Scheme For Networks Research Articles

The neural networking strategy results in the tri‐hybrid nanofluid flowing over a slippery flat plate to act as an antimicrobial agent through solar radiation

AbstractHybrid nanofluids are employed to enhance the antibacterial effect produced by solar radiation. Antimicrobial properties are present in metal nanoparticles (such as silver, copper, or zinc) or metal oxides (such as titanium dioxide or zinc oxide) when exposed to solar radiation. Nanomaterials that have antimicrobial properties are selected from the available literature. A solar sheet with an inclined plane has been chosen and filled with tri‐hybrid nanofluids (THNFs). Hybrid nanofluids including (CuO), Copper oxide, TiO2 (Titanium oxide), and SiO2 (Silicon dioxide), are selected from metal and metal oxide classes including water as a base fluid. The antimicrobial action caused by solar radiation is enhanced by the slip boundaries and variable porous space. The influence of flow and thermal fields on isotherms, velocities, flow lines, and Nusselt numbers are considered. The transformed system of differential equations is solved by the Control Volume Finite Element Method (CVFEM) and RK‐4 technique. The nanoparticulate volume fraction of CuO, TiO2, and SiO2 is largely responsible for the enhancement in the heat transfer rate (HT), as observed. Improved thermal performance is achieved through the flow of THNFs, which in turn acts as an antimicrobial agent. Increasing values of parameters lead to an improvement in the heat transfer rate, which in turn decreases microbial activity.

Radio Frequency Interference Detection and Automatic Modulation Recognition Based on Mask RCNN

The increasing complexity of wireless communication conditions presents noteworthy obstacles to the adaptability and flexibility of cognitive radio systems. The latter can alleviate the problem of spectrum shortages through proper spectrum management. However, cognitive radio has difficulty distinguishing between signals of interest and interference, which can be a general enemy since it is undesirable because it influences the signal's quality. Efficient detection and characterization of interference in wireless communication networks is critical for ensuring strong security. However, in this research, we use the Mask R-CNN methodology to present a new concept for automatic modulation recognition and radio frequency interference detection. Moreover, this algorithm can segment, detect, and recognize several types of interference that can affect wireless communication systems, such as chirp interference (CI), continuous wave interference (CWI), and multiple continuous wave interference (MCWI) within the signal of the interest (SOI), as well as the modulation kind present in the SoI. Overall, the combination of these distinct techniques can be very valuable in the field of signal processing, especially in anti-jamming strategies in wireless networks. Moreover, the proposed approach showcases exceptional performance in the validation dataset. For radio frequency interference detection, it achieves a mean average precision (mAP) of 0.946 and a mean average recall (mAR) of 0.954. For automatic modulation recognition, it attains a mAP of 0.898 and a mAR of 0.916.

Study on the planning and scheduling strategies of the hydrogen supply network for road transportation in deep decarbonization scenarios

As the energy decarbonization continues to advance, hydrogen is gaining momentum. With the increasing demand for hydrogen and the lack of hydrogen infrastructure, it is urgent to solve the problem of hydrogen supply network layout planning. In order to reduce redundant construction of hydrogen supply network infrastructure and increase the rationality of its layout, this paper conducts research on the optimal layout planning of hydrogen supply network infrastructure in the transportation sector. By constructing a two-stage real-time scheduling model of hydrogen supply network, the unit cost composition of hydrogen supply network is more complete and accurate, and the location, technology type and quantity of each link of hydrogen supply network are obtained. The results show that the unit cost of China's hydrogen supply network is 27.8 CNY/kg in 2035 and 19.04 CNY/kg in 2050 under the baseline scenario. Compared with the results of existing literature, the two-stage real-time scheduling model can reduce by 7 % of the hydrogen energy supply network unit cost by decreasing the transportation vehicles and the investment capacity of fixed hydrogen storage equipment. The proposed method of the hydrogen supply network planning can provide policy-makers with the selection of sustainability pathways for dynamic hydrogen infrastructure development planning.

IMCSN: An improved neighborhood aggregation interaction strategy for multi-scale contrastive Siamese networks

Graph contrastive learning is an effective method for enhancing graph representation by maximizing the similarity of representations between analogous graphs while minimizing the similarity between dissimilar ones. A common approach to improve the representation capabilities of graphs involves data augmentation, which generates additional training samples through transformations applied to the original graphs. However, traditional data augmentation techniques, such as random deletion of nodes or edges, often compromise the structural integrity of graphs, result in loss of crucial information, and lead to representation instability. To overcome these drawbacks, this study introduces a novel data augmentation paradigm, the integration of directed random noise (IDR), which incorporates controlled random noise to optimize the augmentation objectives. IDR enhances the diversity and robustness of representations without sacrificing the structural integrity of the graphs. This approach significantly improves the performance of graph contrastive learning, avoiding the common issues of graph structure damage and information loss associated with conventional methods. To further refine the model, this paper proposes an improved multiscale contrastive Siamese network framework that employs three Siamese networks to process different views of input graphs. It utilizes cross-network and cross-view contrastive learning objectives to optimize graph representations, leveraging the complementary information between different views to maximize the consistency of representations among similar graphs. This enhancement improves the quality and generalizability of graph representations. Additionally, the introduction of a self-supervised loss function based on graph reconstruction within the loss framework capitalizes on the structural similarity between the original and reconstructed graphs to further refine graph representations. This loss function ensures that the global view retains more structural information from the original graph, thus enhancing the complementarity between the global and local views. The effectiveness and stability of this research framework are demonstrated through node classification tasks and visualizations on six real-world datasets, comparing favorably against existing methods such as MERIT and GraphVAT. The proposed model achieves accuracy improvements of 3.21 % and 3.71 % on the Cora dataset and 1.12 % and 1.42 % on the CiteSeer dataset. Additionally, it ranks first in an average accuracy comparison across six datasets, with scores of 80.6 % and 52.67 %. These results underscore the robustness and efficacy of the proposed model in self-supervised graph representation learning, offering substantial advancements over existing techniques.

Joint spectrum and power allocation scheme based on value decomposition networks in D2D communication networks

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity.

Protection and Fault Isolation Scheme for DC Distribution Network Based on Active Current-Limiting Control

Protection and Fault Isolation Scheme for DC Distribution Network Based on Active Current-Limiting Control

Optimization scheduling strategy for flexible interconnected distribution network considering transformer offline

Based on the flexible interconnection technology with intelligent soft open point (SOP), new opportunities have arisen to address issues such as complex power flow and uneven load rates in existing distribution networks. However, most existing research focuses on load balancing strategies and N-1 fault tolerance, without fully considering the impact of flexible access and offline of distribution transformers on the losses in the distribution network. Therefore, this paper analyzes the impact of structural adjustments to the distribution transformer interconnection system on the comprehensive losses of the distribution network, and concludes that under low-load conditions, the offline of lightly loaded transformers optimizes the comprehensive losses of the distribution network. However, the offline of lightly loaded transformers leads to an increase in the remaining transformer load rates in the distribution station areas. To address this shortfall, this paper proposes an optimization scheduling strategy for flexible interconnected distribution networks considering transformer offline, by simultaneously utilizing shared energy storage on the DC side to balance transformer load rates and minimize losses. Finally, case studies based on an IEEE 18-node interconnected system are conducted to validate the proposed approach under two different scenarios. The results demonstrate that the proposed control strategy outperforms existing strategies by optimizing the comprehensive losses of the system, thereby enhancing its operational economy and reliability.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BackgroundRNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.ResultsHere, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.ConclusionsScan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Tracing contaminants of emerging concern and their transformations in the whole treatment process of a municipal wastewater treatment plant using nontarget screening and molecular networking strategies

This study employed nontarget screening with high-resolution mass spectrometry and molecular network strategy to characterize the occurrence and tranformation of contaminants of emerging concern (CECs) through a wastewater treatment plant in Guangzhou. We detected 70,631 compounds in positive mode and 14,423 in negative mode in influent, from which 94.5 % of these compounds were successfully eliminated after treatment. Among them, 510 chemicals were identified, with pharmaceuticals being the largest category excluding natural products, accounting for 146 compounds. And 29 CECs were semiquantified with concentrations ranging from 2.80 ng/L (Fluconazole) to 10,351 ng/L (Nicotine). The removal efficiency varied: 60 compounds were easily removable (>90 % removal), 17 were partially removable (40–90 % removal), and 44 were non-degradable (<40 % removal). Additionally, we tentatively identified transformation products (TPs) of CECs using a molecular network analysis, revealing over 20,000 compound pairs sharing common fragments, with 191 compounds potentially linked to 47 level 1 compounds, suggesting their role as TPs of CECs. These findings illuminated the actual treatment efficiency of wastewater treatment plants for CECs and the potential TPs, offering valuable insights for future improvements in wastewater management practices.

Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated.

Exploring optimization strategies for support vector regression networks in predicting power consumption

Exploring optimization strategies for support vector regression networks in predicting power consumption

ScDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

An INS/UWB fusion localization scheme for wireless sensor network

Abstract As the demand for indoor services relying on location information grows, achieving precise indoor positioning is becoming an urgent issue to address. The ultra-wide band (UWB) accuracy can reach centimeter level, but is susceptible to non-line-of-sight (NLOS) effects. The inertial navigation system (INS) is not affected by the environment, but the error will diverge over time. Therefore, this paper focuses on the UWB/INS combined positioning system, which can combine the advantages of both. We propose a loosely-coupled INS/UWB indoor localization scheme based on factor graph optimization (FGO). Firstly, an agglomerative hierarchical clustering based NLOS mitigation method is applied to preprocess the UWB raw measurement data. The position estimates under UWB are then obtained using an improved extended Kalman filter to mitigate NLOS errors and provide more accurate location data for combined navigation. Then, we build the IMU preintegration factor and the zero-bias factor. Finally, FGO is used to fuse the information to get the final location. Designed experiments demonstrate the superiority of the algorithm. The simulation and experimental results show that the proposed scheme outperforms the comparison algorithms.

DRAINAGE SYSTEM TRANSFORMATION TOWARDS EFFECTIVE FLOOD CONTROL WITH EPA SWMM

The village of Kunjir often experiences floods caused by suboptimal drainage systems, land use changes, and revetment construction. The utilization of EPA SWMM proves to be an interesting solution to address flood issues. The research aims to analyze the drainage channel capacity using the EPA SWMM program, assess the tidal influence on the channels, and provide solutions to the existing problems. The method involves hydrological and hydraulic analysis using Microsoft Excel and the EPA SWMM program. Rainfall and topographic data are utilized to determine flood discharge, while existing channel data are used to calculate channel capacity. HWL values are used to analyze the tidal influence on the channels. The modeling process involves creating a network scheme and inputting data into the existing channels. The program is executed, and evaluations are conducted on problematic channels. Redesign planning is implemented by altering channel dimensions and re-running the simulations. The conclusion indicates that channels 2B, 2B1, 2B1 Left, 2C, and 2D cannot accommodate the flow discharge, tidal influence affects channel 1C, Box Culvert, and Downstream River, and solutions include enlarging channels, adding bottom slope to channels, regular maintenance, and installing floodgates to mitigate tidal effects.

Resilience enhancement strategies for power distribution network based on hydrogen storage and hydrogen vehicle

AbstractIn light of the increasing hydrogen permeability in distribution networks as a means to cope with extreme events and improve network resilience, this paper introduces a novel strategy for enhancing power distribution network resilience. It outlines a comprehensive approach that focuses on dispatching hydrogen storage (HS) and hydrogen vehicle (HV) within hydrogen penetrated distribution systems (HPDS), segmenting the strategy into pre‐disaster and post‐disaster stages. Firstly, in the pre‐disaster stage, models for HS and HVs are established to gather operational data and facilitate rapid post‐disaster response, alongside a coupled electric grid and road network model for optimising HV routing and dispatch. Subsequently, the post‐disaster stage focuses on a scheduling model that aims to minimise load power losses and economic costs, balancing immediate power support with cost‐effectiveness through detailed analysis of HS and HV dispatch strategies. Finally, this paper demonstrates the effectiveness of this strategy via a case study, highlighting significant improvements in network resilience and recovery and underscoring the potential of hydrogen technologies in enhancing infrastructure resilience.

Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

Developing Business Strategies to Grow the Business of PT Asia Civil Indonesia

The significant market growth translate into business opportunity for construction such toll road, office building, bridge, LRT, High speed train, including huge opportunity in IKN and data centre with potential The Indonesia Construction Market size is estimated at USD 264.34 billion in 2023, and is expected to reach USD 379.41 billion by 2028, growing at a CAGR of 7.50% during the forecast period (2023-2028). This opportunity in industry up trend but no impact in growth PT Asia Civil Indonesia and company need to renew and review their focus on business model development with business strategy approach and need to evaluation they are market growth and cost structure. In qualitative research utilizing depth interviews, the research design serves as the blueprint guiding the investigation’s execution. The primary objective of this research approach is to delve deeply into participants’ thoughts, feelings, and experiences to gain rich and nuanced insights into the phenomena under study. In conclusion, the research conducted on PT ACI underscores the importance of strategic initiatives in enhancing its business operations within the construction industry. Through a comprehensive analysis of current business practices and market dynamics, PT ACI can identify areas for improvement and leverage opportunities for growth. The implementation plan outlined in the study emphasizes the significance of networking strategies and technology adaptation in achieving business objectives. Networking plays a pivotal role in expanding PT ACI’s reach within the industry, facilitating valuable connections with potential clients, partners, and industry peers. By actively participating in industry events, forums, and networking opportunities, PT ACI can build relationships, gain insights into market trends, and secure new business opportunities.

Cascading failure model and resilience-based sequential recovery strategy for complex networks

Complex networks, which exhibit high connectivity, self-organization, small-world properties, and heterogeneity, are susceptible to the rapid spread of local failures, often resulting in cascading effects throughout the entire system. The paper introduces a cascading failure model based on biased random walks that incorporate betweenness centrality and the power-law distribution of node degrees. This model is used to investigate cascade failures triggered by extreme fluctuations in load that follow a Poisson distribution. Furthermore, we propose a resilience-based sequential recovery strategy that accounts for varying node recovery time and resource limitations, setting an upper limit on the number of nodes that can be in recovery simultaneously. The network’s robustness improves, and the variation in the power-law exponent during cascading failures and recovery decreases when the betweenness bias parameter is set to 1 instead of -1. The capacity parameter has the most significant and direct effect on improving the network’s robustness. Reducing node recovery time can improve the network’s initial invulnerability; however, its impact on final residual resilience remains limited. The power-law exponent of the initial network significantly affects residual resilience during the recovery process, with higher exponents leading to improved network performance. An appropriate increase in the number of nodes that can be in recovery simultaneously can enhance the overall recovery performance of the network. Extensive comparative simulations reveal substantial advantages of our proposed recovery strategy in enhancing network recovery.

Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks

Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks