revealed a distinct variability in estimated thresholds. These findings imply a certain control on debris-flow events and can therefore be used to improve existing alert systems.
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Ntabeni, Unalido, Bokamoso Basutli, Hirley Alves, and Joseph Chuma. "Improvement of the Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks Using Mean Field Games." Sensors 24, no. 21 (2024): 6952. http://dx.doi.org/10.3390/s24216952.
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The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach.
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Figueroa-Ponce, Felipe, and Luis Felipe Hinojosa. "Environmental Filters Structure Cushion Bogs’ Floristic Composition along the Southern South American Latitudinal Gradient." Plants 13, no. 16 (2024): 2202. http://dx.doi.org/10.3390/plants13162202.
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The environmental filtering hypothesis predicts that abiotic factors restrict communities by selecting species capable of survival and persistence under specific conditions, resulting in variations in beta diversity, phylogenetic clustering, and niche differentiation among communities when studying environmental gradients. Cushion bogs and high-altitude wetlands along the Andes display homogeneous flora contrasting with zonal vegetation. Despite being influenced by microclimatic conditions, these ecosystems are subject to diverse environmental effects. Here, we test the environmental filtering hypothesis on the structure of cushion bog communities along a broad-scale latitudinal gradient from 15° S to 42° S. We analyzed 421 bogs and 293 species across three macroclimatic regions with distinct summer, winter, and transitional arid rainfall regimes. Using variance partitioning and membership-based regionalization models, we examined the impacts of climatic, edaphic, and spatial variables on beta diversity. We also assessed species’ niche overlap and the influence of environmental filters on the communities’ phylogenetic diversity. Results show that species turnover and niche overlap vary with macroclimatic differences, delineating three distinct regions. Notably, phylogenetic clustering in the driest part of the gradient (23° S–24° S) highlights the impact of environmental filtering. Aridity and temperature variations at a broad scale serve as environmental filters shaping the composition of bog communities across southern South America.
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Cantelmo, Guido, Rafał Kucharski, and Constantinos Antoniou. "Low-Dimensional Model for Bike-Sharing Demand Forecasting that Explicitly Accounts for Weather Data." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (2020): 132–44. http://dx.doi.org/10.1177/0361198120932160.
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With the increasing availability of big, transport-related datasets, detailed data-driven mobility analysis is becoming possible. Trips with their origins, destinations, and travel times are now collected in publicly available databases, allowing for detailed demand forecasting with methods exploiting big and accurate data. In this paper, we predict the demand pattern of New York City bikes with a low-dimensional approach utilizing three-level data clustering. We use historical demand data along with temperature and precipitation to first aggregate and then decompose data to obtain meaningful clusters. The core of this approach lies in the proposed clustering technique, which reduces the dimension of the problem and, differently from other machine learning techniques, requires limited assumptions on the model or its parameters. The proposed method allows, for the given temperature and precipitation method, to obtain expected vector of movement (mean number and direction of trips) for each zone. In this paper, we synthesize more than 17 million trips into daily and zonal vectors of movement, which combined with weather data allow forecasting of the trip demand. The method allows us to predict the demand with over 75% accuracy, as shown in series of experiments in which various settings and parameterizations are validated against 25% holdout data.
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Vaz, Carlos M. P., Julio C. Franchini, Eduardo A. Speranza, et al. "Zonal Application of Plant Growth Regulator in Cotton to Reduce Variability and Increase Yield in a Highly Variable Field." Journal of Cotton Science 23, no. 2 (2023): 60–73. http://dx.doi.org/10.56454/miuc6583.
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Variable-rate application has great potential to reduce variability and increase yield by spatially optimizing agricultural inputs. In cotton, plant growth regulators (PGRs) control excessive growth and provide suitable plant height for harvest operations. This study evaluates the effect of variable-rate PGR application compared to constant-rate application to reduce yield spatial variability and increase yield. The variable-rate approach was carried out in 2020 based on zonal applications defined by clustering analysis using soil electrical conductivity, vegetation indexes, and yield maps. Application doses and timings were determined by integrating plant height measurements for the whole field in 2019 and by zone in 2020. To compare the two procedures, cultivar and plant populations were kept constant; fertilization and accumulated rain were similar in both seasons. A reduction in yield spatial variability due to the zonal application was observed, with yield coefficient of variation (CV) decreasing from 18% in 2019 to 12% in 2020. Spatial and temporal analysis of Normalized Difference Vegetation Index satellite images showed higher CV values in 2019 (constant-rate) reaching 30% at the end of the season, whereas in 2020 (variable-rate) CV was constant (approximately 10%). Cotton yield increased from 3.5 to 4.3 t ha-1 between 2019 and 2020, which can be partially attributed to the variable-rate approach. The variable-rate approach based on application zones and plant height measurements was a viable strategy for reducing yield spatial variability and likely increasing yield in a highly variable cotton field.
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Hu, Linwei, Joseph Doetsch, Ralf Brauchler, and Peter Bayer. "Characterizing CO2 plumes in deep saline formations: Comparison and joint evaluation of time-lapse pressure and seismic tomography." GEOPHYSICS 82, no. 4 (2017): ID1—ID18. http://dx.doi.org/10.1190/geo2016-0365.1.
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Monitoring the migration of sequestered [Formula: see text] in deep heterogeneous reservoirs is inherently difficult. Geophysical methods have been successfully used, but flow conditions are only indirectly linked to the measured properties. Besides geophysical methods, pressure tomography (PT) is proposed as an alternative method to depict the structure of deep saline formations for [Formula: see text] sequestration and to continuously delineate [Formula: see text] plumes. In contrast to more established geophysical measurements, pressure transients are directly related to flow properties, which allows for the estimation of permeability. We investigate the influence of aquifer heterogeneity on PT performance, and we compare the PT results to crosshole seismic tomography (ST). Multilevel fluid injections and high-frequency P-wave pulses are induced in a simulated deep borehole, and the recorded signals at another well are processed by a traveltime inversion scheme. The reservoir structure is inferred by clustering the inverted hydraulic diffusivity prior to [Formula: see text] injection, and the plume distribution is determined by clustering the tomograms of the inverted mixed-phase diffusivity difference and P-wave velocity difference. The clustered structures are then used for zonal calibration to acquire the saturation within the plumes. Modeling results indicate that PT provides clearer structural information on the [Formula: see text]-free aquifer due to its direct linkage to permeability. However, the plume depicted by PT can be ambiguous, whereas ST is less sensitive to the prevailing heterogeneity of permeability at postinjection and can thus image the plume more clearly. PT and ST can be complementary to each other through the joint clustering to improve plume shape identification and estimation of spatial [Formula: see text] saturation.
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Bessac, Julie, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet. "Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic." Advances in Statistical Climatology, Meteorology and Oceanography 2, no. 1 (2016): 1–16. http://dx.doi.org/10.5194/ascmo-2-1-2016.
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Abstract. Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed in wind conditions due to the existence of different weather types. This modeling blocks time series into periods in which the series is described by a single model. The regime-switching is modeled by a discrete variable that can be introduced as a latent (or hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditional on the regimes, the observed wind conditions are assumed to evolve as a linear Gaussian vector autoregressive (VAR) model. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss the relative advantages of hidden and observed regime-switching models. For artificial stochastic generation of wind sequences, we show that the proposed models reproduce the average space–time motions of wind conditions, and we highlight the advantage of regime-switching models in reproducing the alternation of intensity and variability in wind conditions.
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McMurdie, Lynn A., and Joseph H. Casola. "Weather Regimes and Forecast Errors in the Pacific Northwest." Weather and Forecasting 24, no. 3 (2009): 829–42. http://dx.doi.org/10.1175/2008waf2222172.1.
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Abstract Despite overall improvements in numerical weather prediction and data assimilation, large short-term forecast errors of sea level pressure and 2-m temperature still occur. This is especially true for the west coast of North America where short-term numerical weather forecasts of surface low pressure systems can have large position and central pressure errors. In this study, forecast errors of sea level pressure and temperature in the Pacific Northwest are related to the shape of the large-scale flow aloft. Applying a hierarchical limited-contour clustering algorithm to historical 500-hPa geopotential height data produces four distinct weather regimes. The Rockies ridge regime, which exhibits a ridge near the axis of the Rocky Mountains and nearly zonal flow across the Pacific, experiences the highest magnitude and frequency of large sea level pressure errors. On the other hand, the coastal ridge regime, which exhibits a ridge aligned with the North American west coast, experiences the highest magnitude and frequency of large 2-m minimum temperature errors.
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Lembo, Valerio, Federico Fabiano, Vera Melinda Galfi, Rune Grand Graversen, Valerio Lucarini, and Gabriele Messori. "Meridional-energy-transport extremes and the general circulation of Northern Hemisphere mid-latitudes: dominant weather regimes and preferred zonal wavenumbers." Weather and Climate Dynamics 3, no. 3 (2022): 1037–62. http://dx.doi.org/10.5194/wcd-3-1037-2022.
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Abstract. The extratropical meridional energy transport in the atmosphere is fundamentally intermittent in nature, having extremes large enough to affect the net seasonal transport. Here, we investigate how these extreme transports are associated with the dynamics of the atmosphere at multiple spatial scales, from planetary to synoptic. We use the ERA5 reanalysis data to perform a wavenumber decomposition of meridional energy transport in the Northern Hemisphere mid-latitudes during winter and summer. We then relate extreme transport events to atmospheric circulation anomalies and dominant weather regimes, identified by clustering 500 hPa geopotential height fields. In general, planetary-scale waves determine the strength and meridional position of the synoptic-scale baroclinic activity with their phase and amplitude, but important differences emerge between seasons. During winter, large wavenumbers (k = 2–3) are key drivers of the meridional-energy-transport extremes, and planetary- and synoptic-scale transport extremes virtually never co-occur. In summer, extremes are associated with higher wavenumbers (k = 4–6), identified as synoptic-scale motions. We link these waves and the transport extremes to recent results on exceptionally strong and persistent co-occurring summertime heat waves across the Northern Hemisphere mid-latitudes. We show that the weather regime structures associated with these heat wave events are typical for extremely large poleward-energy-transport events.
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Fish, Meredith A., James M. Done, Daniel L. Swain, et al. "Large-Scale Environments of Successive Atmospheric River Events Leading to Compound Precipitation Extremes in California." Journal of Climate 35, no. 5 (2022): 1515–36. http://dx.doi.org/10.1175/jcli-d-21-0168.1.
Full textAbstract:
Abstract Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-yr catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the interannual variability in AR frequency is driven by AR family versus single events. Using k-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of El Niño–Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years, and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but with a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on subseasonal to seasonal time scales.
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Barrera-Jiménez, Humberto, and Juan Pineda-Jaramillo. "A Framework for Integrating Freight Transport, Urban Land Planning, and Infrastructure Management under Economic Geography Principles." Urban Science 8, no. 2 (2024): 30. http://dx.doi.org/10.3390/urbansci8020030.
Full textAbstract:
This study presents a conceptual framework proposal for integrating urban freight initiatives (UFIs), or city logistics initiatives, into urban planning and urban management (UPUM) land use and infrastructure systems. As a novel approach, this framework integrates three components: Firstly, a conceptual basis on three economic geography theory principles—location, agglomeration, and urbanisation. Secondly, spatial analysis and subsequent clustering integrate companies’ spatial positions, their proximity to other companies, their freight intensity, and the characteristics of the zonal road infrastructure; these clusters are defined as freight traffic zones (FTZs). Thirdly, a functional yet strategic UFI clustering or grouping is proposed to work in an optimised and integrated manner with the FTZs’ opportunities for efficiency and reduced externalities. It is expected that the integrated result of these three components can serve to optimise freight initiatives and road infrastructure from a city governance perspective, reduce freight externalities, and function as a stakeholder cooperation tool from government-led, policy-driven perspectives. This research also identifies and characterises various variables influencing the emergence and existence (planned or organic) of FTZs and shows how these could be incorporated into high-level UPUM processes. Although it is deemed that the principles and methodological approach followed here could be common to urban areas, an example for the Metropolitan Area of the Aburra Valley (MAAV), in Colombia, is presented as an initial case study. Conclusively, this paper introduces a pioneering methodology for integrating UFIs into city or metropolitan governance, offering guidance for policymakers to promote sustainable freight systems.