Hierarchical Network Research Articles

Mussel‐inspired self‐assembly of silver nanoclusters into multifunctional silver aerogels for enhanced catalytic and bactericidal applications

AbstractSilver nanoclusters (AgNCs) have shown broad application prospects in catalysis, sensing, and biological fields. However, the limited stability of AgNCs has become the main challenge restricting their practical application in complex environments. Herein, a mussel‐inspired, dopamine‐assisted self‐assembly approach is reported to fabricate 3D AgNC aerogels (PDA/AgNCs), which possess significantly enhanced structural stability and synergistic functional properties. The prepared AgNC aerogels display a hierarchical network structure with an ultrafine ligament size of 10.3 ± 1.2 nm and a high specific surface area of 50.7 m2 g−1. The gelation mechanism is elucidated by in‐depth characterization and time‐lapse monitoring of the gelation process vis spectroscopic and microscopic approaches. Owing to the distinct features of aerogels and the synergistic effect of AgNCs and PDA, the fabricated aerogels can not only efficiently decolorize dyes with a faster kinetic than individual AgNCs, but also exhibit remarkable broad‐spectrum antimicrobial activity. Consequently, a conceptual water‐treatment device is established by depositing PDA/AgNC aerogels on the cotton substrate, which shows good performance in both catalytic dye degradation and bacterial killing in the flowing system. This mussel‐inspired self‐assembly strategy has great potential in developing robust AgNC‐based functional materials, which also provides a new guideline for designing sophisticated materials with integrated functions and synergistic properties.

Identification of Biomarkers for Severity in COVID-19 Through Comparative Analysis of Five Machine Learning Algoritms

Background COVID-19 is a global public health problem. Aim The main objective of this research is to evaluate and compare the performance of the algorithms: Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Network, using metrics such as precision, recall, F1-score and accuracy. Methods A dataset (n=138) was used, with numerical and categorical variables. The algorithms Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Network were considered. These were trained using an 80-20 ratio. The following metrics were evaluated: precision, recall, F1-Score, and 5-fold stratified cross-validation. Results The Random Forest algorithm was superior, achieving a maximum score of 0.9727 in cross-validation. The correlation analysis identified ferritin (0.8277) and oxygen saturation (-0.6444). The heuristic model was compared with metaheuristics models. Models obtained through metaheuristic search could maintaining the metrics with 3 variables and stable weight distribution. A perplexity analysis it allows to differentiate between the best models. The features of creatinine and ALT are highlighted in the model with the best CV score and the lowest perplexity. Conclusion Comparative analysis of different classification models was carried out to predict the severity of COVID-19 cases with biological markers.

State diagrams to determine tree tensor network operators

This work is concerned with tree tensor network operators (TTNOs) for representing quantum Hamiltonians. We first establish a mathematical framework connecting tree topologies with state diagrams. Based on these, we devise an algorithm for constructing a TTNO given a Hamiltonian. The algorithm exploits the tensor product structure of the Hamiltonian to add paths to a state diagram, while combining local operators if possible. We test the capabilities of our algorithm on random Hamiltonians for a given tree structure. Additionally, we construct explicit TTNOs for nearest neighbour interactions on a tree topology. Furthermore, we derive a bound on the bond dimension of tensor operators representing arbitrary interactions on trees. Finally, we consider an open quantum system in the form of a Heisenberg spin chain coupled to bosonic bath sites as a concrete example. We find that tree structures allow for lower bond dimensions of the Hamiltonian tensor network representation compared to a matrix product operator structure. This reduction is large enough to reduce the number of total tensor elements required as soon as the number of baths per spin reaches 3.

Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformer

Underwater images can suffer from visibility and quality degradation due to the attenuation of propagated light and other factors unique to the underwater setting. While Retinex-based approaches have shown to be effective in enhancing the underwater image quality, the use of hand-crafted priors and optimization-driven solutions often prevent the adaptivity of these methods to different types of underwater images. Moreover, the commonly-used white balance strategy which often appears in the preprocessing stage of the underwater image enhancement (UIE) algorithms may give rise to unwanted color distortions due to the fact that wavelength-dependent light absorption is not taken into account. To overcome these potential limitations, in this paper, we present an effective UIE model based on adaptive color correction and data-driven Retinex decomposition. Specifically, an adaptive color balance approach which takes into account different attenuation levels for light with different wavelengths is proposed to adaptively enhance the three color channels. Furthermore, deep neural networks are employed for the Retinex decomposition, formulating the optimization problem as an implicit-prior-regularized model which is solved by learning the priors from a large training dataset. Finally, a hierarchical U-shape Transformer network which uses hierarchically-structured multi-scale feature extraction and selective feature aggregation is applied to the decomposed images for contrast enhancement and blur reduction. Experimental results tested on six benchmark underwater image datasets demonstrate the effectiveness of the proposed UIE model.

An energy-aware cluster-based routing in the Internet of things using particle swarm optimization algorithm and fuzzy clustering

The effectiveness and longevity of IoT infrastructures heavily depend on the limitations posed by communication, multi-hop data transfers, and the inherent difficulties of wireless links. In dealing with these challenges, routing, and data transmission procedures are critical. Among the fundamental concerns are the attainment of energy efficiency and an ideal distribution of loads among sensing devices, given the restricted energy resources at the disposal of IoT devices. To meet these challenges, the present research suggests a novel hybrid energy-aware IoT routing approach that mixes the Particle Swarm Optimization (PSO) algorithm and fuzzy clustering. The approach begins with a fuzzy clustering algorithm to initially group sensor nodes by their geographical location and assign them to clusters determined by a certain probability. The proposed method includes a fitness function considering energy consumption and distance factors. This feature guides the optimization process and aims to balance energy efficiency and data transmission distance. The hierarchical topology uses the advanced PSO algorithm to identify the cluster head nodes. The MATLAB simulator shows that our method outperforms previous approaches. Various metrics have demonstrated significant improvements over DEEC and LEACH. The method reduces energy consumption by 52% and 16%, improves throughput by 112% and 10%, increases packet delivery rates by 83% and 15%, and extends the network lifespan by 48% and 27%, respectively, compared to DEEC and LEACH approaches.

Self‐Gelling Quinone‐Based Wearable Microbattery

AbstractWearable power sources are envisioned since their development promises to speed up the widespread application of wearable devices in different areas, such as healthcare, smart‐city management, and robotics. Here, the 4′‐((9,10‐anthraquinone‐2‐yl)oxy)butyrate (2‐BEAQ), an anthraquinone derivative, is synthesized, and further applied for producing a redox‐active shear‐thinning hydrogel (BEAQ‐gel). The gel comprises cylindrical aggregates of 2‐BEAQ molecules dispersed within a water matrix, interconnected through ionic, ion‐dipole, and hydrogen bonding interactions. BEAQ‐gel also presents interesting rheological characteristics and composition tunability since it can be produced in a large range of concentrations. This improved redox 3D hierarchical network also makes the network capable of retaining high quantities of potassium hydroxide, thereby enhancing conductivity. By coupling BEAQ‐gel with Ferricyanide the development of a wearable battery is demonstrated, which exhibits an output voltage of 0.89 V even when bent at a 180° angle, making it suitable for powering wearable devices. This work presents an innovative alternative for the production of wearable devices, from the design of the anode and cathode materials to the wearable casing and demonstrates the use of redox‐active low‐molecular‐weight‐gel (LMWG) as an active material of a microbattery giving a valuable glimpse to the further development of wearable energy storage devices.

Prediction of magnetic nature of oxide compositions by using machine learning models

The magnetic nature of oxide compositions carries most significance in the applications of science and technology, promoting the development of electronic devices, sensors, and data storage technologies. However, the field encounters a remarkable research gap regarding the use of machine learning techniques for accurately predicting the magnetic behavior of compositions. This study tries to fill the gap by looking into the efficacy of machine learning models for predicting the magnetic nature of oxide compositions, which has been relatively not explored compared to traditional analytical methods. The study seeks to identify the most accurate and reliable machine learning model for this prediction task by evaluating five machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). According to this study, the RF outperforms other models on three distinct evaluation metrics, having accuracy 89.53%, precision 92.354%, F1 score 91.343%, while ANN outperforms on the Recall metric having a score 91.43%. This paper closes a significant research gap and improves the applications of materials science and industry by presenting a successful machine learning method that uses Random Forest to predict the magnetic properties of oxide compositions.

Time-course swRNA-seq uncovers a hierarchical gene regulatory network in controlling the response-repair-remodeling after wounding

Wounding initiates intricate responses crucial for tissue repair and regeneration. Yet, the gene regulatory networks governing wound healing remain poorly understood. Here, employing single-worm RNA sequencing (swRNA-seq) across 12 time-points, we delineated a three-stage wound repair process in C. elegans: response, repair, and remodeling. Integrating diverse datasets, we constructed a dynamic regulatory network comprising 241 transcription regulators and their inferred targets. We identified potentially seven autoregulatory TFs and five cross-autoregulatory loops involving pqm-1 and jun-1. We revealed that TFs might interact with chromatin factors and form TF-TF combinatory modules via intrinsically disordered regions to enhance response robustness. We experimentally validated six regulators functioning in transcriptional and translocation-dependent manners. Notably, nhr-76, daf-16, nhr-84, and oef-1 are potentially required for efficient repair, while elt-2 may act as an inhibitor. These findings elucidate transcriptional responses and hierarchical regulatory networks during C. elegans wound repair, shedding light on mechanisms underlying tissue repair and regeneration.

Molecular network characteristics and drug resistance analysis of 392 newly reported MSM HIV/AIDS cases in Chongqing, China.

To comprehensively investigate the molecular transmission patterns of HIV-1 genotypes among men who have sex with men (MSM) in Chongqing, we employed 392 pol sequences of MSM to construct a phylogenetic tree and gene transmission network. Among the viral subtypes, CRF07_BC accounted for 73.2% (287/392) and CRF01_AE accounted for 20.7% (81/392), emerging as the predominant subtypes in this investigation. Additionally, we observed the presence of CRF55_01B, subtype B, CRF08_BC and other circulating recombinant forms. The HIV-1 molecular network was constructed with a gene distance threshold of 1.5%, resulting in an entry rate of 61.4% (241/392). Within the network, we identified a total of 23 molecular clusters, with the largest cluster being the CRF07_BC molecular cluster comprising 148 node values. Transmitted drug-resistance (TDR) mutations were found in 4.34% of the cases, with 1.79% associated with protease inhibitors (PIs), 0.51% with nucleoside reverse transcriptase inhibitors (NRTIs), and 2.55% with non-nucleoside reverse transcriptase inhibitors (NNRTIs). Statistical analysis indicated a higher enrollment rate in the HIV-1 molecular network among infected individuals with the CRF07_BC subtype, those identifying with same-sex sexual roles as "vers," and individuals with higher education levels. This suggests the need for strengthened investigation and intervention in this population to prevent the formation of larger transmission clusters. Furthermore, continuous monitoring of the HIV-1 molecular dynamics network is necessary to promptly and accurately track changes in molecular epidemic characteristics.

Effects of network heterogeneity on phases of the quenched contact process in directed complex networks

We investigate the quenched contact process within directed complex networks, including directed random networks and directed scale-free networks. Additionally, we explore the contact process within directed general small-world networks and directed hierarchical modular networks. We observed the presence of a Griffiths phase characterized by continuously varying critical exponents of the order parameter. Notably, the directionality of the networks plays a significant role in influencing the Griffiths phase within the quenched contact process. We suggest that the presence of strongly directed connecting clusters is responsible for the changes in the continuously varying critical exponents of the order parameter within directed networks.

Verifying Hierarchic Multipartite and Network Nonlocalities with a Unified Method

AbstractThe multipartite nonlocality provides deep insights into the fundamental feature of quantum mechanics and guarantees different degrees of cryptography security for potential applications in the quantum internet. Verifying multipartite nonlocal correlations is a difficult task. A unified approach is proposed that encompasses all the quantum characteristics of the multipartite correlated system beyond from fully separable to biseparable no‐signaling correlations. A straightforward method is offered to verify general systems by lifting partial nonlocal correlations. This allows to construct a chained Bell inequality, facilitating the unified verification of hierarchic multipartite nonlocalities. The lifting method is finally applied to verify the correlations derived from quantum networks.

Deep Learning Option Price Movement

Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is currently missing. On an intraday tick-level dataset of options on an exchange traded fund from the Chinese market, we apply a variety of machine learning methods, including decision tree, random forest, logistic regression, and long short-term memory neural network. As machine learning models become more complex, they can extract deeper hidden relationship from input features, which classic market microstructure models struggle to deal with. We discover that the price movement is predictable, deep neural networks with time-lagged features perform better than all other simpler models, and this ability is universal and shared across assets. Using an interpretable model-agnostic tool, we find that the first two levels of features are the most important for prediction. The findings of this article encourage researchers as well as practitioners to explore more sophisticated models and use more relevant features.

Towards ultrasonic guided wave fine-grained damage detection on hierarchical multi-label classification network

This study advocates the hierarchical multi-label classification (HMC) network for fine-grained damage detection using ultrasonic guided wave. Existing deep learning methods only focus on one aspect of the damage type or the degree of damage progression, and both are important for the long-term safe operation of the structure. To address this limitation, an ultrasonic guided wave HMC network (GHmcNet) and its lightweight version (L-GHmcNet) are developed. The essential designs of the proposed methods are derived from two motivations: (1) achieving robust fine-grained damage classification; (2) designing a lightweight network. The hierarchical labels (defect type-depth-size) are constructed on guided wave data. Inspired by the work of image classification, residual connections are introduced to transfer and fuse features between parent and children categories. The GHmcNet has three output channels and a weighted cross-entropy loss function is imposed on each channel. The weights of the corresponding loss are automatically adjusted during training. To reduce the parameters, we improve the structure of GHmcNet by applying different numbers of convolutional layers and channels at different levels based on the number of classes in each level, proposing the lightweight model L-GHmcNet. Both numerical and experimental studies are carried out, in which the dataset contains signals of different damage types, depths and sizes. The results show the superiority of the proposed methods in terms of accuracy at three levels compared to state-of-the-art methods. Moreover, the high accuracy classification results of L-GHmcNet demonstrate that the proposed lightweight network is a successful attempt.

First report of PST-producing Microseira wollei from China reveals its novel toxin profile

Microseira wollei, a globally distributed freshwater bloom-forming benthic cyanobacterium, is known for its production of cyanotoxins and taste and odor (T&O). While CYN (Cylindrospermopsin)-producing populations of M. wollei are confined to Australia, PST (Paralytic shellfish toxins)-producing populations have been exclusively documented in North America. In this study, four benthic cyanobacterial strains, isolated from West Lake in China, were identified as M. wollei based on morphological and phylogenetic analyses. Detection of sxtA gene and UPLC-MS/MS analysis conclusively confirmed the PST-producing capability of M. wollei CHAB5998. In the phylogenetic tree of 16S rDNA, M. wollei strains formed a monophyletic group with two subclades. Notably, non-PST-producing Chinese strains clustered with Australian strains in Clade II, while all other strains, including PST-producing ones, clustered in Clade I. Additionally, CHAB5998 contains ten PST variants, of which STX, NEO, GTX2, GTX3, GTX5 and C1 were identified for the first time in M. wollei. Sequence analysis of PST biosynthetic gene cluster (sxt) genes indicated potential base variations, gene rearrangements, insertions, and deletions in the strain CHAB5998. Also, sxt gene has a longer evolutionary history in M. wollei than that in cyanobacteria from Nostocales. Multiple recombination breakpoints detected in sxt genes and the inconsistency in the topology of the phylogenetic trees between sxt and 16S rDNA suggested that multiple horizontal gene transfers (HGT) have occurred. Overall, the present study marks the first documented occurrence of PST-producing M. wollei outside of North America and identifies it as the first toxic freshwater benthic cyanobacterium in China. This revelation implies that benthic cyanobacteria may pose a higher environmental risk in China than previously acknowledged.

Task allocation planning based on hierarchical task network for national economic mobilization

Task allocation planning based on hierarchical task network for national economic mobilization

Metro Flow Prediction With Hierarchical Hypergraph Attention Networks

Metro Flow Prediction With Hierarchical Hypergraph Attention Networks

Adaptive Federated Pruning in Hierarchical Wireless Networks

Adaptive Federated Pruning in Hierarchical Wireless Networks

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.

Mitochondrial DNA evidence reflects high genetic divergence of Amynthas aspergillum (Oligochaeta: Megascolecidae) in southern China.

Amynthas aspergillum (Perrier, 1872), a natural resource used in traditional Chinese medicine (Guang-dilong) with high economic value, is widely distributed in forests and farmland habitats in the hilly areas of southern China. To investigate the extent of genetic differentiation and diversity in A. aspergillum, a population genetic structure study was performed on 157 samples from 75 locations in southern China using the mitochondrial genes COI, COII, 12S rRNA, 16S rRNA, and NDI. The results indicated that A. aspergillum had a high level of genetic diversity, and variation within populations was the main source of the total variation. Six deeply divergent mitochondrial clades (I-VI) were detected using both phylogenetic tree and haplotype network analyses. This finding was supported by the high Kimura two-parameter genetic distance and the pairwise fixation index value obtained based on the COI gene. No significant phylogeographic structures were observed. The widespread geographic distribution of clades II, IV, and VI suggested a recent demographic expansion based on multiple analysis results. These results include a high level of Hd and low π, star-shaped haplotype network structures with a high number of less frequent haplotypes, significantly negative neutrality test values, and a unimodal mismatch distribution pattern. The divergence time estimates and reconstruction of the ancestral area revealed that A. aspergillum originated in Guangxi Province and underwent initial intraspecific diversification in the early Pliocene to generate clade I. Then, it gradually dispersed eastward and rapidly differentiated into clades II-V during the Pleistocene. The Yunnan-Guizhou Plateau and Nanling and Wuyi Mountains might act as geographical barriers for the spread of A. aspergillum to the west and north.