Hierarchical Clustering Model Research Articles

Multiscale Urban Functional Zone Recognition Based on Landmark Semantic Constraints

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis.

Aviation Safety Risk Analysis Based on Bayesian Network Modeling

The occurrence of serious flight accidents not only brings huge economic losses to airlines but also poses a great threat to passengers' lives. The occurrence of serious flight accidents will not only bring great economic losses to airlines but also cause great threats to the lives of passengers. Therefore, in this paper, based on the flight records of different flight crews, flight routes, airports, and specific flight conditions, aircraft safety risk analysis is conducted to calculate and evaluate the risk propensity. Firstly, the data are preprocessed, and then the QAR data are clustered to extract the key data items affecting the safe flight of the aircraft; then the data are transformed to the attached data, and a hierarchical clustering model is established based on the K-means clustering algorithm, which classifies the factors affecting the safety quality of the aircraft flight into the three main aspects of environmental factors, crew maneuvering, and aircraft state, and the key parameters are weighted by the Random Forest algorithm. weights of the key parameters. Then, based on the 3 criterion, the outliers exceeding 3 were selected as the dependent variables leading to the occurrence of deviation; based on the three-layer model, a Bayesian neural network model based on the three-layer model, and then determine the Bayesian nodes; finally, calculate the Bayesian node probability, and then provide a reasonable quantitative description of the flight maneuver.

Mode-I analysis of glass fiber-reinforced polymer laminates toughened by interleaved polyamide 66 nanofibers and chopped carbon using acoustic emission

The fracture toughness of epoxy resins is weak, so enhancing its properties is essential. In this research it was found that interleaving chopped carbon fibers and polyamide 66 nano mats between fiberglass reinforced plastic laminates with 110 µm thickness, increased fracture toughness in Mode I by 35 and 112%, respectively. Acoustic emission technique showed that damage mechanisms begin at further displacements of load point in modified samples. A hierarchical clustering model was used to classify various failure types. It was observed that matrix cracking, fiber breakage, and matrix cracking were the major damage mechanisms for Reference, chopped carbon, and nanomodified samples, respectively. The cumulative energy released by nanomodified samples was 83% lower than that of Reference and chopped carbon samples. A scanning electron microscope was used to validate different types of damage mechanisms.

Prediction of groundwater contamination in an open landfill area using a novel hybrid clustering-based ai model

Groundwater is a vital resource that provides drinking water to over half of the world’s population. However, groundwater contamination has become a serious issue due to human activities such as industrialization, agriculture, and improper waste disposal. The impacts of groundwater contamination can be severe, including health risks, environmental damage, and economic losses. A list of unknown groundwater contamination sources has been developed for the Wang-Tien landfill using a groundwater modeling system (GMS). Further, AI-based models have been developed which accurately predict the contamination from the sources at this site. A serious complication with most previous studies using artificial neural networks (ANN) for contamination source identification has been the large size of the neural networks. We have designed the ANN models which use three different ways of presenting inputs that are categorized by hierarchical K-means clustering. Such an implementation reduces the overall complexity of the model along with high accuracy. The predictive capability of developed models was assessed using performance indices and compared with the ANN models. The results show that the hybrid model of hierarchical K-means clustering and ANN model (HCA-ANN) is a highly accurate model for identifying pollution sources in contaminated water.

Monogenetic volcanoes as windows into transcrustal mush: A case study of Slamet and Loyang volcanoes, Central Java

Monogenetic volcanic fields (MVFs) are commonly associated with long-lived polygenetic composite volcanoes in many tectonic settings. The association between polygenetic and monogenetic volcanoes has raised questions as to the source of melts feeding monogenetic volcanoes – specifically, whether they are derived from the transcrustal mush underlying a nearby polygenetic volcano, or from a batch of parental magma derived from a different mantle source. To address this question, we studied a well-constrained suite of scoria samples from Gunung Slamet in Central Java, Indonesia, and its most distal monogenetic scoria cone, Gunung Loyang. Slamet is composed of calc-alkaline basalts, and has an extensive MVF on its eastern flank, similar to other Indonesian volcanoes. We analyzed the textural and chemical features of the olivine cargo in both volcanoes, including olivine- and clinopyroxene-hosted melt inclusions. These melt inclusions are the first such analyzed for volatile element contents (H2O, CO2) in the Slamet-Loyang system. We applied hierarchical clustering and mass balance models to constrain the features of the olivine populations in both systems. The olivines in both volcanoes cluster in five distinct textural groups, defined by the presence or lack of zoning and resorption, and according to the type and degree of zoning they exhibit. Olivines show a range of moderate to evolved Fo%, and they also show significant variation in nickel contents (50–1600 ppm). By estimating saturation pressures of entrapped melt inclusions, we observe that Loyang olivines are drawn from a similar petrological system as Slamet, but from deeper mid-crustal levels (12 km), and more mafic source. Our work suggests that monogenetic volcanoes like Loyang can be used as “petrological windows” into the deeper crustal levels of mush reservoirs at composite stratovolcanoes and their associated MVFs.

Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes†

Comprehensive SummaryThe electrical tunneling sensors have excellent potential in the next generation of single‐molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities. Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information. Despite the extraordinarily large amount of data that has been gathered, it is still difficult to correlate signal transformations with molecular processes, which creates great obstacles for signal analysis. Machine learning is an effective tool for data analysis that is currently gaining more significance. It has demonstrated promising results when used to analyze data from single‐molecule electrical measurements. In order to extract meaningful information from raw measurement data, we have combined intelligent machine learning with tunneling electrical signals. For the purpose of analyzing tunneling electrical signals, we investigated the clustering approach, which is a classic algorithm in machine learning. A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering. Additionally, customized statistical algorithms were designed. It has been proven to efficiently gather molecular information and enhance the effectiveness of data analysis.

Crop Information Retrieval Framework Based on LDW-Ontology and SNM-BERT Techniques

Currently, on the Internet, the information about agriculture is augmenting extremely; thus, searching for precise, relevant data of various details is highly complicated. To deal with particular difficulties like lower relevancy rate, false detection of retrieval resources, poor similarity rate, unstructured data format, multivariate data, irrelevant spelling, and higher computation time, an intelligent Information Retrieval (IR) system is required. An IR Framework centered on Levenshtein Distance Weight-centric Ontology (LDW-Ontology) and Sutskever Nesterov Momentum-centred Bidirectional Encoder Representation from Transformer (SNM-BERT) methodologies is presented here to overcome the complications as mentioned earlier. Firstly, the data is pre-processed, transmuting the unstructured data into a structured format, thus mitigating the error probabilities. Then, the LDW-Crop Ontology construction is done regarding the structured data. In the methodology presented, significance, frequency,and the suggestion of word in mind are considered to build Crop ontology. In the MongoDB database, the data being constructed are amassed. Then, by utilizing SNM-BERT, the data is trained for IR regarding clustered input produced by Inter Quartile Pruning Range-centred Hierarchical Divisive Clustering (IQPR-HDC) model. The LDW is computed for the provided user query; subsequently, the similarity evaluation outcomes are obtained from the database. The experiential evaluation displays that when analogized with the prevailing methodologies, a better accuracy of 94 % for simple queries and 92% for complex queries is achieved. Along with retrieval rate with lower computation time is achieved by the proposed methodology.

Explainable semi-supervised AI for green performance evaluation of airport buildings

Despite the global sustainability trend, airport buildings have received limited attention regarding their environmental impact within the built environment and aviation sectors. With less than 1% of green building certifications worldwide, there is a lack of essential green performance assessment tools, including frameworks, primary datasets, and models that require less human supervision. Previous research has addressed these issues by proposing frameworks, primary datasets, and a supervised method that leverages Classification and Regression Trees. However, these methods still rely on human effort to fix credit scores and assign category weights, necessitating significant supervision from human experts. This study presents a green performance evaluation model for airport buildings using an explainable semi-supervised AI approach to reduce human dependency during the inference of green scores. Principal Component Analysis and Hierarchical Clustering algorithms (agglomerative and divisive) are employed to construct the models. The dataset's dimensionality is reduced based on metrics such as scatter coefficient, Psi Index, variation, and permutation. The cluster numbers are determined using elbow point and silhouette coefficient values, resulting in cluster initializations between 6 and 12. Performance measures, including the rand index, mean absolute error, precision, recall, F1-measure, and accuracy, are evaluated for the models. The AHC and DHC models exhibit a maximum accuracy of 74%, albeit with different cluster numbers. Consequently, models are developed with suitable cluster numbers to derive green rules with improved predictive accuracy. Each agglomerative and divisive hierarchical clustering model yields 12 practical green rules that can assist airport operators and facility managers in enhancing the green performance of their airport buildings. These explainable green rules serve as guidelines to improve airport facilities. Additionally, a web-based green rating tool is developed to demonstrate the proof-of-concept, utilizing the green rules extracted from the agglomerative model.

Cultivation of Melilotus officinalis as a source of bioactive compounds in association with soil recovery practices.

Melilotus officinalis is a Leguminosae with relevant applications in medicine and soil recovery. This study reports the application of Melilotus officinalis plants in soil recovery and as a source of bioactive compounds. Plants were cultivated in semiarid soil under four different fertilizer treatments, urban waste compost at 10 t/ha and 20 t/ha, inorganic fertilizer and a control (no fertilizer). Agronomic properties of soil (pH, EC, soil respiration, C content, macro- and microelements) were analyzed before and after treatment. Also, germination, biomass, element contents, and physiological response were evaluated. Metabolite composition of plants was analyzed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Results showed a significant enhancement of the soil microbial activity in planted soils amended with compost, though there were no other clear effects on the soil physicochemical and chemical characteristics during the short experimental period. An improvement in M. officinalis germination and growth was observed in soils with compost amendment. Metabolite composition of plants was analyzed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Principal Component and Agglomerative Hierarchical Clustering models suggest that there is a clear separation of the metabolome of four groups of plants grown under different soil treatments. The five most important discriminative metabolites (annotated) were oleamide, palmitic acid, stearic acid, 3-hydroxy-cis-5-octenoylcarnitine, and 6-hydroxynon-7- enoylcarnitine. This study provides information on how the metabolome of Melilotus might be altered by fertilizer application in poor soil regions. These metabolome changes might have repercussions for the application of this plant in medicine and pharmacology. The results support the profitability of Melilotus officinalis cultivation for bioactive compounds production in association with soil recovery practices.

Customer Segmentation using Machine Learning Techniques

The objective of this customer segmentation endeavor is to utilize machine learning methodologies, Python programming, and the Streamlit framework in order to deliver individualized suggestions pertaining to savings plans, loans, and strategies for wealth management. Through the division of customers according to their demographic data, purchase patterns, and online navigation tendencies, this initiative empowers financial establishments to gain deeper insights into their customer demographics and customize their services to cater to specific requirements.
 The project involves several stages, including data collection, data preprocessing, feature selection, model training, and evaluation. The acquired customer data is refined to resolve any inconsistencies or missing information. Important characteristics are identified, and an exploratory analysis of the data is conducted to uncover hidden patterns and relationships. We then select an appropriate machine learning algorithm, such as K-means clustering, hierarchical clustering, or Gaussian Mixture Models, to categorize the customer base into distinct clusters.
 After the completion of model training and assessment, a Streamlit-based web application is constructed, offering an interactive platform for users. This application permits users to input their particulars and obtain tailored suggestions grounded in their designated segment. The suggestions encompass fitting savings schemes, loan alternatives, and wealth management approaches that harmonize with their financial objectives and risk appetite.
 The deployed application facilitates a seamless user experience, providing real-time recommendations and insights. Continuous improvement of the model and application is encouraged through user feedback, allowing for refinement and better customization of recommendations over time.

Autoantibody status according to multiparametric assay accurately estimates connective tissue disease classification and identifies clinically relevant disease clusters

ObjectiveAssessment of circulating autoantibodies represents one of the earliest diagnostic procedures in patients with suspected connective tissue disease (CTD), providing important information for disease diagnosis, identification and prediction of potential...

Semi-automated Hierarchical Clustering Model for 4D-STEM Datasets.

Semi-automated Hierarchical Clustering Model for 4D-STEM Datasets.

Analysis of glass artifacts based on hierarchical clustering and regression models

Glass is a valuable evidence of the early friendly trade between China and the Western countries. In this study, the rationality and sensitivity of the developed model were first evaluated by analyzing the definitive and quantitative data of each type of glass artifacts and establishing a model to classify the glass artifact categories into subcategories, as well as categorizing unknown types of glass. Considering the discrete and discontinuous nature of the data, the chi-square test was chosen to investigate the correlation between glass type, decoration and color and surface weathering. Secondly, descriptive statistics of high potassium and lead-barium glass artifacts before and after weathering were conducted to obtain statistical patterns about the chemical composition content, and then the chemical composition content of glass artifacts before weathering was predicted based on the mean rate of change of the chemical composition content of high potassium and lead-barium glass before and after weathering, respectively. Finally, this study further explored the relationship between glass artifacts and the content of each chemical composition using Spearman's correlation coefficient to derive the basis for the classification of glass types, based on the hierarchical clustering method to classify the subclasses of high potassium glass and lead-barium glass with and without weathering, respectively, and used the elbow rule to derive the most appropriate number of subclasses for the above four types of glass, which endowed the model with rationality.

Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data

BackgroundBarth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices.ResultsData from a randomized, double-blind, placebo-controlled crossover trial of 12 patients with BTHS were used, including physiological time series data measured using a wearable device (heart rate, respiratory rate, activity, and posture) and functional scores. The latter included the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength by handheld dynamometry, 5 times sit-and-stand test (5XSST), and monolysocardiolipin to cardiolipin ratio (MLCL:CL). Groups were created through median split of the functional scores into “highest score” and “lowest score”, and “best response to elamipretide” and “worst response to elamipretide”. Agglomerative hierarchical clustering (AHC) models were implemented to assess whether physiological data could classify patients according to functional status and distinguish non-responders from responders to elamipretide. AHC models clustered patients according to their functional status with accuracies of 60–93%, with the greatest accuracies for 6MWT (93%), PROMIS (87%), and SWAY balance score (80%). Another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%).ConclusionsIn this proof-of-concept study, we demonstrated that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment among patients with BTHS.

Simulation monitoring of tetracyclines in wastewater based on fluorescence image processing and machine learning classifier

Antibiotics, as an emerging pollutant, have attracted extensive attention due to their trace residues and toxic side effects in the environment. Here, we constructed a new detection mechanism for tetracyclines in sewage by online trace sensing through machine learning (ML) algorithm and image fingerprint extraction method. The image fingerprint information was obtained from the blue to red specific fluorescence response generated by the interaction between the luminescent metal-organic framework (MOF) with tetracyclines, which greatly improved the ability against background interference. Fluorescence images of samples with different concentrations were collected, and the RGB mean value of sample images was extracted through the program as "fingerprint information". Then k-means clustering model (KCM) and hierarchical clustering model (HCM) were constructed to analyze the differences in fingerprint information of samples with different concentrations by clustering analysis method in machine learning. The clustering results showed good clustering characteristics of samples with similar concentrations, which provided reliable support for rapid detection while avoiding the use of expensive large instruments and time-consuming analysis methods.

A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks

The evolution of communities in dynamic (time-varying) network data is a prominent topic of interest. A popular approach to understanding these dynamic networks is to embed the dyadic relations into a latent metric space. While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure. This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures. Our model extends existing latent space approaches by explicitly modeling the additions, deletions, splits, and mergers of groups with a hierarchical Dirichlet process hidden Markov model. Our proposed approach, the hierarchical Dirichlet process latent position cluster model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods. We provide a Markov chain Monte Carlo estimation algorithm and demonstrate its ability to detect evolving community structure in a network of military alliances during the Cold War and a narrative network constructed from the Game of Thrones television series.

Income Expectations in Sustainability of Subjective Perception of Households’ Wellbeing

The knowledge of the sources of the sustainability of the subjective perception of households’ material wellbeing is essential in designing a country’s effective socio-economic policy to increase citizens’ satisfaction. The empirical goal of the study is to check the effectiveness and efficiency of statistical and econometric tools. The cognitive task of the analysis is describing material wellbeing and identifying the sources of satisfaction with life. The methods applied to measure and model emotions of interest include correspondence analysis, hierarchical clustering and panel data models (within estimator variants). The main findings are that Polish households declare a sustainable, increasingly high subjective perception of wellbeing. The author identified possible sources of wellbeing and self-perception sustainability. The main factors constituting the subjective perception of wellbeing are dwelling situation and the possession of durables, along with the absence of drastic differences in the material condition of families.

Single and Joined Behaviour of Circulating Biomarkers and Metabolic Parameters in High-Fit and Low-Fit Healthy Females

Biomarkers are important in the assessment of health and disease, but are poorly studied in still healthy individuals with a (potential) different risk for metabolic disease. This study investigated, first, how single biomarkers and metabolic parameters, functional biomarker and metabolic parameter categories, and total biomarker and metabolic parameter profiles behave in young healthy female adults of different aerobic fitness and, second, how these biomarkers and metabolic parameters are affected by recent exercise in these healthy individuals. A total of 102 biomarkers and metabolic parameters were analysed in serum or plasma samples from 30 young, healthy, female adults divided into a high-fit (V̇O2peak ≥ 47 mL/kg/min, N = 15) and a low-fit (V̇O2peak ≤ 37 mL/kg/min, N = 15) group, at baseline and overnight after a single bout of exercise (60 min, 70% V̇O2peak). Our results show that total biomarker and metabolic parameter profiles were similar between high-fit and low-fit females. Recent exercise significantly affected several single biomarkers and metabolic parameters, mostly related to inflammation and lipid metabolism. Furthermore, functional biomarker and metabolic parameter categories corresponded to biomarker and metabolic parameter clusters generated via hierarchical clustering models. In conclusion, this study provides insight into the single and joined behavior of circulating biomarkers and metabolic parameters in healthy females, and identified functional biomarker and metabolic parameter categories that may be used for the characterisation of human health physiology.

Step-by-Step Modeling and Experimental Study on the Sol–Gel Porous Structure of Percolation Nanoclusters

Non-conventional crystallization techniques have been developed in recent years. Non-conventional crystallization techniques use primary structural elements (for example, clusters) rather than atoms and molecules. Modern nanomaterial science is going through great changes as an entirely new approach of non-conventional growth mechanisms is emerging due to cluster coupling, catalyzing interest in cluster physics. The formation of fractal and percolation clusters has increased. We carried out step-by-step modeling and an experimental study of the formation of fractal and percolation clusters based on tin dioxide and silicon dioxide and formed by sol–gel technology. In this paper, the growth of fractal aggregates (clusters) from sol particles SnO2 and SiO2 based on the modified models of diffusion-limited and cluster–cluster aggregation is discussed. A percolation model using simulated fractal clusters of SnO2 and SiO2 particles is proposed. Experimental data on the sol–gel percolation structure of porous nanocomposites are presented. The modeling of SnO2 and SiO2 particles, which also consist of clusters (the next step in the hierarchy), is shown. We propose a generalized hierarchical three-dimensional percolation cluster model that allows calculating the surface area, knowing the experimental sizes of macropores and taking into account the micro- and mesopores (sizes less than a few nanometers).

Persistent Brain Connectivity Changes in Healthy Volunteers Following Nitrous Oxide Inhalation

BackgroundNitrous oxide holds promise in the treatment of major depressive disorder. Its psychotropic effects and NMDA receptor antagonism have led to comparisons with ketamine. Despite longstanding use, persistent effects of nitrous oxide on the brain have not been characterized. MethodsSixteen healthy volunteers were recruited in a double-blind crossover study. In randomized order, individuals underwent a 1-hour inhalation of either 50% nitrous oxide/oxygen or air/oxygen mixtures. At least two 7.5-minute echo-planar resting-state functional magnetic resonance imaging scans were obtained before and at 2 and 24 hours after each inhalation (average 130 min/participant). Using the time series of preprocessed, motion artifact–scrubbed, and nuisance covariate–regressed imaging data, interregional signal correlations were measured and converted to T scores. Hierarchical clustering and linear mixed-effects models were employed. ResultsNitrous oxide inhalation produced changes in global brain connectivity that persisted in the occipital cortex at 2 and 24 hours postinhalation (p < .05, false discovery rate–corrected). Analysis of resting-state networks demonstrated robust strengthening of connectivity between regions of the visual network and those of the dorsal attention network, across 2 and 24 hours after inhalation (p < .05, false discovery rate–corrected). Weaker changes in connectivity were found between the visual cortex and regions of the frontoparietal and default mode networks. Parallel analyses following air/oxygen inhalation yielded no significant changes in functional connectivity. ConclusionsNitrous oxide inhalation in healthy volunteers revealed persistent increases in global connectivity between regions of primary visual cortex and dorsal attention network. These findings suggest that nitrous oxide inhalation induces neurophysiological cortical changes that persist for at least 24 hours.