Self-organizing Map Research Articles

The influence of lightweight wearable resistance on whole body coordination during sprint acceleration among Australian Rules football players.

Rapid acceleration is an important quality for field-based sport athletes. Technical factors contribute to acceleration and these can be deliberately influenced by coaches through implementation of constraints, which afford particular coordinative states or induce variability generally. Lightweight wearable resistance is an emerging training tool, which can act as a constraint on acceleration. At present, however, the effects on whole body coordination resulting from wearable resistance application are unknown. To better understand these effects, five male Australian Rules football athletes performed a series of 20 m sprints with either relatively light or heavy wearable resistance applied to the anterior or posterior aspects of the thighs or shanks. Whole body coordination during early acceleration was examined across eight wearable resistance conditions and compared with baseline (unresisted) acceleration coordination using group- and individual-level hierarchical cluster analysis. Self-organising maps and a joint-level distance matrix were used to further investigate specific kinematic changes in conditions where coordination differed most from baseline. Across the group, relatively heavy wearable resistance applied to the thighs resulted in the greatest difference to whole body coordination compared with baseline acceleration. On average, heavy posterior thigh wearable resistance led to altered pelvic position and greater hip extension, while heavy anterior thigh wearable resistance led to accentuated movement at the shoulders in the transverse and sagittal planes. These findings offer a useful starting point for coaches seeking to use wearable resistance to promote adoption of greater hip extension or upper body contribution during acceleration. Importantly, individuals varied in how they responded to heavy thigh wearable resistance, which coaches should be mindful of.

Advancing Light Aircraft Health Monitoring with Flight Phase Clustering

This paper addresses the clustering of flight phases of a light aircraft for health monitoring using vibration data. The aim is to improve diagnostic and prognostic functions. Grouping condition monitoring data under similar operating conditions is significant for predictive maintenance. Clustering also supports advanced analytics for fault detection and estimation of remaining life. The proposed framework uses self-organizing maps for flight phase clustering. The findings show that the algorithm can recognize and classify flight phases in various operational domains. Additionally, visualization of cluster maps uncovers complex patterns and non-linear relationships in sensor data under different flight conditions. As a followup, analyzing the vibration properties within these estimated clusters (regimes) provides insights from condition monitoring data behavior during flight phases. The results confirm the effectiveness of the method, but also confirm that determining light aircraft regimes requires more focus due to their unique flight patterns that are absent in commercial airliners. In this context, this research has dealt with these unique patterns and provided the foundation for a new model for clustering with an attempt to contribute valuable insights into improving the reliability and efficiency of light aircrafts.

Geochemical fingerprints, evolution, and driving forces of groundwater in an alpine basin on Tibetan Plateau: Insights from unsupervised machine learning and objective weight allocation approaches

Study RegionTongde Basin, a typical alpine basin on the northeastern Tibetan Plateau Study focusResearch on the hydrochemical pattern and availability of groundwater is very limited in alpine regions, which greatly hinders the rational ulitilization and scientific management of groundwater resources. Multiple approaches integrating self-organizing map, K-means, entropy weight were employed to elucidate the distinct hydrochemical evolution patterns and controlling mechanisms of groundwater in alpine basins with dense human activities. New hydrological insights for the regionMultiple hydrochemical evolution patterns were discovered for groundwater in present alpine basin on northeastern Tibetan Plateau. Groundwater presents a gradual hydrochemical evolution from the piedmont to the riverside under the natural regulation with the increasing of TDS and major ions along the flow path. While, groundwater at some sporadic sites is featured with relatively high total alkalinity due to carbonate mineral dissolution. The riverine groundwaters are regulated by the strong hydrodynamic condition of river and demonstrate fresher hydrochemical characteristics. Groundwaters in the irrigation areas are featured by elevated nitrate levels with a maximum of 42.86 mg/L, attributed to agricultural pollution inputs. A conceptual model was established to illustrate the distinct hydrochemical fingerprints, evolution patterns and controlling mechanisms of groundwater in alpine basins that with dense human activities. This research can enhance the understanding of groundwater chemical status, genesis and availability, and is beneficial for the conservation and sustainable management of groundwater resources in ecologically fragile alpine regions worldwide.

Identifying the pattern of shallow groundwater hydrochemistry and its driving factors in a typical estuarine delta of Poyang Lake watershed, China: Insights into water quality assessment

Study regionA typical estuarine delta of Gan−Xiu River within Poyang Lake watershed, situated in the north of Jiangxi Province, China Study focusAlthough groundwater is considered as a crucial water resource for social development and public health, it presents significant challenges in preventing the deterioration of groundwater quality in the seasonal floodplain regions. Hence, this study focuses on the spatiotemporal variations of ionic concentrations of shallow groundwater in an estuarine delta of Gan-Xiu River in Poyang Lake watershed, which is greatly complicated by complex hydrological regimes and intensive anthropogenic activities such as cropland fertilizer. The potential factors controlling the ionic concentrations were identified using self-organizing map clustering and Piper diagram approaches. Moreover, the overall water quality of shallow groundwater was assessed according to the ionic compositions. New hydrological insights for the region(1) Shallow groundwater chemistry was influenced by weathering silicate and carbonate from ionic and Gibbs plots, which is highly associated with the distribution of the carbonate rock fissure-karst aquifer in the study region. (2) The contribution of groundwater to river water varied from 53.2 % to 71.5 % via isotopic analysis, suggesting that groundwater dominate the component of river water. (3) Most samples from cluster types are suitable for drinking and irrigation use except for sites from Cluster 6, which were characterized by the higher concentrations of Cl−, NO3− and SO42−.

Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents

This study aimed to automatically classify physical fitness and cardiometabolic risk in a Chilean adolescent using self-organizing maps. This cross-sectional study analysed a nationally representative database from the Physical Education Quality Measurement System (n = 7197). Physical fitness and cardiometabolic risk variables were derived from anthropometric indicators. Self-Organizing maps (SOM) were employed to identify participant profiles based on an unsupervised predictive model. After implementing and training the SOM, a detailed analysis of the generated maps was conducted to interpret the revealed relationships and clusters. The analysis resulted in three classification groups, categorizing the sample into low, moderate, and high-risk levels. Students with better physical fitness exhibited lower cardiometabolic risk levels and a lower body mass index. SOM, through an unsupervised model, is a reliable tool for classifying cardiometabolic risk and physical fitness in adolescents.

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Context. Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. Aims. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. Methods. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. Results. The findings demonstrate a high agreement level (∼70–90%) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry, and CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single–binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than ∼700 nm. Conclusions. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

Dominant Synoptic Systems for Summer Precipitation over the Complex Terrain of Southwestern China

Abstract Understanding precipitation over complex terrain, such as southwestern China, requires the consideration of both multiscale circulation and topography. First, the dominant synoptic system must be clarified, as it determines how multiscale topography affects precipitation. Here, based on a self-organizing map, large-scale winds are categorized into anomalous-westerly types, anomalous-easterly types, and transitional types. Four synoptic-scale systems (vortex type, cold-front type, tropical-depression type, and weak-synoptic-forcing type) dominate the summer precipitation. The vortex type occurs with strengthened large-scale westerlies, and its precipitation is distributed within the moisture convergence region. The cold-front type, tropical-depression type, and weak-synoptic-forcing type exhibit large-scale easterly anomalies. For the cold-front type, a low-level northeasterly blocked by topography shapes the northwest–southeast-oriented front zone at the upper highland slope. The precipitation frequency and intensity are high within the frontal zone, while the intensity is weak on both sides. For the tropical-depression type, moist low-level easterlies uplifted by westward-rising topography anchor precipitation at the lower slope. Large precipitation for the tropical-depression type is attributed to a high frequency. Large-scale horizontal winds are the weakest for the weak-synoptic-forcing type, and the local topography influences the scattered precipitation distribution. Both the frequency and intensity are high for the weak-synoptic-forcing type. Overall, long-lasting nocturnal events dominate the precipitation of the four synoptic types, while large-scale easterlies favor precipitation events with shorter durations and earlier peaks. For obvious synoptic systems, large-scale topography influences precipitation via a dynamic blocking effect, while the thermodynamic role of local topography is important with a weak-synoptic-forcing. Significance Statement Clarifying dominant synoptic systems is highly important for understanding precipitation over complex terrain, as the effect of topography on precipitation varies with different synoptic backgrounds. Taking southwestern China as a representative of complex terrain, this study objectively identified the dominant synoptic systems associated with summer precipitation. The distribution and fine-scale characteristics of precipitation have been further analyzed considering the combined influence of multiscale circulation and topography. In addition to advancing our understanding of precipitation in southwestern China, this study provides a reference for analyzing precipitation in other regions with complex terrains.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

In the era of globalization, digitalization, and diversification, currently societal cities face risks intertwined by uncertainty and complexity. Thus, it is crucial to address risks concerns to achieve modernization of spatial management. Moreover, how to scientifically identify the risk pattern of territorial space and how to map and control it in space becomes a critical and urgent issue to be addressed for achieving modernization of spatial management. Understanding the intricate relationship between land use, environmental factors, and population density is essential for identifying risk patterns that can inform effective disaster management strategies. By employing advanced analytical methods, this study aims to provide a comprehensive framework for recognizing and mitigating territorial spatial risks in an increasingly complex urban landscape. This study used a case study to conduct the unsupervised algorithm of stack autoencoder self-organizing map to analyze the distribution and spatial clustering of Shenzhen's land spatial risk pattern and reveal the driving mechanism of its risk pattern formation in detail. It was found that: (1) The overall territorial spatial risk pattern of Shenzhen presents a distribution trend of "high in the west and low in the east, high in the south and low in the north"; that is, I and II risk areas are clustered in the hilly areas in the northeast and IV and V risk areas in the coastal areas in the southwest; (2) Population density, land use intensity, seawater intrusion, geological disasters, relief amplitude, soil type, soil erosion intensity are the main driving forces for the formation of Shenzhen's territorial spatial risk pattern. In particular, seawater intrusion, population density and land use intensity, soil type were found having significantly synergistic impact on the risk pattern.

What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square

Plazas are an essential pillar of public life in our cities. Historically, they have been seen as public fora, hosting public events that fostered trade, interaction, and debate. However, with the rise of modern urbanism, city planners considered them as part of a larger strategic development scheme overlooking their social importance. As a result, plazas have lost their function and value. In recent years, awareness has risen of the need to re-activate these public spaces to strive for social inclusion and urban resilience. Geometric and urban features of plazas and their surroundings often suggest what kinds of usage the public can make of them. In this project, we explore the application of machine learning to predict the suitability of events in public spaces, aiming to enhance urban plaza design. Learning from traditional urbanism indicators, we consider factors associated with the features of the public space, such as the number of people and the high degree of comfort, which are evolved from three subcategories: external factors, geometric shape, and design factors. We acknowledge that the predictive capability of our model is constrained by a relatively small dataset, comprising 15 real plazas in Madrid augmented digitally to 2025 fictional scenarios through self-organising maps. The article details the methods to quantify and enumerate quantitative urban features. With a categorical target variable, a classification model is trained to predict the type of event in the urban space. The model is then evaluated locally in Grasshopper by visualising a parametric verified geometry and deploying the model on other existing plazas worldwide regarding geographical proximity to Madrid, where to share or not the same cultural and environmental conditions. Despite these limitations, our findings offer valuable insights into the potential of machine learning in urban planning, suggesting pathways for future research to expand upon this foundational study.

A New Heuristic Hybrid Model for Cancer-Based P-Glycoprotein Drug Classification: Automated Prediction

As the population improves in real-time applications, drug identification becomes an increasingly difficult task. Various biotechnological applications based on in vitro fertilization can benefit from better drug identification. For many nations, agriculture is the backbone of cancer medication development; increasing crop yields is the key to better drug identification in agricultural contexts. Computer vision applications suffer as a result of the efficient degradation of production quality and quantity caused by p-glycoprotein drugs used in cancer treatment. The identification of cancer-based p-glycoprotein drugs is an aggressive concept in the present day, due to the various symptoms present in cancer. Various authors use soft computing techniques and machine learning approaches to predict these drugs. For the purpose of automatically identifying based p-glycoprotein drugs in cancers, this paper proposes a Hybrid Novel Heuristic Model (HNHM) that combines Self-Organizing Maps (SOMs) with Convolution neural networks (CNNs). The primary challenge in identifying cancer drugs based on p-glycoprotein is feature extraction; hence, dimensionality reduction is crucial in filtering out noise from the cancer sub-cancer drug dataset and separating the subset of the cancer drug dataset affected by the p-glycoprotein drug using support vector machines (SOM). It is a promising approach to automatically identify based p-glycoprotein drugs in cancers by exploring protein features in cancer and predicting which patches are affected by virus-bacteria. CNN is used to match cancer sub-cancer drug data sets. In this study, we use sub-Cancer drug data sets to develop a hybrid heuristic approach to identify based p-glycoprotein drugs found in cancers. Clinical trials of the suggested hybrid model were conducted using sub-cancer drug data sets derived from peach cancers and the publicly available Cancer's drug datasets housed in the UCI repository. With less training required to explore based on p-glycoprotein drugs from sub-Cancer drug datasets, the suggested hybrid model achieves an accuracy of nearly 99.93% when tested on different testing datasets for sub cancer drugs. When compared to other state-of-the-art methods, this one significantly reduces running time while providing efficient performance in a number of p-glycoprotein drug prediction metrics, including recall, sensitivity, and specificity

Strategic Formation of Agricultural Market Clusters in Ukraine: Emerging as a Global Player

This study investigates the cluster approach to optimize strategies for agricultural enterprises in Ukraine, emphasizing geographical proximity as a key factor in cluster formation. The research applies Kohonen Self-Organizing Maps (SOMs) and Ward’s hierarchical clustering to classify enterprises based on storage capabilities, transport logistics, crop yields, and military risk exposure. By analyzing these factors, this study identifies distinct patterns of innovation adoption, strategic management, and economic resilience among the clusters. The findings highlight variations in competitiveness and resource efficiency, providing a detailed understanding of regional economic performance. Unlike previous research, this study offers a novel integration of conflict-related risks into the clustering methodology, revealing new insights into how military factors influence cluster dynamics. Comprehensive maps and diagrams illustrate the spatial and economic distribution of clusters, aiding in visual interpretation. The results propose strategic measures tailored to enhance agricultural productivity and competitiveness, particularly in Ukraine’s current military context. This approach offers a more adaptive framework for managing agricultural enterprises, promoting resilience and long-term sustainability in global markets.

Thoron, radon and microbial community as supportive indicators of seismic activity in groundwater

Earthquakes have a significant impact on groundwater environments as well as human life. However, identifying active and affected zones from seismic events using isotopic and microbial diversity indicators remains a challenging frontier. To validate the applicability of this coupled method for real-time analysis, we analyzed thoron (220Rn), radon (222Rn), microbial community compositions, and hydrochemistry in groundwater samples during the 2017 Pohang earthquake for the first time. We observed the detection of 220Rn in groundwater right before the aftershocks, with a high correlation to 222Rn concentrations. This indicates that 220Rn and 222Rn can serve as reliable seismic indicators for real-time analysis. The microbial data can assist in identifying affected groundwater zones, particularly when real-time detection of 220Rn is not feasible. At the phylum level, Peregrinibacteria and Firmicutes were only found in samples with detected thoron. At the genus level, hydrogen-oxidizing or sulfur-oxidizing bacteria could serve as indicators of active zones. Two statistical analyses, self-organizing map (SOM) and principal component analysis (PCA) using hydrochemical parameters, also correlated with the results from these coupled indicators. This study demonstrates the theoretical and practical applicability of 220Rn, 222Rn, and microbial community compositions as new multi-faceted ecological indicators, whether for real-time analysis or otherwise.

An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks

Device-to-device (D2D) communication is an essential part of the 5G system used in cellular networks, which enables devices to communicate with each other without passing the base station BS. It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. It results in improved throughput, energy efficiency, and other parameters. At the same time, the introduction of D2D communication in cellular networks creates new technical challenges to be addressed. One of the major issues is the mitigation of interference between device users (DUs) and primary users (PUs). This issue leads to performance degradation if it is not managed properly. In this paper, an intelligent distributed channel selection framework (IDCSF) in D2D communication for 5G networks with hybrid selection modes of D2D is proposed to help DUs select the best channel for transmission to mitigate interference. This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. Furthermore, PU activity model is adjusted to aid in recovering from bad channel selection decisions. Moreover, fuzzy logic technique is utilized to determine the reliable channels which can help the DU along with channel selection algorithm to find channel’s weight values. The channel with highest weight value is selected as the best channel. Simulation results show that the proposed framework IDCSF enhances selecting the best channel, improves throughput and spectral utilization, and reduces average delay, interference and search time compared to other approaches.

Telomere shortening and cardiac alterations in asymptomatic type 2 diabetes patients: impact of sex

Abstract Background Telomere shortening is associated with the occurrence of deleterious events in the context of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM). However, no evidence is available on the link between leukocyte telomere length (LTL) and subclinical cardiac alterations in T2DM patients. Purpose Our aim is to evaluate the relation between LTL and cardiovascular function in asymptomatic T2DM patients, taking into consideration the impact of the sex. Methods We analyzed a cohort of asymptomatic T2DM patients free from any overt cardiovascular disease (including coronary artery disease) and other T2DM comorbidities. A detailed metabolic and echocardiographic evaluation was performed together with LTL measurements at inclusion and after 2 years of follow-up. Univariate analyses in regard of the comparison of three graded LTL tertiles and different gender were used to test individual relationships betweenbioclinical and echocardiographic parameters and LTL measures. Subsequently, an unsupervised clustering method (self-organizing maps [SOM]) was used to identify cluster at risk of cardiomyopathy. Results We studied 89 T2DM patients aged 50±4 years (43% female) with a mean T2DM duration of 10±5.8 years and an average follow-up of 2.1±0.2 years. Leukocyte TL analysis demonstrated a slight but significant attrition after 2 years. The analysis of echocardiographic variables (transmitral flow velocities, velocity of mitral annular motion, left ventricular ejection fraction) did not show any evidence for ouvert cardiomyopathy. However, in the lower LTL tertile a higher proportion of patients with global longitudinal strain [GLS]<18% was found [41.6% vs. 22.8% vs. 21.6%]. Interestingly, leukocyte TL was associated with T2DM duration, FIB-4, a noninvasive indice used to assess fibrosis associated with non-alcoholic fatty liver disease (NAFLD), and usage of GLP-1 agonists. Comparison of men and women by univariate analysis did not show any differences in both LTL and echocardiographic parameters. However, unsupervised cluster method (SOM) unmasked a subgroup of female T2DM patients with diastolic alterations (e’ mean) and systolic alteration (GLS) both linked with lower LTL, whereas no specific subgroup in male T2DM patients could be distinguished. Conclusion Our study identifies subclinical diastolic and systolic dysfunction together with shorter LTL in a subgroup of female T2DM patients. These findings raise the importance of considering sex effect in the understanding of diabetic cardiomyopathy.

Study on the genesis and development trend of tropical depressions under different large-scale backgrounds

Based on self-organizing maps (SOM), large-scale backgrounds associated with tropical depression (TD) genesis over the western North Pacific (WNP) in 1949–2021 are classified into four circulation patterns, monsoon gyre (MG) pattern, monsoon confluence (MC) pattern, monsoon trough (MT) pattern and easterly wave (EW) pattern. TDs generated in the MC pattern has the southernmost average genesis location and the highest development probability, while TDs occurred in the EW pattern are averagely located northernmost and their probability of development is the lowest. TDs formed in the MG, MT and EW patterns are most active in August, whereas in the MC pattern, TD genesis number peaks in October. Advantageous conditions for TD genesis vary in different circulation patterns. The vigorous vorticity "embryo" provides stronger initial disturbances for MG pattern; The strong upper-level divergence and the weak deep-layer VWS provide sufficient dynamic conditions for the MC pattern; The MT pattern possess the highest SST, which supplies an ample supply of heat and moisture; The EW pattern has less beneficial conditions compared with other three patterns. The Extreme Gradient Boosting (XGBoost) method is applied to quantify the relative importance of individual factors to TD development trend. 500-hPa vorticity, 200-hPa divergence and SST are major dynamic and thermal affecting factors for TD development, the importance of which all ranked at top four in the four patterns; VWS plays an indispensable role in TD development for the MC and EW patterns; Comparely,850-hPa vorticity and vertically integrated water vapor flux are not as important as above environmental factors in deciding whether a TD develops.

CHARACTERIZATION OF A FLUVIAL DELTAIC SALT-INFLUENCED SYSTEM FOR CO2 STORAGE USING SEISMIC POST-STACK INVERSION AND SELF-ORGANIZING MAPS IN THE UPPER MIOCENE, ONSHORE LOUISIANA

Basins that have historically been explored for hydrocarbons are now seen through a new perspective for carbon capture and storage. Legacy hydrocarbon reservoirs may now have the potential to become carbon storage targets. The Miocene Sandstone in south Louisiana is a stratigraphic interval that contains favorable zones for carbon storage, and seismic interpretation is a critical tool for characterizing these sands and estimating the amount of possible carbon storage. New workflows leveraging machine learning and seismic inversion can be applied to legacy fields to generate a new estimate for pore space and carbon storage capacity. The study area contains a 3D seismic survey covering 310 mi2 (803 km2) and 16 wells. Using these data, the workflow leverages self-organized maps (SOM) to identify zones of interest both laterally and vertically for carbon storage. SOM provides an uplift in resolution for interpretation compared to single attributes. A model-based acoustic impedance inversion produces porosity information that is used in tandem with SOM outputs. The acoustic impedance results strongly correlate to porosity within the target sands, and the SOM and porosity maps delineate specific sand lobes for potential storage. Wellbore data within the sand lobe align with SOM and inversion results and show rock quality with 26% average porosity and 90 feet gross thickness. The research highlights a project sized target within the middle Miocene containing 10.3 Megatons of estimated CO2 storage. Using SOM and inversion maps in tandem is an effective way for screening an area for carbon storage targets.

How to use efficiently airborne criteria pollutants and radon-222 in source apportionment: A self-organizing maps approach

Pollutant source apportionment represents one of the fundamental activities in environmental science. Several efficient chemometric tools are available to the scope, mostly based on multivariate techniques and usually applied to aerosol chemical speciation data. In the present work, an alternative source profiling method is proposed, based on the self-organizing maps (SOM) algorithm. Moreover, the dataset used includes typical criteria pollutants and physical parameters related to airborne particulate matter widely used as a complement of aerosol source apportionment and largely available at a higher time resolution than bulk aerosol samplings, allowing the information on the dynamic behavior of the local airshed to be extended. In this work, data was collected at a coastal location in NW Italy, between January and July 2012. Hourly concentrations of typical gaseous pollutants (SO2, NO, NO2, benzene, toluene, (m-p)-xylene, o-xylene), black-carbon and particle number concentrations by an optical particle sizer (OPS) were collected. The dataset was integrated with radon-222 activity concentration and meteorological parameters to enrich and refine the information obtained by SOM computation as well as to improve the air pollution source localization. Despite the lower specificity of criteria pollutants, the approach developed was capable of revealing distinct pollution sources such as the urban background traffic, the coal-fired power plant active at the time of the study, and the harbor, in agreement with previous PM-based source apportionment studies carried out locally, while enlightening peculiar dynamical conditions detectable at the sub-daily time scale. The application of the SOM algorithm, with the integration of meteorological parameters and atmospheric radon, proved to be very efficient in unveiling the air pollution sources.

Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients.

Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

Correlation between Different Factors of Non-point Source Pollution in Yangtze River Basin

This research used the improved export coefficient model to estimate non-point source load in sub-watersheds and a self-organizing map model to identify the correlation relationship of factors, that can be affected by non-point source load in Yangtze River Basin. Research results indicate that total nitrogen load is 4.87 - 15.53 kg/ha; total phosphorus load is 0.18 - 0.51 kg/ha; Dong Ting Lake sub-basin has the lowest contribution value of total nitrogen and phosphorus; Tai Lake sub-basin has the highest contribution load of total nitrogen and total phosphorus; Jinsha River sub-basin reveals the highest value of total nitrogen and phosphorus contribution on grassy land and desert land; Tai Lake sub-basin has the highest total nitrogen and total phosphorus load on forest, agricultural land, and urban construction land. The correlation relationship of factors reveals a very close correlation with each other as precipitation, total nitrogen load, and total phosphorus load factors have correlation in the highest value, in which total nitrogen load and total phosphorus load factors have a close correlation with each other from the lowest value to highest value; wetland and barren land factors correlate with the lowest value and the highest value; agricultural land, forestland, and grassy land factors correlate with one another in the high value and the highest value. The forest cover rate factor, as well as the population factor, does not correlate with other factors.