Cluster Architecture Research Articles

Strain‐Induced Structural Rearrangement Towards a White‐Light‐Emitting Adamantane‐Type Cluster Dimer

AbstractGroup 14/16 adamantane‐type hybrid clusters of the type [(RT)4E6] (T=group 14 element, E=group 16 element, R=organic group) have been reported to emit white‐light when irradiated in an amorphous state with a continuous‐wave (CW) infrared laser diode. This effect is enhanced if the cluster core is varied from a binary to a more complex composition. To further explore this phenomenon, we synthesized clusters with a multinary R/R’‐T/T’‐E/E’ composition, including isolobal replacement of E with CH2, in [(2‐NpSi){CH2Sn(S)Ph}3] (1, Np=naphthyl). When expanding one of the CH2 moieties to a C2H4 group, thus generating a R/R’‐T/T’‐E/E’/E’’ cluster composition, we unexpectedly observed a dimerization of the initially formed, yet non‐isolable adamantane‐like cluster [(2‐NpSi){CH2Sn(S)Ph}2{C2H4Sn(S)Ph}] (2) to [(2‐NpSi){CH2Sn(S)Ph}2{C2H4Sn(S)Ph}]2 (3), exhibiting a heretofore unprecedented cluster architecture. Both monomeric 1 and dimeric 3, show white‐light emission as thin films. The nonlinear optical response of the compounds was also modelled with DFT methods.

Retraction Note: Hybrid intelligence system using fuzzy inference in cluster architecture for secured group communication

Retraction Note: Hybrid intelligence system using fuzzy inference in cluster architecture for secured group communication

Electro-functionalized 2D nitrogen-carbon nanosheets decorated with symbiotic cobalt single-atoms/clusters

Two-dimensional (2D) materials loaded with single atoms and clusters are being set at the forefront of catalysis due to their distinctive geometric and electronic features. However, the usually-complicated synthesis procedures impede in-depth clarification of their catalytic mechanisms. To this end, herein we developed an efficient one-step dimension-reduction carbonization strategy, with which we successfully architected a highly-efficient catalyst for oxygen reduction reaction (ORR), featured with symbiotic cobalt single atoms and clusters decorated in two-dimensional (2D) ultra-thin (3.5 nm thickness) nitrogen-carbon nanosheets. The synergistic effects of the two components afford excellent oxygen reduction activity in alkaline media (E1/2=0.823 V vs. RHE) and thereof a high power density (146.61 mW cm−2) in an assembled Zn-air battery. As revealed by theoretical calculations, the cobalt clusters can regulate electrons surrounding those individual atoms and affect the adsorption of intermediate species. As a consequence, the derived active sites of single cobalt atoms lead to a significant improvement of the ORR performance. Thus, our work may fuel interests to delicate architecture of single atoms and clusters coexisting 2D support toward optimal electrocatalytic performance.

ENIAO: Energy Aware Faulty Node Re-Placement Integrated With Duty Cycling and Improved Harmony Search Based Clustering through Adaptive Fish School Search Routing for WSN Optimization

In wireless sensor networks (WSNs), sensor nodes are constrained by resource constraints. The limited energy supply and susceptibility to failure greatly affect WSN lifespan, hindering long-term deployment. ENIAO is an Integrated cross-layer Optimized Routing Approach for WSNs that is fault-tolerant and energy-efficient. A bio-inspired clustering architecture and adaptive duty cycling are incorporated into ENIAO's routing optimization. In a clustering protocol, the network is partitioned, and paths are dynamically optimized within and between clusters. By optimizing active/sleep schedules, duty cycling optimizes energy efficiency. A variety of network conditions have been simulated to assess ENIAO's performance. Regarding fault tolerance and energy consumption, ENIAO significantly prolongs the network lifetime. As compared to benchmark protocols, it achieves higher throughput. As a result of the cross-layer design, ENIAO is automatically adapted to optimize energy usage and routing reliability. In the long run, large-scale IoT deployments are possible with ENIAO due to the integrated approach.

Strain-Induced Structural Rearrangement Towards a White-Light-Emitting Adamantane-Type Cluster Dimer.

Group 14/16 adamantane-type hybrid clusters of the type [(RT)4E6] (T=group 14 element, E=group 16 element, R=organic group) have been reported to emit white-light when irradiated in an amorphous state with a continuous-wave (CW) infrared laser diode. This effect is enhanced if the cluster core is varied from a binary to a more complex composition. To further explore this phenomenon, we synthesized clusters with a multinary R/R'-T/T'-E/E' composition, including isolobal replacement of E with CH2, in [(2-NpSi){CH2Sn(S)Ph}3] (1, Np=naphthyl). When expanding one of the CH2 moieties to a C2H4 group, thus generating a R/R'-T/T'-E/E'/E'' cluster composition, we unexpectedly observed a dimerization of the initially formed, yet non-isolable adamantane-like cluster [(2-NpSi){CH2Sn(S)Ph}2{C2H4Sn(S)Ph}] (2) to [(2-NpSi){CH2Sn(S)Ph}2{C2H4Sn(S)Ph}]2 (3), exhibiting a heretofore unprecedented cluster architecture. Both monomeric 1 and dimeric 3, show white-light emission as thin films. The nonlinear optical response of the compounds was also modelled with DFT methods.

Hybrid Boosted Chameleon and modified Honey Badger optimization algorithm-based energy efficient cluster routing protocol for cognitive radio sensor network

Single clustering protocols cannot meet the event-driven and time-triggered traffic requirements of Cognitive Radio Sensor Networks (CRSNs). The long wait between the completion of events and the process of clustering and searching for accessible routes results in increased time for information transmission. This paper proposed a Hybrid Boosted Chameleon and Modified Honey Badge optimization Algorithm-based Energy Efficient cluster routing protocol (HBCMHBOA) for handling the issues of traffic driven information transfer with energy efficiency in the CRSNs. This HBCMHBOA is proposed as one among few event-driven and time-triggered clustering protocol for the requirements of CRSNs. The integration of Boosted Chameleon and Modified Honey Badge optimization Algorithm is adopted for determining optimal number of clusters and constructs the structure of primitive clusters in an automated way to serve the time-triggered traffic in a periodic manner. It adopted priority-based schedule and its associated frame structure for guaranteeing reliable event-driven information delivery. It leveraged the merits of time-triggering for the construction of clustering architecture and confirmed than none of the cluster construction and selection of routes are facilitated after the emergent events. This characteristic helps in permitting only the nodes and their associated Cluster Heads (CHs) of CRSNs to discover emergent events. It facilitates the coverage of a fewer nodes, especially when sink is positioned in a corner to minimize the delay and node energy consumption. The simulation results of the proposed HBCMHBOA confirmed a reduction in total energy consumption and number of covered nodes on an average of 34.12 %, and 26.89 % than the prevailing studies.

Mathematical modelling of fracture waves in a blocky medium with thin compliant interlayers.

Physical mechanisms that contribute to the generation of fracture waves in condensed media under intensive dynamic impacts have not been fully studied. One of the hypotheses is that this process is associated with the blocky structure of a material. As the loading wave passes, the compliant interlayers between blocks are fractured, releasing the energy of self-balanced initial stresses in the blocks, which supports the motion of the fracture wave. We propose a new efficient numerical method for the analysis of the wave nature of the propagation of a system of cracks in thin interlayers of a blocky medium with complex rheological properties. The method is based on a variational formulation of the constitutive relations for the deformation of elastic-plastic materials, as well as the conditions for contact interaction of blocks through interlayers. We have developed a parallel computational algorithm that implements this method for supercomputers with cluster architecture. The results of the numerical simulation of the fracture wave propagation in tempered glass under the action of distributed pulse disturbances are presented. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

Secured lightweight authentication for 6LoWPANs in machine-to-machine communications

The development of machine-to-machine (M2M) technologies is becoming increasingly important in the rapidly growing domain of wireless sensor networks (WSNs) and the Internet of Things (IoT). Adopting IPv6 over 6LoWPANs (Low-Power Wireless Personal Area Networks) is instrumental in communicating across diverse domains within WSNs, albeit with its challenges. Particularly, resource limitations and security vulnerabilities remain significant concerns. 6LoWPAN-based M2M protocols that rely on authentication and key establishment schemes (AKE) often fall short due to inadequate security issues and excessive resource requirements. This paper addresses these challenges by introducing a secure and resource-efficient framework—Lightweight AKE for 6LoWPAN Nodes (LAKE-6LN). LAKE-6LN capitalizes on the clustering architecture's merits and contrasts conventional router-centric approaches. To ensure lightweight and efficient operation, it uses hash functions, XOR functions, and symmetric encryption techniques. Pseudo-identity, sequence tracking numbers, and secure parameters ensure privacy and protection against attacks, including traceability, perfect forward secrecy, ephemeral secret leakage, and secure the session key. An informal analysis of LAKE-6LN's security confirms that compliance with all essential security properties has been achieved. In addition, the framework's logical robustness and security analysis are rigorously verified using BAN logic, AVISPA, and Scyther tools. LAKE-6LN has demonstrated superior performance over related schemes, demonstrating a reduction in storage costs (by 33.33 % to 85.71 %), computational overhead (by 14.28 % to 95.97 %), communication overhead (by 16.12 % to 51.85 %), and energy consumption (by 22.04 % to 99.40 %). In our comparative analysis, LAKE-6LN demonstrates its resilience against various security threats, demonstrating its potential to secure 6LoWPAN networks in M2M.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Discovery of novel cellobiose lipid gene clusters from Basidiomycetes: How chemical variation is reflected in gene cluster architecture.

Cellobiose lipids are surface-active compounds or biological detergents produced by distinct Basidiomycetes yeasts, of which the most and best-described ones belong to the Ustilaginomycetes class. The molecules display slight variation in congener type, which is linked to the hydroxylation position of the long fatty acid, acetylation profile of the cellobiose unit, and presence or absence of the short fatty acid. In general, this variation is strain specific. Although cellobiose lipid biosynthesis has been described for about 11 yeast species, hitherto only two types of biosynthetic gene clusters are identified, and this for only three species. This work adds six more biosynthetic gene clusters and describes for the first time a novel type of cellobiose lipid biosynthetic cluster with a simplified architecture related to specific cellobiose lipids synthesized by Trichosporonaceae family members.

Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties

Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties

Deep Spectral Clustering With Constrained Laplacian Rank.

Spectral clustering (SC) is a well-performed and prevalent technique for data processing and analysis, which has attracted significant attention in the field of clustering. While the scalability and generalization ability of this method make it prohibitive for the large-scale dataset and the out-of-sample-extension problem. In this work, we propose a new efficient deep clustering architecture based on SC, named deep SC (DSC) with constrained Laplacian rank (DSCCLR). DSCCLR develops a self-adaptive affinity matrix with a clustering-friendly structure by constraining the Laplacian rank, which greatly mines the intrinsic relationships. Meanwhile, by introducing a simple fully connected network with an orthogonality constraint on the last layer, DSCCLR learns discriminative representations in a short training time. The proposed method has the following salient properties: 1) it overcomes limited generalization ability and scalability of the existing DSC methods; 2) it explores the intrinsic relationship between samples in the affinity matrix, which maintains the latent manifold of data as much as possible; and 3) it alleviates the complexity of eigendecomposition via a simple but effective fully connected network. The extensive empirical results demonstrate the superiorities of DSCCLR over other 17 clustering methods.

Voronoi diagrams and tree structures in HRP-EE: Enhancing IoT network lifespan with WSNs

Within the domain of the Internet of Things (IoT), wireless sensor networks (WSNs) play a pivotal role, facilitating advancements in sectors such as smart urban infrastructures, intelligent healthcare systems, and residential automation. Despite their versatility, WSNs grapple with challenges like limited energy reserves and constrained computational capacities, making energy conservation a paramount concern for IoT deployments underpinned by WSNs. This study introduces the Hybrid Routing Protocol for Efficient Energy (HRP-EE) designed to enhance the operational efficiency of WSNs. The HRP-EE protocol unfolds over three meticulous stages: the selection of cluster heads, the formation of clusters, and the establishment of routing pathways. Initially, the protocol evaluates nodes based on their residual energy and their Euclidean distance to the central sink (or gateway) device to designate apt cluster heads. Subsequently, these chosen cluster heads are integrated into the Voronoi diagram as central nodes to orchestrate cluster architectures. In the terminal stage, an innovative hybrid algorithm is instituted. This algorithm amalgamates the principles of the Minimum Spanning Tree for structuring intra-cluster communication trees and Dijkstra’s algorithm to ascertain the most efficient paths for inter-cluster data transmission from cluster heads to the sink device. The primary objective of this protocol is the judicious utilization of the sensor nodes’ energy, thereby optimizing the overall network longevity. To assess the proficiency of HRP-EE, we executed a series of simulations using NS2. Comparative analyses reveal that HRP-EE outperforms existing protocols such as LEACH-VA, PEGCP, and TBC, delivering superior energy efficiency, extended network lifespan, and enhanced throughput across both homogeneous and heterogeneous network architectures.

Inappropriate YouTube content detection and classification by using proposed novel auto-determined k-means clustering and PDBRNN architecture

To ensure a safe and pleasant user experience while watching content on YouTube, it is necessary to identify and classify inappropriate content, especially content that is inappropriate for children. In this work, we have concentrated on establishing an efficient system for detecting inappropriate content on YouTube. Most of the work focuses on manual pre-processing; however, it takes too much time, requires manpower support, and is not ideal for solving real-time problems. To address this challenge, we have proposed an automatic preprocessing scheme for selecting appropriate frames and removing unwanted frames such as noise and duplicate frames. For this purpose, we have utilized the proposed novel auto-determined k-means (PADK-means) algorithm. Our PADK-means algorithm automatically determines the optimal cluster count instead of manual specifications. By doing this, we have solved the manual cluster count specification problem in the traditional k-means clustering algorithm. On the other hand, to improve the system’s performance, we utilized the Proposed Feature Extraction (PFE) method, which includes two pre-trained models DenseNet121 and Inception V3 are utilized to extract local and global features from the frame. Finally, we employ a proposed double-branch recurrent network (PDBRNN) architecture, which includes bi-LSTM and GRU, to classify the video as appropriate or inappropriate. Our proposed automatic preprocessing mechanism, proposed feature extraction method, and proposed double-branch RNN classifier yielded an impressive accuracy of 97.9%.

Cultivation, chemistry, and genome of Psilocybe zapotecorum

AbstractPsilocybe zapotecorum is a strongly blue-bruising psilocybin mushroom used by indigenous groups in southeastern Mexico and beyond. While this species has a rich history of ceremonial use, research into its chemistry and genetics has been limited. Herein, we report on mushroom morphology, cultivation parameters, chemical profile, and the full genome sequence of P. zapotecorum. First, we detail growth and cloning methods that are simple, and reproducible. In combination with high resolution microscopic analysis, the strain was identified by DNA barcoding, confirming the field identification. Full genome sequencing reveals the architecture of the psilocybin gene cluster in P. zapotecorum, and can serve as a reference genome for Psilocybe clade I. Characterization of the tryptamine profile revealed a psilocybin concentration of 17.9 ± 1.7 mg/g, with a range of 10.6–25.7 mg/g (n = 7), and similar tryptamines (psilocin, baeocystin, norbaeocystin, norpsilocin, aeruginascin, and 4-HO-tryptamine) in lesser concentrations for a combined tryptamine concentration of 22.5 ± 3.2 mg/g. These results show P. zapotecorum to be a potent and chemically variable Psilocybe mushroom. Chemical profiling, genetic analysis, and cultivation assist in demystifying these mushrooms. As clinical studies with psilocybin gain traction, understanding the diversity of Psilocybe expands the conversation beyond the molecule.

Modernization of the organizational structure of the North Caucasus tourism cluster

To develop a modernized model of the architecture of the tourist cluster of the North Caucasus Federal District in modern economic realities, taking into account the multifaceted tourism potential of the territory, including opportunities for the development of skiing tourism, as well as sanatorium‐resort recreation with a combination of business visits, cultural and historical excursions, organisation of sports, events and other niche types of tourism. The study was carried out by modeling based on the analysis of data on the natural, historical, socio‐cultural features of the North Caucasus, as well as on the assessment of socio‐economic indicators of the tourism industry over the past five years.The study indicates that for the effective development and promotion of the tourist cluster of the North Caucasus Federal District, it is necessary to expand the range of services provided from homogeneous skiing tourism to multidirectional initiatives, combining the synergetic effect of deepening technological ties and cooperation of residents of the tourist cluster regions.The introduction of a matrix model of a tourism cluster of the North Caucasus in the management of the activities and marketing promotion of the tourism industry in the southern region of Russia would make the North Caucasus an outstanding centre of tourist attraction, a worthy competitor of world tourist resorts.

Monitoring, Modeling Techniques Help Optimize Eagle Ford Completions

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212367, “Sealed Wellbore Pressure Monitoring and Calibrated Fracture Modeling: The Next Step in Unconventional Completions Optimization,” by Karen Olson, SPE, Joshua Merritt, and Rair Barraez, SPE, Well Data Labs, et al. The paper has not been peer reviewed. _ Sealed wellbore pressure monitoring (SWPM) has been used across North and South America, with more than 16,000 stages monitored. A recent development is the added capability of a fracture model that can automatically history-match the volume to first response (VFR) determined from SWPM. The complete paper’s focus is a case study of the Department of Energy Eagle Ford refracturing project, where a range of completion designs were trialed while monitoring offset SWPM and fiber-optic strain. Introduction of the Hydraulic Fracture Test Site 1 (HFTS1), Phase 3 Background. The HFTS1 Phase 3 was conducted at the Zgabay unit in northwest DeWitt County, Texas. This unit was initially developed with horizontal multistage completions and has been producing since 2013. The 10 original wells in the unit, and the four new drilled wells, are landed in the approximately 100-ft-thick Lower Eagle Ford Shale. Of the four new drills, Well 14H is a dedicated observation well instrumented with various diagnostics, including a permanent fiber-optic cable. Well 12H, another new drill, was monitored using a wireline-deployable fiber. Both of these wells featured downhole gauges, while Wells 11H and 13H featured surface gauges. The focus of the refracturing strain diagnostics— SWPM and fiber—was to characterize new fracture growth and interaction with pre-existing fractures during the liner refracturing of parent Wells 5H and 3H. Completion Designs. Multiple stage and cluster architectures were tested on the Zgabay Wells 3H and 5H. Four single-cluster stages were performed per well. Both a high (2,000-psi target) and low (500-psi target) perforation friction design was tested on seven-, 12-, and 22-cluster stages. Well 3H maintained a slightly tighter cluster spacing (10–15 ft) compared with the 12–20 ft spacing of Well 5H. The main method of measuring the effect of different designs is strain monitoring using SWPM and fiber optics. Technology Background SWPM. SWPM uses an uncompleted wellbore to monitor fracture intersections from offset-well stimulations. The monitor well collecting the SWPM data cannot be connected to a formation through perforations or other types of access points. The wellbore should be filled with low-compressibility fluid, with pressure added to the monitor well. Fractures intersecting the sealed wellbore cause local deformation, which results in a small volume reduction in the closed system and generates a discernable and distinct pressure response. The total slurry injected into the stage of the active well when the first fracture arrival is identified using SWPM is the VFR. VFRs are used as a proxy for cluster efficiency and to calculate fracture-growth rates. The field execution of SWPM does not require any tools to enter the wellbore. Multiple sealed wellbores can be used as monitor wells for a single treatment well.

Swift : a modern highly parallel gravity and smoothed particle hydrodynamics solver for astrophysical and cosmological applications

ABSTRACT Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this paper, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid shared- and distributed-memory task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with ≈300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift.

Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication

Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch combines three strategies to efficiently utilize homomorphic encryption in machine learning: (1) It optimizes the feature vector for a distributed architecture, processing the first fully connected layer (FC-16) in plaintext on the client side and the subsequent layer (FC-1) post-encryption on the server, thereby minimizing encrypted computations; (2) It employs a homomorphic encryption-compatible data compression technique capable of handling 8,192 authentication results concurrently; and (3) It utilizes a clustered server architecture to simultaneously process authentication results, thereby enhancing scalability with increasing user numbers. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover, Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its privacy-focused design, high accuracy, and efficiency, Blind-Touch is a promising alternative to conventional fingerprint authentication for web and cloud applications.

Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering

Multi-relational clustering is a challenging task due to the fact that diverse semantic information conveyed in multi-layer graphs is difficult to extract and fuse. Recent methods integrate topology structure and node attribute information through graph filtering. However, they often use a low-pass filter without fully considering the correlation among multiple graphs. To overcome this drawback, we propose to learn a graph filter motivated by the theoretical analysis of Barlow Twins. We find that input with a negative semi-definite inner product provides a lower bound for Barlow Twins loss, which prevents it from reaching a better solution. We thus learn a filter that yields an upper bound for Barlow Twins. Afterward, we design a simple clustering architecture and demonstrate its state-of-the-art performance on four benchmark datasets. The source code is available at https://github.com/XweiQ/BTGF.