Network Structure Research Articles

Analysis and prediction of sputtering yield using combined hierarchical clustering analysis and artificial neural network algorithms

Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E th, maximum sputtering energy E max and maximum sputtering yield SY max. In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors (R 2 evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R 2 of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.

Controllable synthesis of Pt-rich shell/Mx core [M = Ni, Fe, Co] metal aerogels as efficient electrocatalysts for hydrogen evolution reaction

Water electrolysis for hydrogen production offers a solution to the fossil fuel crisis, relying heavily on electrocatalysts for the hydrogen evolution reaction (HER). Fortunately, metal aerogels (MAs) with three-dimensional network structures reveal great potential as HER electrocatalysts. However, their simple synthesis and widespread application still present significant challenges. Here, PtNix MAs electrocatalysts, by controlling the Ni/Pt feeding ratio and using NaBH4 reduction, are prepared via a simple one-step method. Notably, PtNi4 MAs with Pt-rich shell/Ni core structure exhibit excellent HER property, showing a low overpotential of 16.3 mV at 10 mA cm−2 and a mass activity density of 2.28 A mgPt-1 at 70 mV. The outstanding HER properties of PtNi4 MAs are ascribed to the synergistic catalytic effect of Pt and Ni, the core–shell structure of the sample, and the abundant three-dimensional network porosity. Density functional theory (DFT) demonstrates that PtNi4 MAs’ core–shell structure have the lowest dissociation energy of H2O and ΔGH* on the catalyst surface. Furthermore, the replacement of non-precious metal (Ni/Fe/Co) does not affect the formation of the Pt-rich shell structure. Opposite the substitution of reducing agent species affects the formation of Pt-rich shell structure. This work can offer valuable insights for the future sustainable energy applications of MAs materials.

Effect of three polysaccharides with different charge characteristics on the properties of highland barley starch gel

Highland barley, a nutritious whole grain, faces limited market utilization due to the poor heating stability of its starch. The aim of this study was to investigate the effects of three differently charged ionic polysaccharides—guar gum (GG), xanthan gum (XG), and carboxymethyl chitosan (CMC)—on the gel properties of highland barley starch (HBS). GG and XG notably increased pasting viscosity, viscoelasticity, hardness, and strength of HBS gels. Conversely, CMC resulted in decreased gel properties. All three polysaccharides enhanced OH tensile vibration (3000–3800 cm−1), with GG and XG promoting denser honeycomb network structures and lower spin–spin relaxation time (T2), indicating improved structural integrity. In contrast, low concentrations of CMC led to disorder and loose structure. Hydrogen bonding and electrostatic interactions were the main forces by which polysaccharides influenced the properties of starch gels. This research contributes to enhancing the properties of HBS gel during heating and expanding its commercial applications. It also provides some insights to understand the interaction between different charged polysaccharides and starch.

MD-BiMamba: An aero-engine inter-shaft bearing fault diagnosis method based on Mamba with modal decomposition and bidirectional features fusion strategy

Inter-shaft bearing fault diagnosis can greatly save maintenance costs and guarantee the normal operation of aero-engines. However, existing methods are limited in their ability to handle sensor signals with high dimensional and complex noise. To tackle the above problems, we propose a Mamba network structure with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and bidirectional feature fusion for aero-engine inter-shaft bearing fault diagnosis, called MD-BiMamba. Firstly, we utilize the CEEMDAN technique to decompose the sensor signals of inter-shaft bearings and extract the intrinsic modal function by improving the noise addition method and decomposition strategy. Then, bidirectional Mamba is designed to extract the bidirectional long-range correlation of the sequence to achieve global feature extraction and fast detection of fault types with linear complexity. Moreover, an adaptive attention fusion method is proposed to achieve bidirectional feature fusion. Finally, we use the label smoothing regularization strategy to optimize the cross-entropy loss and enhance the generalization performance. Experimental results on real inter-shaft bearing datasets and noisy datasets show that the proposed method achieves 99.88 % and 95.81 % accuracy in the standard dataset and −10 dB noise environment, respectively, which is superior to other remarkable methods, in addition to achieving lower model complexity.

A Memory-augmented Conditional Neural Process model for traffic prediction

This paper presents the first neural process-based model for traffic prediction, the Memory-augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction involves predicting future traffic patterns based on historical traffic data and the road network structure. This problem remains a challenge due to the dynamic and heterogeneous nature of urban traffic. Existing models often struggle to capture these complexities, particularly in data-limited scenarios. To address these limitations, our model presents a novel framework for uncertainty estimation based on the conditional neural process, and further incorporates a memory network module designed to acquire a representative contextual reference, thereby improving model performance under complex data distributions. By integrating the conditional neural process and the memory network, MemCNP enables the learning of the most representative contexts through iterative updates, enhancing the model’s generalisability. This allows our model to be applicable beyond car traffic, effectively handling diverse real-world traffic scenarios, including urban non-motorised traffic such as cycling, which is essential for advancing more sustainable transportation systems. This is demonstrated by comprehensive experimental results on six benchmark datasets (PeMS04, PeMS07, PeMS08, NYCTaxi, CHIBike, and T-Drive) against existing state-of-the-art traffic prediction models, where MemCNP demonstrates superior performance. Additionally, through ablation and reliability studies, we provide a comprehensive analysis of the model’s effectiveness.

Oriented crystalline structure in melt-drawn ultrahigh-molecular-weight polyethylene induced by entanglement networks

In this study, we focused on the unique structure with oblique periodicity formed by the melt-drawing of metallocene-catalyzed ultrahigh-molecular-weight polyethylene (UHMW-PE) to clarify the relationship between tight entanglements that cannot be disentangled during melt-drawing and the formation of the periodic structure. In situ X-ray measurements during the heating of the melt-drawn UHMW-PE film revealed that the disappearance of the characteristic oblique streaks that appeared tilted from the drawing direction and the orthorhombic-to-hexagonal phase transition occurred simultaneously, suggesting that the change in the tension state of extended-chain crystals due to the orthorhombic-to-hexagonal phase transition and the disappearance of order in the structure resulting in oblique streaks are related behaviors. This indicates the presence of the network structure including oriented amorphous chains in which molecular chains are fixed by tight entanglements in metallocene-catalyzed UHMW-PE melt-drawn film.

Polyoxometalate-derived anti-aggregation MoC nanoparticles for efficient hydrogen evolution in basic and acidic media

As a cost-effective alternative to Pt-based catalysts, molybdenum carbide (MoC) exhibits considerable potential for catalysing hydrogen evolution reaction (HER) in both alkaline water electrolyzers and proton exchange membrane water electrolyzers. However, achieving ampere-level current densities at low overpotentials remains challenging for MoC-based electrocatalysts. In this study, we utilized electrospinning technology followed by a subsequent heat treatment to successfully synthesize monodisperse MoC nanoparticles (approximately 4.3 nm) embedded in carbon nanofibers. The resultant self-supporting one-dimensional molybdenum carbide@nitrogen-doped carbon nanofiber (MoC-A@NCNF), prepared with polyoxometalate anion (POM) and polyvinylpyrrolidone (PVP), exhibits excellent anti-aggregation behavior. Benefiting from its high specific surface area and one-dimensional conductive network structure, the MoC-A@NCNF displays outstanding hydrogen evolution reaction (HER) performance in both 1 M KOH and 0.5 M H2SO4, achieving overpotentials of 491 mV and 568 mV at a current density of 1 A cm−2, respectively. Furthermore, it exhibits exceptional electrochemical stability during prolonged HER testing under both acidic and alkaline conditions.

Fusion innovation: Multi-scale dilated collaborative model of ConvNeXt and MSDA for fault diagnosis

Bearings and gears are critical components in modern industry, and cross-domain diagnosis of these elements is of great significance. However, in practical applications, challenges such as insufficient training data and variability between equipment arise. To address this issue, this study proposes an innovative neural network structure, ConvNeXt, and a Multi-Scale Dilated Attention (MSDA) mechanism to improve the accuracy problem caused by inadequate feature extraction. ConvNeXt improves upon traditional convolutional neural networks by introducing a multi-scale attention mechanism to enhance the model's performance and expressiveness. Through parallel multi-channel convolution operations, ConvNeXt can capture dependencies between different channels and reduce the number of parameters. Meanwhile, the MSDA mechanism allows signals to interact and exchange information at different scales, effectively extracting complex features in one-dimensional signals. Experimental results demonstrate a significant performance improvement in one-dimensional signal processing using ConvNeXt and MSDA, better capturing relationships between global and local features in one-dimensional signals and enhancing model accuracy. The joint application of ConvNeXt and MSDA brings new solutions to one-dimensional signal processing, offering potential opportunities for effective monitoring of critical components in rotating machinery. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with an average accuracy of 94.28%, providing reliable support for bearing fault diagnosis.

Fabrication of novel polysaccharides and glycerol monolaurate based camellia oil composite oleogel: Application in wound healing promotion

In this study, a novel camellia oil composite oleogel (SX@G-CO) was prepared by a combination of direct dispersion and emulsion-templated methods using polysaccharides (sodium alginate and xanthan gum, ratio in 4:6) as oleogelators and glycerol monolaurate (GML, 7 wt%) as gel-enhancer. The comparative experiments revealed that the polysaccharides could effectively enhance the densification of the three-dimensional network structure of the oleogel through hydrogen bonding and electrostatic interactions, and significantly improve its thermal stability, rheological properties (adhesive strength 49,243.6 mPa•s, viscosity recovery rate 94.6 %) and oil binding capacity (80.6 %). The introduction of GML further enriched the crystal diversity of the oleogel and imparted excellent antimicrobial ability (nearly 100 % inhibition effect on E.coli). Furthermore, the in vitro experiments demonstrated that the synergistic effect of polysaccharides and GML significantly enhanced the anti-inflammatory, antioxidant, cell migration and proliferation abilities of SX@G-CO oleogel compared with GML-CO and SX-CO oleogels. In addition, SX@G-CO oleogel has also been demonstrated to effectively promote full-thickness burn healing in mice by reducing bacterial infection and inflammatory response, regulating free radical levels, and promoting neovascularization in vivo, with effects comparable to marketed ointment. SX@G-CO oleogel as a bioactive molecule-polysaccharides composite has potential clinical application in burn wound repair.

Robustness of LEO satellite hypernetworks under different network topologies and attack strategies

The development of low earth orbit (LEO) satellites has become a crucial and essential component with the advancement of digitalization. However, LEO satellites also face various security challenges within their networks, and the failure of certain satellites may trigger cascading failures. To gain a deeper understanding of cascading failure process in LEO satellite networks and enhance its robustness, this letter proposes a novel satellite network model by combining complex network and hypernetwork theories. By altering the network structure and model parameters, the robustness performance of the LEO satellite network under three types of attacks and two different load distribution methods is thoroughly investigated. The results indicate that satellite networks with varying structures are distinct and exhibit different robustness characteristics under specific attack types. Furthermore, the robustness of satellite networks in multiple attack scenarios can be significantly enhanced by implementing improved load allocation strategies. These findings not only provide a theoretical foundation for the design and optimization of satellite networks but also ignite new perspectives and ideas for future research on their robustness.

An artificial neural network visible mathematical model for predicting slug liquid holdup in low to high viscosity multiphase flow for horizontal to vertical pipes

AbstractSlug liquid holdup (SLH) is a critical requirement for accurate pressure drop prediction during multiphase pipe flows and by extension optimal gas lift design and production optimization in wellbores. Existing empirical correlations provide inaccurate predictions because they were developed with regression analysis and data measured for limited ranges of flow conditions. Existing SLH machine learning models provide accurate predictions but are published without any equations making their use by other researchers difficult. The only existing ML model published with actual equations cannot be considered optimum because it was selected by considering artificial neural network (ANN) structures with only one hidden layer. In this study, an ANN-based model for SLH prediction with actual equations is presented. A total of 2699 data points randomly divided into 70%, 15%, and 15% for training, validation, and testing was used in constructing 71 different network structures with 1, 2, and 3 hidden layers respectively. Sensitivity analysis revealed that the optimum network structure has 3 hidden layers with 20, 5, and 15 neurons in the first, second, and third hidden layers, respectively. The optimum network structure was translated into actual equations with the aid of the weights, biases, and activation functions. Trend analysis revealed that this study’s model reproduced the expected effects of inputs on SLH. Test against measured data revealed that this study’s model is in agreement with measured data with coefficient of determinations of 0.9791, 0.9727, 0.9756, and 0.9776 for training, testing, validation, and entire datasets, respectively. Comparative study revealed that this study’s model outperformed existing models with a relative performance factor of 0.002. The present model is presented with visible mathematical equations making its implementation by any user easy and without the need for any ML framework. Unlike existing ANN-based models developed with one hidden layered ANN structures, the present model was developed by considering ANN structures with one, two, and three hidden layers, respectively.

Jellyfish-Inspired Polyurea Ionogel with Mechanical Robustness, Self-Healing, and Fluorescence Enabled by Hyperbranched Cluster Aggregates.

Ionogels are promising for soft iontronics, with their network structure playing a pivotal role in determining their performance and potential applications. However, simultaneously achieving mechanical toughness, low hysteresis, self-healing, and fluorescence using existing network structures is challenging. Drawing inspiration from jellyfish, we propose a novel hierarchical crosslinking network structure design for in situ formation of hyperbranched cluster aggregates (HCA) to fabricate polyurea ionogels to overcome these challenges. Leveraging the disparate reactivity of isocyanate groups, we induce the in situ formation of HCA through competing reactions, enhancing toughness and imparting the clustering-triggered emission of ionogel. This synergy between supramolecular interactions in the network and plasticizing effect in ionic liquid leads to reduced hysteresis of the ionogel. Furthermore, the incorporation of NCO-terminated prepolymer with dynamic oxime-urethane bonds (NPU) enables self-healing and enhances stretchability. Our investigations highlight the significant influence of HCA on ionogel performance, showcasing mechanical robustness including high strength (3.5 MPa), exceptional toughness (5.5 MJ m-3), resistance to puncture, and low hysteresis, self-healing, as well as fluorescence, surpassing conventional dynamic crosslinking approaches. This network design strategy is versatile and can meet the various demands of flexible electronics applications.

Network Analysis of Association Between Problematic Social Network Use and Alexithymia in Freshmen.

Exploring the core and bridge nodes in problematic social network use and alexithymia among freshmen to provide a basis for understanding the relationship and interventions. A total of 4057 first-year students from four universities in Shandong Province were chosen and surveyed with the Problematic Mobile Social Media Use Assessment Questionnaire and the Toronto Alexithymia Scale (TAS). Network analysis was performed using R to estimate the connections between nodes. Centrality and predictability indicators were used to identify key nodes, with accuracy and stability validation techniques applied. Gender and residence differences in the network structure were also examined. In the problematic social network use network, the nodes with the highest expected influence were P16 (excessive swiping) and P14 (lack of control over phone usage). In the problematic social network use-alexithymia network, cognitive failure had the highest strength (strength = 1.155) and centrality. Difficulty identifying feelings (bridgestrength = 0.32), externally oriented thoughts (bridgestrength = 0.24), and cognitive failure (bridgestrength = 0.19) were key bridge nodes. No significant differences were found in the network structure across gender and residence, though the network was tightly connected. Cognitive failure plays a central role in problematic social network use among freshmen. Difficulty identifying feelings, externally oriented thoughts, and cognitive failure are critical in linking problematic social network use with alexithymia.

A Network Structure of Mental Health and Problematic Mobile Phone Use Among Middle School Students.

Numerous studies have shown that the mental health of middle school students is closely related to problematic mobile phone use. The purpose of this study is to investigate the network structure between the dimensions of the Middle School Students Mental Health Scale and the items of the Self-Rating Questionnaire for Adolescent Problematic Mobile Phone Use by using the network analysis, to clarify the core symptoms and bridge symptoms of the network structure, and to provide ideas and methods for intervening in the mental health and problematic mobile phone use of middle school students. A stratified cluster sampling method was used to select 1637 students from four general middle schools in Xiamen in June 2020 for the survey, and the Middle School Students Mental Health Scale (MSSMHS-60) and the Self-Rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU) were used. SPSS28.0 was used for descriptive statistical analysis and R (version 4.2.1) for network analysis. 1. The core symptoms of the network of middle school students' mental health and problematic mobile phone use were "spending more time playing with the phone in order to be satisfied", anxiety, and depression; 2. The bridge symptoms of the network of middle school students' mental health and problematic mobile phone use were academic stress, psychological disequilibrium, and "depression without phone". Reducing the time of smartphone use and relieving anxiety and depression can improve the mental health of middle school students and reduce the incidence of problematic mobile phone use; helping middle school students adjust their study pressure and improving their social support level can reduce the severity of problematic mobile phone use.

The network structure and properties of a unique mining solid waste-based glass-ceramics

To alleviate the environmental pollution caused by the stockpile of mining-based, such as coal-based solid waste, mining solid waste-based CaO-MgO-Al2O3-SiO2 (CMAS) glass-ceramics with a diopside phase as the main crystalline phase was developed by one-step sintering method, and the effect of different solid waste content on the network structure and properties were investigated. The results show that, with the diversification of solid waste content, the crystallization temperature (Tp), the amount of Fe3+ concentration, and diopside crystalline, the main structure of glass Q2 changed also. Accordingly, significant changes have occurred in properties such as the bending strength, Vickers hardness, and density. Optimizing the raw material ratio, crystallization mechanism, a mining solid waste-based glass-ceramic with unique network structure is well developed and the bending strength, Vickers hardness, fracture toughness, and density reaches maximum values of 175.19 MPa, 10.47 GPa, 2.03 MPa⋅m1/2, and 3.00 g/cm3, respectively. The findings contribute significantly to the development of durable and reliable materials for the utilization of mining solid waste resources.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

Elucidation of characteristics of networks where every node has its own lifetime

Growth is regarded as an important mechanism for explaining the structures of real networks. However, when the increase in the number of nodes is suppressed owing to their lifetime, the growth property alone is not sufficient to explain even fundamental network properties, such as the scale-free property. In this paper, we propose a network model that considers the lifetime of nodes and the excess addition of local internal links as a mechanism that supports network structures. By investigating the model network, we aimed to elucidate the network characteristics supported by local interactions between nodes via their common neighbors even when the rates of node addition and deletion were balanced. We found that the stationary state of the number of nodes is characterized by a scale-free property with the power-law exponent γ≃1 and localization of the peaks at l=2 in the distance distributions of neighboring nodes (DDN) as the node degree k increases. The specific behavior of the DDN explains the very slow decrease in the clustering strength C(k) with k compared with the normal behavior C(k)∼k−1 and the accelerated growth of the neighborhood graph of each node. Moreover, we showed that some real networks share local structures similar to those of the model network. These findings suggest that the same mechanism as that of the proposed model plays an essential role in supporting the local structures of some real networks.

Learning accurate neighborhood- and self-information for higher-order relation prediction in Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are commonly employed to model complex real-world scenarios with diverse node and edge types. However, due to constraints in data collection and processing, constructed networks often lack certain relations. Consequently, various methods have emerged, particularly recently, leveraging heterogeneous graph neural networks (HGNNs) to predict missing relations. Nevertheless, these methods primarily focus on pairwise relations between two nodes. Real-world interactions, however, often involve multiple nodes and diverse types, extending beyond simple pairwise relations. For instance, academic collaboration networks may entail interactions among authors, papers, and conferences simultaneously. Despite their prevalence, higher-order relations are often overlooked. While HGNNs are effective at learning network structures, they may suffer from over-smoothing, resulting in similar representations for nodes and their neighbors. The learned inaccurate proximity among nodes impedes the discernment of higher-order relations. Furthermore, observed edges among a target group of nodes can provide valuable evidence for predicting higher-order relations. To address these challenges, we propose a novel model called Accurate Neighborhood- and Self-information Enhanced Heterogeneous Graph Neural Network (ANSE-HGN). Building upon HGNNs to encode network structure and attributes, we introduce a relation-based neighborhood encoder to capture information within multi-hop neighborhoods in heterogeneous higher-order relations. This enables the calculation of accurate proximity among target groups of nodes, thereby enhancing prediction accuracy. Additionally, we leverage self-information from observed higher-order relations as an auxiliary loss to reinforce the learning process. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed method in higher-order relation prediction tasks.

Differential impact of Paenibacillus infection on the microbiota of Varroa destructor and Apis mellifera

The Western honey bee (Apis mellifera) is a vital agricultural pollinator whose populations are threatened by the parasitic mite Varroa destructor and associated pathogens. While the impact of Paenibacillus species on honey bees, particularly Paenibacillus larvae causing American foulbrood, is documented, their effect on the microbiota of Varroa mites remains unclear. This study aimed to investigate the influence of Paenibacillus sp. on the bacterial communities of Varroa mites and adult honey bees. We hypothesized that Paenibacillus sp. would significantly alter the microbiota of Varroa mites but have minimal effect on that of adult honey bees. Utilizing 16S rRNA sequencing data from a previous study, we reanalyzed samples categorized into four groups based on Paenibacillus sp. infection load: highly infected and lowly infected honey bees (A. mellifera) and mites (V. destructor). Infection status was determined by Paenibacillus sp. read counts, with more than three reads indicating high infection. Microbial diversity was assessed using alpha and beta diversity metrics. Co-occurrence networks were constructed to visualize bacterial community assemblies, and network robustness was evaluated through node addition and removal tests. Keystone taxa were identified based on eigenvector centrality and relative abundance. Highly infected Varroa mites exhibited a significant reduction in alpha diversity and a markedly different bacterial community composition compared to lowly infected mites (p < 0.05). Their bacterial co-occurrence networks showed decreased connectivity and robustness, indicating a disruptive effect of Paenibacillus sp. In contrast, adult honey bees displayed no significant differences in alpha diversity or network structure between highly and lowly infected groups (p > 0.05), suggesting a resilient microbiota. Keystone taxa analysis revealed fewer central species in highly infected Varroa mites, potentially impacting network stability. High Paenibacillus sp. infection is associated with significant alterations in the microbiota of Varroa mites, disrupting bacterial communities and potentially affecting mite physiology. The microbiota of adult honey bees appears more robust against Paenibacillus sp. influence. These findings enhance our understanding of the complex interactions within the "honey bee–mite–microorganism" system and may inform future strategies for managing Varroa mite infestations and associated pathogens.

Classification of Crack Coordinates through Wavelet - Analysis and CNN: Analysis of Data Preparation and Network Structure

For the two-dimensional localization of acoustic emission events (AE), artificial intelligence (AI) was applied. AE events were generated by breaking pencil leads on a granite plate measuring with dimensions of 50 cm × 50 cm × 6 cm. A total of 128 measurements were carried out at each of the 81 positions on the top surface of the granite plate. The recorded signals with 16 channels were processed using continuous wavelet transformation (CWT). The resulting RGB images served as input data for a convolutional neural network (CNN). The transformed images of the measured AE signals were converted into a four-dimensional input by considering them as deeper input, allowing for improved data capture. The result was analysed using a binary classifier according to the one-versus-all method. However, due to limited computational capacity, processing the four-dimensional data posed a challenge, resulting in significant programming effort. The results of this investigation provide insights into the potential of using AI for the localization of AE events.