Hierarchical Method Research Articles

Reversible data hiding in encrypted images based on secret sharing and hierarchical embedding

With the increasing emphasis on information security, reversible data hiding technology in encrypted images (RDH-EI) has been widely applied in cloud storage privacy protection in recent years. However, once the data hider is leaked or compromised, the current algorithm of RDH-EI will encounter the challenge of effectiveness and security. In order to tackle this issue, this paper proposes an RDH-EI scheme based on secret sharing and hierarchical embedding. First, an image secret sharing based on (4, 6)-threshold algorithm is proposed. This algorithm aims to combine image encryption and sharing processes to generate six different encrypted images with vertical correlation. Sending them in encrypted form to cloud storage is beneficial for protecting the semantics of cover image. Then, the hierarchical embedding method is suggested to attain a high embedding payload. In this approach, by analyzing the vertical correlation of adjacent pixels, we have designed three mapping rules based on multi-MSB prediction. Among them, case 1 reserves the highest embedding capacity, case 2 follows, and case 3 has no embedding space. Based on this, the paper can adaptively embed secret data. Finally, fully authorized users can extract secret data and restore the original image by decrypting four or more marked encrypted images. Experimental results indicate that our approach surpass some existing RDH-EI approaches in payload, which average payloads in the best case on the BOSSbase and BOWS-2 datasets reach 4.466 bpp and 4.152 bpp, respectively. Additionally, the proposed RDH-EI method transfers and stores image data in a distributed manner, which helps prevent excessive concentration of power by data hiders and misuse of data. Compared to traditional end-to-end RDH-EI schemes, the proposed approach offers higher security.

Synthesis of desulfurization solvent networks based on process characteristics: New insights of desulfurization units and a novel synthesis method

Synthesis of desulfurization solvent network (DSN) can reduce the solvent circulation and the energy consumption for solvent regeneration. Based on the desulfurization process characteristics, this work proposes a novel graphic to explore the operating limitations of a desulfurization unit. A feasible operating window of the desulfurization unit is presented with three boundary lines to constrain the solvent reuse. Different cases for the solvent reuse between units are analyzed in details, and two rules for rich solvent reuse are proposed and proven. A novel hierarchical method is developed for the DSN synthesis. A refinery case is investigated using the proposed method. Four DSN designs are obtained, and the fresh solvent consumption can be reduced by 13.7 %. The accuracy of the four designs is verified by rigorous simulation, showing a maximum relative error of 1.22 %. The proposed method can show clear insights in DSN synthesis and give reliable and concise designs.

Intelligent Identification and Quantitative Evaluation Method of Fluid Components from T2-T1 2D NMR Logging

Summary T2-T1 2D nuclear magnetic resonance (NMR) logging plays an important role in fluid identification and reservoir evaluation. When the signal/noise ratio (SNR) is low, the response of the fluid component overlaps on the NMR 2D spectrum, and the fluid characteristic map obtained in the laboratory could not be directly applied to 2D NMR logging data. In this work, we proposed a T2-T1 2D NMR fluid identification and quantitative evaluation method based on blind source separation (BSS) and hierarchical clustering. This method fixes the number of fluid components of BSS as eight and utilizes a hierarchical clustering method to further process the 2D spectra obtained from BSS, so that each spectrum contains only one fluid component response. Subsequently, the fluid types are determined according to the response characteristics, and the fluid volumes are quantitatively calculated. Based on the synthetic data of T2-T1 2D NMR logging, this method was used to calculate the volumes of the fluid components, and the accuracy of the method was verified by comparing with the model and other four methods. This method was applied to 2D NMR logging data from Well Z and Well G and verified by oil test results. The results show that the relative error between the fluid volumes calculated by the proposed method and the model is smaller than other methods. The data processing results of Well Z and Well G are consistent with those of the oil test. Both synthetic data and actual logging data show that this method can provide the accurate fluid identification and quantitative evaluation results of T2-T1 2D NMR logging data.

Mars Exploration: Research on Goal-Driven Hierarchical DQN Autonomous Scene Exploration Algorithm

In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration.

Configuration design methodof hybrid electric agricultural machinery system based on layered two-dimensional matrix

The working environment and objects of agricultural machinery are different from those of automobiles, andagricultural machinery is greatly affected by the working environment and working conditions, and the power output systemismore complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, whichtogetherconstitute the output system of agricultural machinery. Agricultural machinery conditions can be dividedintoroadtransportation working conditions and field operation working conditions. The working conditions of agricultural machinerycan be divided into different load conditions according to the different traction tools and whether the hydraulic andPTOwork,such as ploughing, rotary tillage, fertilization, and transportation. Therefore, developing hybrid electric agricultural machinerysystems that are suitable for various complex working conditions holds great theoretical significance and practical value. Giventhe complex working conditions of agricultural machinery systems in agricultural work and the intricate challengesindesigning hybrid agricultural machinery systems. In this paper, the two-dimensional matrix is used to represent thephysicalstructure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrixmethod for the generation and screening of hybrid electric agricultural machinery systems with multi-power output powerisproposed. The components of agricultural machinery are divided into an input layer and an output layer, and these componentsare coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is usedtogenerateand screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, the complexityoftheconfiguration generation is reduced, and the constraints are applied to the basic screening of the generated configurations. Therationality of the configurations obtained after generation and screening is verified by Simulink simulation. The resultsshowthat the configuration screened by this method can meet the performance requirements of agricultural machinery.

Which pollutants and sources should be prioritized for control in multi-pollutants complex contaminated areas?

Risk assessment and source identification of multi-pollutants are essential for accurate control of soil contamination. However, complexity in pollutant properties and diversity in source types raise challenges to the target. Therefore, this study constructed a hierarchical ecological risk quantification method combined with risk ranking, risk of single pollutant using potential affected fraction (PAF), and joint risk of multi-pollutants employing msPAF. Taking regional contamination in South China as a case, the risk ranking was determined, while single and joint effects showed msPAF reaching 79.4 %, with risk as heavy metals (HMs) > per- and polyfluoroalkyl substances (PFASs) > polycyclic aromatic hydrocarbons (PAHs). Meanwhile, an integrated source apportionment method was established from three layers by principal component analysis to classify source types, multiple linear regression of distance to identify key sources, and positive matrix factorization to track omitted sources. Consequently, key sources were captured, with 80.8 %−93.2 % contribution of farmland and electroplating to three main HMs, 52.2 %−69.4 % contribution of roads to three main PAHs, and 71.1 %−73.2 % contribution of electroplating to two main PFASs. Further, omitted sources were tracked with contribution of 31.2 %−84.1 % to eight pollutants. The established methods can identify control targets, including high-risk pollutants and their key sources.

Mapping phenoregions and phytoplankton seasonality in Northeast Pacific marine coastal ecosystems via a satellite-based approach

Phytoplankton phenology describes yearly algal growth cycles and characterises the timing, duration, and magnitude of bloom occurrences. This study used satellite chlorophyll-a data from 1998 to 2020 and the Hierarchical Agglomerative Clustering method to define phenoregions based on phytoplankton phenology spatial patterns over the British Columbia and Southeast Alaska coastal oceans. The defined phenoregions were used to simplify the spatial complexity of the heterogenous study region and thus better describe phytoplankton seasonality across the target area. The cluster analysis allowed the delineation of four coherent regions: two coastal regions and northern and southern shelf/offshore regions. Results showed that each phenoregion had distinguishable phytoplankton phenological characteristics, likely due to different physical forcings acting in these areas. Moreover, the interannual variability of the spring bloom initiation was evaluated considering interactions between sea surface temperature (SST) anomalies and the El Niño Southern Oscillation Index (ENSO). Early spring blooms were associated with positive SST anomalies and El Niño conditions; conversely, average or late spring blooms occurred in years with negative SST anomalies and La Niña conditions, with the strongest relationship occurring in the southern shelf/offshore phenoregion. This study provided new insights into the regionalisation of the British Columbia and Southeast Alaska coastal oceans based on phytoplankton phenology patterns. Given the critical role of phytoplankton as the base of the marine food web, such phenoregions have implications for regional zooplankton biomass and fish production. The link between phytoplankton phenology and climate drivers points to the importance of environmental change in phytoplankton bloom dynamics. Further research into the connection between phytoplankton bloom indices and zooplankton community structure and production would be an important step towards using these indices for ecosystem monitoring and fisheries management.

The impact of board capital on open innovation with the moderating effect of executive equity incentives

As a predominant global innovation paradigm, open innovation constitutes a pivotal means of enhancing firms’ innovation performance. Scholars and practitioners are increasingly concerned about strategies to strengthen open innovation, but previous research has overlooked corporate governance factors. This study empirically investigates the impact of board capital on corporate open innovation using data from Chinese listed firms (1999–2009) within the resource dependence and upper echelons theory framework, employing the fixed effects model for panel data and the hierarchical regression method. Findings reveal that the breadth and depth of board capital positively affect the breadth of open innovation, though not significantly the depth. Additionally, executive equity incentives enhance the relationship between board capital and open innovation breadth. Robustness checks support these findings. Notably, board capital’s impact is more pronounced in large enterprises and high-technology industries. This study offers practical insights for firms to prioritize board capital considerations in fostering open innovation initiatives.

Fuzzy Membership Partition Based Effective Hierarchical Agglomerative Flat Clustering Method for High Dimensional Data

Fuzzy Membership Partition Based Effective Hierarchical Agglomerative Flat Clustering Method for High Dimensional Data

Research on fire hazard assessment of metro baggage based on improved hierarchical analysis

Underground station passenger flow is large, the number of parcels carried by passengers is large and varied, and the parcels carried have an impact on the fire hazard and evacuation of the station. In order to determine the weights of the passenger luggage risk and environmental factor index system in the fire risk evaluation of underground stations in a more realistic way, an optimized and improved hierarchical analysis method for determining the judgement matrix is proposed, which improves the traditional nine-scaled method and adopts the three-scaled method for the four major categories of luggage, namely, handbags, rucksacks, portable power tools and trolley cases. The advantage of this method is that there is no need for consistency judgement in determining packages with a wide range of types and uncertain contents, thus simplifying the calculation. Meanwhile, the reasonableness and reliability of the method is verified by combining it with an actual metro station fire risk assessment system.

Automatic hierarchical background virtualization method for monocular vision image based on depth information extraction

Automatic hierarchical background virtualization method for monocular vision image based on depth information extraction

Classification of damage modes in scaled open-hole composite laminates under combined tension-shear loading using acoustic emission technique

The damage initiation and evolution of carbon fiber-reinforced polymer (CFRP) composite under multi-axial loading is of fundamental interest. This study investigates intra- and interlaminar damage modes induced in open-hole quasi-isotropic (QI) carbon/epoxy composite laminate under combined tension-shear loading using the acoustic emission (AE) technique. The effect of layer thickness is studied using three types of laminates with stacking sequences PL-1: [−452/452/902/02]s, PL-2: [−453/453/903/03]s (ply blocked), and SL-1: [−45/45/90/0]2s (a sub-laminate blocked or distributed). Clustering of acquired AE signals is carried out using hierarchical methods. Damage modes evolution for the open hole QI CFRP is captured using the clustered AE signal data. It is observed that the thickness scaling significantly affects the open-hole strength and the critical failure mechanisms. Dominant damage mechanisms are identified based on AE cumulative energy. Ply-level blocked (PL-1 and PL-2) laminates exhibit lower strength than the SL-1 blocked laminate. The findings from this experimental investigation indicate that the AE technique in conjunction with the clustering approach is a prospective tool for the structural health monitoring of composite structures under combined loading.

PENGGUNAAN K-MEANS DAN HIERARCHICAL CLUSTERING SINGLE LINKAGE DALAM PENGELOMPOKKAN STOK OBAT

Adequate and efficient availability of medicines is necessary to ensure patients receive optimal care. However, inefficient drug stock management can result in various problems, such as waste of resources, lack of necessary drugs, or even excessive stock. This study aims to improve the efficiency of the drug stock management process by using KMeans Clustering and Hierarchical Clustering methods on drug stock data. The data used includes information on initial stock, purchase, incoming distribution, service, outgoing distribution, outgoing adjustment, and final stock. Clustering analysis was performed to identify patterns in the drug stock data, which was then validated using Silhouette Score. The results showed that Hierarchical Clustering was able to achieve a Silhouette Score of 0.976, while KMeans achieved a Silhouette Score of 0.954

Voltage Hierarchical Control Strategy for Distribution Networks Based on Regional Autonomy and Photovoltaic-Storage Coordination

High-penetration photovoltaic (PV) integration into a distribution network can cause serious voltage overruns. This study proposes a voltage hierarchical control method based on active and reactive power coordination to enhance the regional voltage autonomy of an active distribution network and improve the sustainability of new energy consumption. First, considering the reactive power margin and spatiotemporal characteristics of distributed photovoltaics, a reactive voltage modularity function is proposed to divide a distribution grid into voltage regions. Voltage region types and their weak points are then defined, and the voltage characteristics and governance needs of different regions are obtained through photovoltaic voltage regulation. Subsequently, a dual-layer optimal configuration model of energy storage that accounts for regional voltage regulation is established. The upper-layer model focuses on planned configurations to minimize the annual comprehensive operating cost of the energy storage system (ESS), while the lower-layer model focuses on optimal dispatch to achieve the best regional voltage quality. KKT conditions and the Big-M method are employed to convert the dual-layer model into a single-layer linear model for optimization and solution. Finally, an IEEE 33-node system with high-penetration photovoltaics is modeled using MATLAB (2022a). A comparative analysis of four scenarios shows that the comprehensive cost of an ESS decreased by 8.49%, total revenue increased by 19.36%, and the overall voltage deviation in the distribution network was reduced to 0.217%.

A novel hierarchical model calibration method for deep water reservoirs under depletion and aquifer influence

A novel hierarchical model calibration method for deep water reservoirs under depletion and aquifer influence

An ecological population study of lampreys in the Lotka-Volterra tertiary trophic level based on sex differences

This study examines the adaptive changes in sex ratio on animal populations and its effects on ecosystem stability and interactions. The effects of sex ratio dynamics on the ecosystem were analyzed through mathematical modeling using lampreys as the study population. In this study, a two-stage tertiary trophic Lotka-Volterra mathematical model of ecological dynamics was developed, taking into account food preferences, ecological roles, and sex interactions of lampreys. The simulation results showed that a rapid adjustment in sex ratio led to a reduction in environmental loads, which triggered an early rebound increase in lampreys and predator populations. To assess the ability of lampreys populations to adjust sex ratios, this study used a combination of fuzzy integrated evaluation and hierarchical analysis methods. The results of the study revealed the importance of sex-adaptive traits for population development, including increased resource utilization efficiency and population fitness, as well as decreased reproductive success and reduced genetic diversity. Based on the RRT (resistance, resilience and temporal stability) method, this study constructed an ecosystem stability assessment model and investigated the effects of changes in the sex ratio of lampreys on ecosystem stability. The experimental results showed that ecosystem stability was highest when the sex ratio was 0.6 and lowest when the sex ratio was 0.2. Variation in sex ratio may affect the behavior and abundance of lampreys, thereby directly influencing their interactions with other species. Variability in sex ratio provides more resources for predators and parasites of lampreys, promotes their growth, and positively affects ecosystem balance. Simulation results showed that the model was highly stable under different interactions. In summary, this study reveals the important effects of sex ratio on ecosystem stability and interactions, and provides important insights into the understanding of adaptive evolutionary traits of species, ecosystem stability and ecological interactions.

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets. BLEND learns the most suitable references for each bulk sample by exploring the convex hulls of references and employs a "bag-of-words" representation for bulk count data for deconvolution. To speed up the computation, we provide an efficient EM algorithm for parameter estimation. Notably, BLEND requires no data transformation, normalization, cell type marker gene selection, or reference quality evaluation. Benchmarking studies on both simulated and real human brain data highlight BLEND's superior performance in various scenarios. The analysis of Alzheimer's disease data illustrates BLEND's application in real data and reference resource integration.

Long-term natural history of ellipsoid zone width in USH2A-retinopathy

AimsTo investigate the long-term natural history of ellipsoid zone (EZ) width in USH2A-retinopathy.MethodsEZ width measurements from optical coherence tomography were retrospectively obtained from 110 eyes of 55 participants with molecularly...

Environmental Sustainability in South America

The purpose of this article is to determine the similarities in the context of environmental practices and sustainability of twelve South American countries and compares the environmental sustainability performance with six principal components. A multivariate analysis as a hierarchical method was carried out with 12 countries of South American with seventeen sustainable environmental indicators, secondary official data sources were consulted, namely the databases of ECLAC (Economic Commission for Latin America and the Caribbean), applying eigenvector and eigenvalues from the correlation matrix and Ward's method with squared Euclidean distances. The results suggest that the initial jumps in terms of distance are small. accordingly, the twelve countries analysed in study are grouped into five clusters. Deepening then in the perspective of characteristics of each cluster, CL1: Colombia, Venezuela, Suriname, Argentina and Bolivia; CL2: Guyana, Paraguay, Uruguay and Peru; CL3: Brazil; CL4: Ecuador; CL5: Chile. The research highlights significant differences among these South American countries, clustering those presenting similar patterns of behaviour and identifying the best performers. We argue that biodiversity must be protected and encouraged, and awareness raised of the importance of environmental sustainability and support the impact of climate change. Argentina, Uruguay and Chile face severe scarcity water issues and temperatures have risen in every country, but especially in Brazil, Colombia, Ecuador, Paraguay, Suriname, Venezuela.

Effectiveness and Efficiency: Label-Aware Hierarchical Subgraph Learning for Protein–Protein Interaction

The study of protein–protein interactions (PPIs) holds immense significance in understanding various biological activities, as well as in drug discovery and disease diagnosis. Existing deep learning methods for PPI prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they often experience a decline in performance in the real world. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes’ degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. The huge data growth of PPI leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose a label-aware hierarchical subgraph learning method (laruGL-PPI) that can effectively infer PPIs while being interpretable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments conducted across various scales of PPI datasets have conclusively demonstrated that the laruGL-PPI method surpasses the most advanced PPI prediction techniques currently available, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.