Clustering Mechanism Research Articles

EVRP: A novel geometrical based energy efficient eye vision routing protocol for wireless sensor networks based on the k-means algorithm

The limited energy source used in wireless sensor networks (WSNs) is a crucial aspect when designing a routing protocol. Developing a routing protocol that tries to maximize energy utilization can significantly improve the system's lifetime and balance energy consumption. A novel geometrical-based energy-efficient routing protocol called "EVRP" is presented in this paper. The Cluster generation process is based on the k-means algorithm for nodes grouping. To choose a cluster head (CH) for each cluster, a cost function is calculated for each node considering factors such as remaining energy level for the node, and the mean distance between the node and its neighbors. In the inter-cluster routing phase, a novel geometrical-based routing protocol is introduced considering factors such as the distance to a base station (BS), remaining energy level, and distance between CHs to find the optimal path between BS and CH. The cluster structure is integrated with direct communication in addition to a dynamic clustering mechanism based on the optimum number of CHs and a load balancing mechanism to reduce the load on CHs near BS. The simulation results demonstrate that in different scenarios, the proposed protocol can significantly increase the network lifetime, network throughput, and network stability by 52 %, 45 %, and 30 % respectively compared with the EECRAIFA protocol and by 100 %, 97.6 %, 64.3 % respectively compared with D2CRP protocol.

A secure routing protocol using trust‐based clustering and bionic intelligence algorithm for UAV‐assisted vehicular ad hoc networks

AbstractVehicular ad hoc networks (VANET) are one of the advanced technologies for distributing dynamic vehicular information across the globe. The VANET is extensively used in many applications, especially road safety applications and intelligent transport systems (ITS). However, direct communication causes high bandwidth (BW) requirement and power consumption. Hence, this article introduces a clustering‐based mechanism to communicate vehicles with infrastructures. The cluster head (CH) is formed based on certain rules, and nodes or vehicles are combined. But, maintaining stability remains challenging for the traditional clustering mechanism. Moreover, the developed technique must examine the malicious and reduce the risk of fake information sharing. This research emphasizes a trust‐based clustering mechanism to select the CH based on a vehicle's knowledge, reputation, and experience. In addition to this, the backup head is also analyzed to promote trust in each vehicle. After clustering, secure routing is undertaken. For this, a bionic remora optimization algorithm (BROA) is proposed, and it considers the hop Count Field as well as the transmission range of vehicles to select the best routes. The performance measures such as end‐to‐end delay ratio, packet delivery ratio (PDR), throughput, trust values, energy consumption are analyzed and compared with existing techniques. In an experimental scenario, the proposed technique has an end‐to‐end delay of 3.8 ms, PDR of 98%, trust value of .4 and .06 for wormhole and Sybil attack, energy of and throughput of 78.7 kbps are attained. The outcome results prove the efficacy of a proposed method.

Improvement of Practical Byzantine Fault Tolerance Consensus Algorithm Based on DIANA in Intellectual Property Environment Transactions

In response to the shortcomings of the consensus algorithm for intellectual property transactions, such as high communication overhead, random primary node selection, and prolonged consensus time, a Practical Byzantine Fault Tolerance (PBFT) improvement algorithm based on Divisive Analysis (DIANA) D-PBFT algorithm is proposed. Firstly, the algorithm adopts the hierarchical clustering mechanism of DIANA to cluster nodes based on similarity, enhancing node partition accuracy and reducing the number of participating consensus nodes. Secondly, it designs a reward and punishment system based on node ranking, to achieve consistency between node status and permissions, timely evaluation, and feedback on node behaviours, thereby enhancing node enthusiasm. Then, the election method of the primary node is improved by constructing proxy and alternate nodes and adopting a majority voting strategy to achieve the selection and reliability of the primary node. Finally, the consistency protocol is optimised to perform consensus once within the cluster and once between all primary nodes, to ensure the accuracy of the consensus results. Experimental results demonstrate that the D-PBFT algorithm shows a better performance, in terms of communication complexity, throughput, and latency.

DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet delivery rate (about 0.61%), and delay (approximately 14.29%). However, its overhead is approximately 8.72% more than MWCRSF.

Role of Iodine-Assisted Aerosol Particle Formation in Antarctica.

New particle formation via the ion-mediated sulfuric acid and ammonia molecular clustering mechanism remains the most widely observed and experimentally verified pathway. Recent laboratory and molecular level observations indicate iodine-driven nucleation as a potentially important source of new particles, especially in coastal areas. In this study, we assess the role of iodine species in particle formation using the best available molecular thermochemistry data and coupled to a detailed 1-d column model which is run along air mass trajectories over the Southern Ocean and the coast of Antarctica. In the air masses traversing the open ocean, ion-mediated SA-NH3 clustering appears insufficient to explain the observed particle size distribution, wherein the simulated Aitken mode is lacking. Including the iodine-assisted particle formation improves the modeled Aitken mode representation with an increase in the number of freshly formed particles. This implies that more particles survive and grow to Aitken mode sizes via condensation of gaseous precursors and heterogeneous reactions. Under certain meteorological conditions, iodine-assisted particle formation can increase cloud condensation nuclei concentrations by 20%-100%.

Knowledge-based Clustering Federated Learning for fault diagnosis in robotic assembly

Fault diagnosis in industrial robots is a critical aspect of intelligent manufacturing. However, the accuracy of fault diagnosis models can be significantly affected by the few-shot problem, which refers to the limited availability of labeled data for training. Traditional methods often rely on combining data from different sources, which can raise privacy concerns due to the sensitive nature of the data involved. Federated Learning has emerged as a privacy-preserving approach, but it faces challenges in dealing with the Non-IID (Non-Independent and Identically Distributed) data distribution across different robot systems. In this study, we propose a novel approach called Knowledge-Based Clustering Federated Learning (KCFed) to address both the few-shot problem in robot fault diagnosis and the Non-IID problem in Federated Learning. KCFed incorporates Federated Learning principles to ensure privacy protection during the model training process. Additionally, by leveraging the Knowledge-based Clustering Mechanism (KCM) and Knowledge Accumulation Mechanism (KAM), KCFed aims to improve the performance of fault diagnosis models by clustering similar tasks and accumulating useful prior information. To evaluate the effectiveness of KCFed, we conducted experiments using data collected from industrial robots performing 12 different tasks, resulting in a diverse set of 48 states. The experimental results demonstrate the promising performance of KCFed in improving fault diagnosis accuracy while preserving privacy in a federated learning setting.

Investigation of friction and wear behavior of titanium alloy-diamond based on the morphology evolution of single abrasive clusters

This study investigates the wear mechanism of single diamond clusters during flexible scratching of titanium alloys. High-temperature rotary friction and wear tests were conducted using a flexible abrasive tool with bonded single diamond clusters. The study examined the impact of normal loads, rotation speeds, and temperatures on cutting ability and wear in titanium alloys. The percentage of abraded area in the annular cross-section was proposed as an index for evaluating diamond wear. The results indicate that the primary factors affecting diamond cluster wear are normal load and temperature, while friction speed has a relatively minor impact. At a 2 N load, the abrasive grains exhibited abrasive blunt wear without causing obvious damage to the titanium alloy surface. However, at a 4 N load and a temperature of 110 °C, adhesive wear became dominant, resulting in localized damage and oxidative wear on the diamonds. The annular cross-section abrasive wear profile revealed a nearly 30% increase in abrasive grain wear area as the normal load and temperature increased. This study aims to improve understanding of the abrasive grain wear at various stages and lays the foundations for grinding and polishing large-sized, high-precision parts.

Enhancing Energy Efficiency in Wireless Sensor Networks through I-LEACH: A Data Clustering and Routing Protocol

The inclusion of innovative technology into sensor networks has resulted in notable advancements in several industries, including mining, healthcare, military surveillance, and more. Using a multi-hop approach, nodes are placed strategically across the Region of Interest (RoI) in order to transfer data to the Base Station (BS). In order to address issues with energy consumption, effective cluster head selection, packet loss, routing algorithms, and energy efficiency, the Wireless Sensor Network (WSN) has become a key field of study. The objectives of this research are to increase nodes' Residual Energy, and lengthen the network lifetime. Using an effective clustering mechanism and Cluster Head election procedure is the suggested method. The goal of choosing a CH is to minimize the distance from the base station while optimizing the node's remaining energy. The protocol acts in two stages: firstly, computing a new Threshold value for the cluster head election process, and secondly, applying a data fusion mechanism based energy model is used in the network to reduce redundant energy transfer.

Enhancing Energy Efficiency in Wireless Sensor Networks through I-LEACH: A Data Clustering and Routing Protocol

The inclusion of innovative technology into sensor networks has resulted in notable advancements in several industries, including mining, healthcare, military surveillance, and more. Using a multi-hop approach, nodes are placed strategically across the Region of Interest (RoI) in order to transfer data to the Base Station (BS). In order to address issues with energy consumption, effective cluster head selection, packet loss, routing algorithms, and energy efficiency, the Wireless Sensor Network (WSN) has become a key field of study. The objectives of this research are to increase nodes' Residual Energy, and lengthen the network lifetime. Using an effective clustering mechanism and Cluster Head election procedure is the suggested method. The goal of choosing a CH is to minimize the distance from the base station while optimizing the node's remaining energy. The protocol acts in two stages: firstly, computing a new Threshold value for the cluster head election process, and secondly, applying a data fusion mechanism based energy model is used in the network to reduce redundant energy transfer.

Quantum aided efficient resource control for connected support in IRS assisted networks

Context:To achieve the vision of all connected world with uninterrupted communication support, 6G technology plays an important role. But the scarce radio spectrum and limited network resources is the main challenge in delivering its promised performance. Objectives:This paper presents an IRS-aided cell free NOMA network model that aims to provide uniform network coverage. The future 6G technology envisions for serving billions of interconnected devices with seamless communication support, data handling capabilities and computational accuracy. But the scarcity of network resources is the main limitation. Thus, the need is to design quantum enabled intelligent and dynamic networks capable of offering extended network capabilities. Proposed work is on intelligent network framework that provides uniform network coverage through efficient resource management for a 6G enabled expanded IoT network. Methods:To enable efficient resource management, a quantum enabled resource control algorithm is proposed that creates user clusters and associates each AP-IRS pair to each cluster. Each AP transmits the superimposed signals of its intended cluster against all the user clusters as in conventional NOMA system. The nodes in each cluster have been assigned unique pilots so as to avoid intracluster interference. The use of IRS enables desired NOMA beamforming such that the effect of unfavourable wireless environment is mitigated. Results:The performance of the IRS-aided cell-free NOMA network is evaluated for average sum rate with different AP transmit power, cluster sizes, IRS reflecting elements and IRS phase shifts. It is shown that at transmit power per AP of 30dBm, the average sum rate of the system improves by 7.52% with the proposed algorithm using equal power allocation scheme. Further, the comparative performance analysis of three different communication systems is carried out to validate the proposed communication model. Conclusion:It is observed that with more number of users per cluster, the average sum rate of the system initially increases for small cluster sizes and then it becomes constant for large cluster sizes. The proposed clustering method outperforms the random clustering approach achieving sum rate of 12.9 bits/s/Hz with N =300 and M= 8. The comparison of different communication scenarios reveals that the maximum sum rate of 12.2 bits/s/Hz is achieved with the proposed model incorporating proposed clustering mechanism. Further, the energy efficiency analysis suggests that energy efficiency improves with N and Pc with proposed clustering approach. The use case scenarios for the integration of quantum computing with IRS technology are also presented.

Spatial Distribution Analysis of Urban Retail Industry Using POI Big Data

With the development of social economy, it is very important to identify urban functional areas and understand the spatial distribution characteristics of urban functional areas for urban scientific planning and government decision-making. Reasonable spatial distribution of urban retail industry is of great significance to promote urban economic development, optimize urban structure, and meet the consumption needs of urban residents. Based on the data of retail industry's POI, GDP, population and transportation in Nanning, China, this paper analyzes the spatial distribution characteristics and formation mechanism of different retail industry clusters in the city, and studies the spatial correlation between retail industry and population, economy, transportation, etc., using the methods of kernel density method, spatial correlation analysis, Getis-Ord G * index statistics, and average nearest neighbor distance. The results show that:(1)On the whole, the retail industry is concentrated in the central urban area of Nanning, spreading from the middle to the surrounding areas. (2)The retail hot spots have formed a multi-center development and surrounding spread pattern in spatial distribution. (3) The spatial distribution of the retail industry is highly related to the main traffic lines. At the same time, the concentration and distribution of the retail industry have a strong spatial correlation with the economy and population.

Abstract 197: The GPR52-MCAM axis as a novel regulatory mechanism of breast cancer cell clustering and metastatic potential

Abstract Background: Breast cancer is the most diagnosed cancer worldwide and is the second leading cause of cancer death among women in the United States. Despite breakthroughs in targeted therapies for breast cancer, the 5-year survival rate for women with metastatic breast cancer is 30%. There is a strong rationale to explore the therapeutic potential of G-protein coupled receptors (GPCRs), which comprise the largest class of proteins successfully targeted by FDA-approved drugs. Importantly, at least one-third of GPCRs are “orphans” and remain largely uncharacterized. One of these orphan GPCRs is GPR52. To date, there is no literature on the role of GPR52 in any cancer type, but we find from publicly available clinical data that low mRNA expression of GPR52 in breast tumors correlates with reduced overall survival (hazard ratio=0.67 [0.53-0.83], logrank P= 0.00035) and that GPR52 mRNA levels tend to be lower in tumor metastases compared to primary tumor (n>80, p=4.45e-17). Therefore, we hypothesize that loss of GPR52 may support breast cancer metastasis. Results: We used CRISPR-Cas9 to knockout (KO) GPR52 in triple-negative breast cancer cell lines MDA-MB-468 and MDA-MB-231, and in the non-cancerous breast epithelial cell line MCF10A, and confirmed the generation of frameshift mutations by Sanger sequencing. Interestingly, we observed that GPR52 KO cells clustered together in vitro in 2D and hypothesized that this characteristic may allow GPR52 KO breast cancer cells to increase their metastatic potential. We conducted invasion assays though Matrigel and found that GPR52 KO cells were more likely to invade as sheets or clusters. To determine the in vivo impact of GPR52 loss, we injected MDA-MB-468 GPR52 KO cells in zebrafish (Danio rerio) two days post-fertilization and observed that GPR52 loss led to increased metastatic foci and total cancer surface area thirty hours post-injection (n≥12, p<0.05). We determined from RNA-sequencing (n=3, p<.0001) and confirmed with western blotting that the melanoma cell adhesion molecule (MCAM) is upregulated in the MCF10A and MDA-MB-468 GPR52 KO cells. Increased mRNA expression of MCAM has been associated with reduced overall survival in breast cancer patients (hazard ratio=1.33 [1.05-1.62], logrank P= 0.02) Conclusion: Knockout of GPR52 from breast cancer cells promotes increased cell clustering in vitro and metastasis in zebrafish. These data support the investigation of GPR52 as a biomarker of cancer aggression and a potential therapeutic target for breast cancer patients whose tumors express GPR52. Citation Format: Sarah Z. Hanif, CheukMan C. Au, Ingrid Torregroza, Bhavneet Bhinder, Lukas Dow, Olivier Elemento, Todd Evans, Kristy A. Brown. The GPR52-MCAM axis as a novel regulatory mechanism of breast cancer cell clustering and metastatic potential [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 197.

Membrane Catalyzed Formation of Nucleotide Clusters and Their Role in the Origins of Life: Insights from Molecular Simulations and Lattice Modeling.

One of the mysteries in studying the molecular "Origin of Life" is the emergence of RNA and RNA-based life forms, where nonenzymatic polymerization of nucleotides is a crucial hypothesis in formation of large RNA chains. The nonenzymatic polymerization can be mediated by various environmental settings, such as cycles of hydration and dehydration, temperature variations, and proximity to a variety of organizing matrices, such as clay, salt, fatty acids, lipid membrane, and mineral surface. In this work, we explore the influence of different phases of the lipid membrane toward nucleotide organization and polymerization in a simulated prebiotic setting. Our molecular simulations quantify the localization propensity of a mononucleotide, uridine monophosphate (UMP), in distinct membrane settings. We perform all-atom molecular dynamics (MD) simulations to estimate the role of the monophasic and biphasic membranes in modifying the behavior of UMPs localization and their clustering mechanism. Based on the interaction energy of mononucleotides with the membrane and their diffusion profile from our MD calculations, we developed a lattice-based model to explore the thermodynamic limits of the observations made from the MD simulations. The mathematical model substantiates our hypothesis that the lipid layers can act as unique substrates for "catalyzing" polymerization of mononucleotides due to the inherent spatiotemporal heterogeneity and phase change behavior.

Multivariate Hypocenter Clustering and Source Mechanism of 2017 Mw 6.2 and 2019 Mw 6.5 in the South Seram Subduction System

Multivariate Hypocenter Clustering and Source Mechanism of 2017 Mw 6.2 and 2019 Mw 6.5 in the South Seram Subduction System

A Unified Visual and Linguistic Semantics Method for Enhanced Image Captioning

Image captioning, also recognized as the challenge of transforming visual data into coherent natural language descriptions, has persisted as a complex problem. Traditional approaches often suffer from semantic gaps, wherein the generated textual descriptions lack depth, context, or the nuanced relationships contained within the images. In an effort to overcome these limitations, we introduce a novel encoder–decoder framework called A Unified Visual and Linguistic Semantics Method. Our method comprises three key components: an encoder, a mapping network, and a decoder. The encoder employs a fusion of CLIP (Contrastive Language–Image Pre-training) and SegmentCLIP to process and extract salient image features. SegmentCLIP builds upon CLIP’s foundational architecture by employing a clustering mechanism, thereby enhancing the semantic relationships between textual and visual elements in the image. The extracted features are then transformed by a mapping network into a fixed-length prefix. A GPT-2-based decoder subsequently generates a corresponding Chinese language description for the image. This framework aims to harmonize feature extraction and semantic enrichment, thereby producing more contextually accurate and comprehensive image descriptions. Our quantitative assessment reveals that our model exhibits notable enhancements across the intricate AIC-ICC, Flickr8k-CN, and COCO-CN datasets, evidenced by a 2% improvement in BLEU@4 and a 10% uplift in CIDEr scores. Additionally, it demonstrates acceptable efficiency in terms of simplicity, speed, and reduction in computational burden.

Theoretical study of a Ti4 cluster interacting with B-doped and non-doped multivacancy graphene

A density functional theory study was performed to understand the interactions of a small Ti4 cluster with multi-vacancy graphene. We also consider the effect produced by the B-doping of the vacancy defect. In this sense, we analyze the binding mechanisms of the metal cluster. The focus is set on its stability on the substrate, geometrical structure and the interactions participating between the cluster and graphene substrate. A qualitative study of the principal bonds was carried out employing overlap population and bond order chemical descriptors. Additionally, as an attempt to comprehend better the electronic structure and related properties, we performed total density of states, band structure and magnetic moment calculations to characterize the electrical and magnetic properties of the B-doped and non-doped systems.

An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs’ lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis.

Nek2A prevents centrosome clustering and induces cell death in cancer cells via KIF2C interaction

Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal segregation of genomic material by coalescing their centrosomes into two poles. This unique trait of cancer cells presents a promising target for cancer therapy, focusing on selectively attacking cells with supernumerary centrosomes. Nek2A is a kinase involved in mitotic regulation, including the centrosome cycle, where it phosphorylates linker proteins to separate centrosomes. In this study, we investigated if Nek2A also prevents clustering of supernumerary centrosomes, akin to its separation function. Reduction of Nek2A activity, achieved through knockout, silencing, or inhibition, promotes centrosome clustering, whereas its overexpression results in inhibition of clustering. Significantly, prevention of centrosome clustering induces cell death, but only in cancer cells with supernumerary centrosomes, both in vitro and in vivo. Notably, none of the known centrosomal (e.g., CNAP1, Rootletin, Gas2L1) or non-centrosomal (e.g., TRF1, HEC1) Nek2A targets were implicated in this machinery. Additionally, Nek2A operated via a pathway distinct from other proteins involved in centrosome clustering mechanisms, like HSET and NuMA. Through TurboID proximity labeling analysis, we identified novel proteins associated with the centrosome or microtubules, expanding the known interaction partners of Nek2A. KIF2C, in particular, emerged as a novel interactor, confirmed through coimmunoprecipitation and localization analysis. The silencing of KIF2C diminished the impact of Nek2A on centrosome clustering and rescued cell viability. Additionally, elevated Nek2A levels were indicative of better patient outcomes, specifically in those predicted to have excess centrosomes. Therefore, while Nek2A is a proposed target, its use must be specifically adapted to the broader cellular context, especially considering centrosome amplification. Discovering partners such as KIF2C offers fresh insights into cancer biology and new possibilities for targeted treatment.

Enhanced collaborative networking protocol based on clustering mechanism for UV networks

Developing a highly efficient multi-node media access control protocol tailored for ultraviolet (UV) networks poses a significant challenge. While our team's UV random-access collaborative networking protocol, utilizing time-division multiple access (UVCN-TDMA), has been instrumental, its scalability is hindered by the number of network nodes. To address this limitation and facilitate large-scale UV node networking, this study advances the UVCN protocol by introducing an enhanced collaborative networking protocol known as the enhanced collaborative clustering mechanism (eCOCM). This study examined the impact of network topology, various data services, and the number of cluster nodes on network performance within the proposed eCOCM framework. Additionally, a comparative analysis between the eCOCM protocol and conventional clustering systems was conducted. The findings demonstrate notable enhancements across key performance metrics including throughput, packet loss rate, delay, and channel utilization. Finally, functional experiments were conducted under two distinct network scenarios to validate the efficacy of the proposed protocol. This study addresses the immediate challenges in UV multi-node networking and offers pragmatic insights to steer its development forward.

Alkyl Chain Length Dependent Luminescence and Single‐Component White‐Light Emission from Gemini Aliphatic Quaternary Ammonium Salts

AbstractAliphatic quaternary ammonium salts (QASs) with intrinsic photoluminescence (PL) have drawn increasing attention due to the importance of exploring the mechanism of ionic emissive clusters, whereas it remains challenging to clarify structure–property relationships. Here, the effective modulation of photophysical properties of gemini aliphatic QASs (n‐BTADB) is managed by altering the alkyl chain length. Boosted quantum yields (up to 24.6%), prolonged phosphorescence lifetimes (up to 515.2 ms), and varied PL colors are achieved by manipulating the aggregation states of the compounds. Interlaced herringbone packing patterns and strong ionic interactions facilitate the intersystem crossing process and stabilize the triplets. The confluence of varied hydrophobic interactions and chain flexibility results in the fluctuation of phosphorescence lifetimes as the alkyl chain lengthens. Furthermore, the single‐component white‐light emission is obtained in 12‐HBTADB crystals by introducing electron‐rich hydroxyls. These results shed new light on the structure–property relationships of ionic emissive clusters and the rational design of nonconventional ionic luminophores.