Search Results (24)

The quality of groundwater, a crucial freshwater resource in cold regions, directly affects human health. This study used groundwater quality monitoring data collected in the central Songnen Plain in 2014 and 2022 as a case study. The improved DRASTICL model was used to assess the vulnerability index, while water quality indicators were selected using a random forest algorithm and combined with the entropy-weighted groundwater quality index (E-GQI) approach to realize water quality assessment. Furthermore, self-organizing maps (SOM) were used for pollutant source analysis. Finally, the study identified the synergistic migration mechanism of NH₄⁺ and Cl⁻, as well as the activation trend of As in reducing environments. The uncertainty inherent to health risk assessment was considered by developing a kernel density estimation–trapezoidal fuzzy number–Monte Carlo simulation (KDE-TFN-MCSS) model that reduced the distribution mis-specification risks and high-risk misjudgment rates associated with conventional assessment methods. The results indicated that: (1) The water chemistry type in the study area was predominantly HCO₃⁻–Ca²⁺ with moderately to weakly alkaline water, and the primary and nitrogen pollution indicators were elevated, with the average NH₄⁺ concentration significantly increasing from 0.06 mg/L in 2014 to 1.26 mg/L in 2022, exceeding the Class III limit of 1.0 mg/L. (2) The groundwater quality in the central Songnen Plain was poor in 2014, comprising predominantly Classes IV and V; by 2022, it comprised mostly Classes I–IV following a banded distribution, but declined in some central and northern areas. (3) The results of the SOM analysis revealed that the principal hardness component shifted from Ca²⁺ in 2014 to Ca²⁺–Mg²⁺ synergy in 2022. Local high values of As and NH₄⁺ were determined to reflect geogenic origin and diffuse agricultural pollution, whereas the Cl⁻ distribution reflected the influence of de-icing agents and urbanization. (4) Through drinking water exposure, a deterministic evaluation conducted using the conventional four-step method indicated that the non-carcinogenic risk (HI) in the central and eastern areas significantly exceeded the threshold (HI > 1) in 2014, with the high-HI area expanding westward to the central and western regions in 2022; local areas in the north also exhibited carcinogenic risk (CR) values exceeding the threshold (CR > 0.0001). The results of a probabilistic evaluation conducted using the proposed simulation model indicated that, except for children’s CR in 2022, both HI and CR exceeded acceptable thresholds with 95% probability. Therefore, the proposed assessment method can provide a basis for improved groundwater pollution zoning and control decisions in cold regions. Full article

Weather patterns substantially influence extreme weathers in Japan. Extreme high temperature events can cause serious health problems, including heat stroke. Therefore, understanding weather patterns, along with their impacts on human health, is critically important for developing effective public health measures. This study examines the impact of weather patterns on heat stroke risk, focusing on a two-tiered high-pressure system (DH: double high) consisting of a lower tropospheric western Pacific subtropical high (WPSH) and an overlapping upper tropospheric South Asian high (SAH), which is thought to cause high-temperature events in Japan. In this study, the self-organizing map technique was utilized to investigate the relationship between pressure patterns and the number of heat stroke patients in four populous cities. The study period covers July and August from 2008 to 2021. The results show that the average number of heat stroke patients in these cities is higher on DH days than on WPSH days in which SAH is absent. The probability of an extremely high daily number of heat stroke patients is more than twice as high on DH days compared to WPSH days. Notably, this result remains true even when WPSH and DH days are compared within the same air temperature range. This is attributable to the higher humidity and stronger solar radiation under DH conditions, which enhances the risk of heat stroke. Large-scale circulation anomalies similar to the Pacific–Japan teleconnection are found on DH days, suggesting that both high humidity and cloudless conditions are among the large-scale features controlled by this teleconnection. Early countermeasures to mitigate heat stroke risk, including advisories for outdoor activities, should be taken when DH-like weather patterns are predicted. Full article

Effectively monitoring the operational status of reactor coolant pumps (RCPs) is crucial for enhancing the safety and stability of nuclear power operations. To address the challenges of limited interpretability and suboptimal detection performance in existing methods for detecting abnormal operating states of RCPs, this paper proposes an interpretable, unsupervised anomaly detection approach. This innovative method designs a framework that combines Kernel Self-Organizing Map (Kernel SOM) clustering with Bayesian Posterior Inference. Specifically, the proposed method uses Kernel SOM to extract typical patterns from normal operation data. Subsequently, a distance probability distribution model reflecting the data distribution structure within each cluster is constructed, providing a robust tool for data distribution analysis for anomaly detection. Finally, based on prior knowledge, such as distance probability distribution, the Bayesian Posterior Inference is employed to infer the probability of the equipment being in a normal state. By constructing distribution models that reflect data distribution structures and combining them with posterior inference, this approach realizes the traceability and interpretability of the anomaly detection process, improving the transparency of anomaly detection and enabling operators to understand the decision logic and the analysis of the causes of anomalous occurrences. Verification via real-world operational data demonstrates the method’s superior effectiveness. This work offers a highly interpretable solution for RCP anomaly detection, with significant implications for safety-critical applications in the nuclear energy sector. Full article

Automatic liver and tumor segmentation in contrast-enhanced magnetic resonance imaging (CE-MRI) images are of great value in clinical practice as they can reduce surgeons’ workload and increase the probability of success in surgery. However, this is still a challenging task due to the complex background, irregular shape, and low contrast between the organ and lesion. In addition, the size, number, shape, and spatial location of liver tumors vary from person to person, and existing automatic segmentation models are unable to achieve satisfactory results. In this work, drawing inspiration from self-attention mechanisms and multitask learning, we propose a segmentation network that leverages Swin-Transformer as the backbone, incorporating self-supervised learning strategies to enhance performance. In addition, accurately segmenting the boundaries and spatial location of liver tumors is the biggest challenge. To address this, we propose a multitask learning strategy based on segmentation and signed distance map (SDM), incorporating an attention gate into the skip connections. The strategy can perform liver tumor segmentation and SDM regression tasks simultaneously. The incorporation of the SDM regression branch effectively improves the detection and segmentation performance for small objects since it imposes additional shape and global constraints on the network. We performed comprehensive evaluations, both quantitative and qualitative, of our approach. The model we proposed outperforms existing state-of-the-art models in terms of DSC, 95HD, and ASD metrics. This research provides a valuable solution that lessens the burden on surgeons and improves the chances of successful surgeries. Full article

Urbanization has intensified traffic loads, posing significant challenges to the efficiency and stability of urban road networks. Overloaded nodes risk congestion, thus making accurate intersection importance classification essential for resource optimization. This study proposes a hybrid clustering method that combines Self-Organizing Maps (SOMs), K-Means, and the Gaussian Mixture Model (GMM), which is supported by the Traffic Flow–Network Topology–Social Economy (TNS) evaluation framework. This framework integrates three dimensions—traffic flow, road network topology, and socio-economic features—capturing six key indicators: intersection saturation, traffic flow balance, mileage coverage, capacity, betweenness efficiency, and node activity. The SOMs method determines the optimal k value and centroids for K-Means, while GMM validates the cluster membership probabilities. The proposed model achieved a silhouette coefficient of 0.737, a Davies–Bouldin index of 1.003, and a Calinski–Harabasz index of 57.688, with the silhouette coefficient improving by 78.1% over SOMs alone, 65.2% over K-Means, and 11.5% over SOM-K-Means, thus demonstrating high robustness. The intersection importance ranking was conducted using the Mahalanobis distance method, and it was validated on 40 intersections within the road network of Zibo City. By comparing the importance rankings across static, off-peak, morning peak, and evening peak periods, a dynamic ranking approach is proposed. This method provides a robust basis for optimizing resource allocation and traffic management at urban intersections. Full article

In this paper, we present an unprecedented method based on Kohonen networks that is able to automatically recognize partial discharge (PD) classes from phase-resolved partial discharge (PRPD) diagrams with features of various simultaneous PD patterns. The PRPD diagrams were obtained from the stator windings of a real-world hydro-generator rotating machine. The proposed approach integrates classification probabilities into the Kohonen method, producing self-organizing probability maps (SOPMs). For building SOPMs, a group of PRPD diagrams, each containing a single PD pattern for training the Kohonen networks and single- and multiple-class-featured samples for obtaining final SOPMs, is used to calculate the probabilities of each Kohonen neuron to be associated with the various PD classes considered. At the end of this process, a self-organizing probability map is produced. Probabilities are calculated using distances, obtained in the space of features, between neurons and samples. The so-produced SOPM enables the effective classification of PRPD samples and provides the probability that a given PD sample is associated with a PD class. In this work, amplitude histograms are the features extracted from PRPDs maps. Our results demonstrate an average classification accuracy rate of approximately 90% for test samples. Full article

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%. Full article

The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases. Full article

Early detriment in the muscle mass quantity, quality, and functionality, determined by calf circumference (CC), phase angle (PA), gait time (GT), and grip strength (GSt), may be considered a risk factor for sarcopenia. Patterns derived from these parameters could timely identify an early stage of this disease. Thus, the present work aims to identify those patterns of muscle-related parameters and their association with sarcopenia in a cohort of older Mexican women with neural network analysis. Methods: Information from the functional decline patterns at the end of life, related factors, and associated costs study was used. A self-organizing map was used to analyze the information. A SOM is an unsupervised machine learning technique that projects input variables on a low-dimensional hexagonal grid that can be effectively utilized to visualize and explore properties of the data allowing to cluster individuals with similar age, GT, GSt, CC, and PA. An unadjusted logistic regression model assessed the probability of having sarcopenia given a particular cluster. Results: 250 women were evaluated. Mean age was 68.54 ± 5.99, sarcopenia was present in 31 (12.4%). Clusters 1 and 2 had similar GT, GSt, and CC values. Moreover, in cluster 1, women were older with higher PA values (p < 0.001). From cluster 3 upward, there is a trend of worse scores for every variable. Moreover, 100% of the participants in cluster 6 have sarcopenia (p < 0.001). Women in clusters 4 and 5 were 19.29 and 90 respectively, times more likely to develop sarcopenia than those from cluster 2 (p < 0.01). Conclusions: The joint use of age, GSt, GT, CC, and PA is strongly associated with the probability women have of presenting sarcopenia. Full article

Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible. Full article

To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to the complexity of the underlying factors and lack of information related to changes in forest coverage in the future. To address this issue, a systematic case study based on the Guizhou Province, China, was carried out. First, three archetypes of driving factors were recognized through the self-organizing maps (SOM) algorithm: the high-strength ecological archetype, marginal archetype, and high-strength archetype dominated by human influence. Then, the probability of forest restoration in the context of ecological restoration was predicted using Bayesian belief networks in an effort to decrease the uncertainty of evaluation. Results show that the overall probability of forest restoration in the study area ranged from 22.27 to 99.29%, which is quite high. The findings from regions with different landforms suggest that the forest restoration probabilities of karst regions in the grid and the regional scales were lower than in non-karst regions. However, this difference was insignificant mainly because the ecological restoration in the karst regions accelerated local forest restoration and decreased the ecological impact. The proposed method of driving-factor clustering based on restoration as well as the method of predicting restoration probability have a certain reference value for forest management and the layout of ecological restoration projects in the mid-latitude ecotone. Full article

Recent improvements in micro-scale material descriptions allow to build increasingly refined multiscale models in geomechanics. This often comes at the expense of computational cost which can eventually become prohibitive. Among other characteristics, the non-determinism of a micro-scale response makes its replacement by a surrogate particularly challenging. Machine Learning (ML) is a promising technique to substitute physics-based models, nevertheless existing ML algorithms for the prediction of material response do not integrate non-determinism in the learning process. Is it possible to use the numerical output of the latest micro-scale descriptions to train a ML algorithm that will then provide a response at a much lower computational cost? A series of ML algorithms with different levels of depth and supervision are trained using a data-driven approach. Gaussian Process Regression (GPR), Self-Organizing Maps (SOM) and Generative Adversarial Networks (GANs) are tested and the latter retained because of its superior results. A modified GANs with lower network depth showed good performance in the generation of failure probability maps, with good reproduction of the non-deterministic micro-scale response. The trained generator can be incorporated into existing multiscale models allowing to, at least partially, bypass the costly micro-scale computations. Full article

The analysis of factors influencing urban shrinkage is of great interest to spatial planners and policy makers. Population loss is usually the most relevant indicator of this shrinkage, but many other factors interact in complex ways over time. This paper proposes a multidimensional and spatio-temporal analysis of the shrinkage process in Spanish municipalities between 1991 and 2020. The method is based on the potentiality provided by self-organizing maps. The generated maps group municipalities according to hidden partial correlations among the data behind the variables characterizing the municipalities at different dates. In addition, as the number of map nodes is too big to allow for the detection of distinct types of municipalities, a Ward clustering algorithm is applied to identify homogeneous areas with a higher probability of shrinkage occurring over time. The results indicate that the municipalities with the lowest shrinkage are more stable and have a geographical concentration: they correspond to areas where peripheralization may occur (creation of surrounding districts close to main urban centers) and constitute the hinterland of large functional areas. The results also report a path of decline, with an important increase in the number of municipalities with higher shrinkage values. This approach has important implications for policy making since local governments may profit from shrinkage predictions. Full article

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10⁻⁴ and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China. Full article

To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period and inundation map would be a useful technique for responding to floods in real-time and could provide an expected inundation area by return period. In this study, return period estimation for observed rainfall was performed via PNN (probabilistic neural network). SVR (support vector regression) and a SOM (self-organizing map) were used to predict flood volume and inundation maps. The study area was the Gangnam area, which has experienced extensive urbanization. The database for training SVR and SOM was constructed by one- and two-dimensional flood analysis with consideration of 120 probable rainfall events. The probable rainfall events were composed with 2–100 year return periods and 1–3 hour durations. The SVR technique was used to predict flood volume according to the rainfall return period, and the SOM was used to cluster various expected flood patterns to be used for predicting inundation maps. The prediction results were compared with the simulation results of a two-dimensional flood analysis model. The highest fitness of the predicted flood maps in the study area was calculated at 85.94%. The proposed method was found to constitute a practical methodology that could be helpful in improving urban flood response capabilities. Full article