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The interpretation of ligand–target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand–target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design. [ABSTRACT FROM AUTHOR]

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As urbanization has accelerated, the coverage area of water distribution networks (WDNs) has been significantly increasing, making the management of large WDNs more difficult. To manage WDNs more accurately, water utilities split them into district metered areas (DMAs) of different sizes. However, the design of static DMAs has limitations in hydraulic performance during abnormal conditions in a WDN. To address this issue, first, this paper proposes a method for optimal DMA layout based on the combination of the self-organizing map (SOM) algorithm and the Leiden algorithm, achieving the optimal multilevel DMA layout and obtaining boundary pipes between DMAs. Then, the nondominated sorting genetic algorithm III (NSGAIII) method is employed to select the optimal arrangement of valves and flowmeters on boundary pipes from multiple perspectives, controlling the opening and closing of valves to achieve dynamic DMA partition. In the case of L-TOWN, the results show that the employed method reduces the average pressure variance by 15.6% compared with Louvain algorithms in the optimal multilevel DMA, which also effectively reduces bad connections between DMAs. Additionally, the obtained dynamic DMA layout exhibits excellent hydraulic performance. Under the two abnormal conditions, including fire events and increased water demand, the resilience index increased by 42% and 16%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l 1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM. • Introduction of novel reweighted zero-attractor term in loss function for heightened SOM algorithmic precision. • Heightened convergence rates and improved behavior, marked by earlier error steady-state attainment during training. • Enhanced robustness against outliers and superior handling of imbalanced data in multiclass clustering scenarios. • Rigorous experimental evaluations across six distinct datasets effectively underpin proposed algorithmic enhancements. [ABSTRACT FROM AUTHOR]

The automatic analysis of histology images is an open research field where machine learning techniques and neural networks, especially deep architectures, are considered successful tools due to their abilities in image classification. This paper proposes a granular computing methodology for histopathological image classification. It is based on embedding tiles of histopathology images using deep metric learning, where a self-organizing map is adopted to generate the granular structure in this learned embedding space. The SOM enables the implementation of an explainable mechanism by visualizing a knowledge space that the experts can use to analyze and classify the new images. Additionally, it provides confidence in the classification results while highlighting each important image fragment, with the benefit of reducing the number of false negatives. An exemplary case is when an image detail is indicated, with small confidence, as malignant in an image globally classified as benign. Another implemented feature is the proposal of additional labelled image tiles sharing the same characteristics to specify the context of the output decision. The proposed system was tested using three histopathology image datasets, obtaining the accuracy of the state-of-the-art black-box methods based on deep learning neural networks. Differently from the methodologies proposed so far for the same purpose, this paper introduces a novel explainable method for medical image analysis where the advantages of the deep learning neural networks used to build the embedding space for the image tiles are combined with the intrinsic explainability of the granular process obtained using the clustering property of a self-organizing map. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the effects of toolpath patterns, geometry types, and layering effects on the mechanical properties of parts manufactured by direct energy deposition (DED) additive manufacturing using data analysis and machine learning methods. A total of twelve case studies were conducted, involving four distinct geometries, each paired with three different toolpath patterns based on finite element method (FEM) simulations. These simulations focused on residual stresses, strains, and maximum principal stresses at various nodes. A comprehensive analysis was performed using a linear mixed-effects (LME) model, principal component analysis (PCA), and self-organizing map (SOM) clustering. The LME model quantified the contributions of geometry, toolpath, and layer number to mechanical properties, while PCA identified key variables with high variance. SOM clustering was used to classify the data, revealing patterns related to stress and strain distributions across different geometries and toolpaths. In conclusion, LME, PCA, and SOM offer valuable insights into the final mechanical properties of DED-fabricated parts. [ABSTRACT FROM AUTHOR]

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR. [ABSTRACT FROM AUTHOR]

Soil and water conservation is one of the most important parts of the national ecological civilization. The spatial control area of soil and water conservation can be divided to effectively manage the soil and water loss regions. However, it is still lacking in the spatial delineation in the regional division of soil and water conservation. Only a few studies have been focused on the spatial control of soil and water conservation, according to the small watershed. This study aims to explore the spatial control zoning for soil and water conservation, and then implement the differentiated protection and management measures. The universal soil loss equation (USLE) model was used to calculate the potential and actual soil erosion. The main influencing factors of soil erosion were determined by random forests. A self-organizing feature map (SOFM) was used to determine the spatial control zone of soil and water conservation in the Dabie Mountain area on a small catchment scale. The results showed that: 1) The average potential and actual soil erosion were 84415.7 t/(km²·a) and 210.25 t/(km²·a), respectively. The actual soil erosion was distributed mainly in 0-300 t/(km²·a) at the small watershed scale. There was the basically same distribution of spatial patterns under the potential and actual soil erosion. The high-value area was distributed mainly in the central and eastern mountain areas at the high elevation. 2) Vegetation coverage and slope were the main influencing factors of potential and actual soil erosion at the small watershed scale, indicating a significantly positive correlation with the potential soil erosion (P<0.01). The high vegetation cover area was concentrated in the hinterland of Dabie Mountain. The high slope area was extended from the west to the east along the ridgeline of Dabie Mountain. 3) The SOFM results showed that the spatial control zone of soil and water conservation was divided into three areas: key prevention, general prevention, and the rest area at the small watershed scale. Among them, the key prevention area involved 710 small watersheds with an area of 1 5287.4 km². There were 890 small watersheds in the general prevention area, covering an area of 18 874.4 km². Two prevention areas accounted for 61.2% of the study area. There was an outstanding difference between actual and potential soil erosion and slope among regions. The classification index can serve as the spatial control of soil and water conservation. The finding can provide theoretical support and decision-making on the spatial control regionalization for soil and water conservation. [ABSTRACT FROM AUTHOR]

Coastal moorings allow scientists to collect long-term datasets valuable in understanding shelf dynamics, detecting climate variability and changes, and evaluating their impacts on marine ecosystems. Continuous time series data from moorings is often disrupted due to mooring losses or instrument failures, which prevents us from obtaining complete and accurate information on the marine environment. Here, we present an updated version of the 14-year subsurface mooring dataset off the southwest coast of Western Australia during 2010–2023 (https://doi.org/10.25919/myac-yx60 , Bui and Feng, 2024). This updated dataset offers continuous daily temperature and current data with a 5-meter vertical resolution, collected from six coastal Integrated Marine Observing System (IMOS) moorings at depths between 48 m and 500 m. Self-Organizing Map (SOM) machine learning technique is applied to fill in the data gaps in the previous version. The usage of the in-filled data product is demonstrated by detecting sub-surface marine heatwaves on the Rottnest shelf. The data products can be used to characterise subsurface features of extreme events such as marine heatwaves, and marine cold-spells, influenced by the Leeuwin Current and the wind-driven Capes Current, and to detect long-term change signals along the coast. [ABSTRACT FROM AUTHOR]

Background: Anxiety is the most prevalent comorbidity among children and adolescents with autism spectrum disorder (ASD), yet little is known about the associated risk factors. Methods: In a heterogenous cohort of children aged 5–18 years old (n = 262, 42% ASD), participants and their parents completed standardized questionnaires to assess anxiety, ASD symptom severity, inattention/hyperactivity, emotional problems, depressive symptoms, parental styles and stress, and demographic factors. Results: An artificial neural network analysis using a self-organizing map, a statistical technique used to cluster large datasets, revealed 3 distinct anxiety profiles: low (n = 114, 5% ASD), moderate (n = 70, 64% ASD) and high (n = 78, 96% ASD) anxiety. A recursive feature elimination analysis revealed that depression and peer problems contributed the most to differences between the anxiety profiles. Difficulties with peers in individuals with ASD who experience anxiety may be related to challenges with social competence and this may heighten depressive symptoms. Conclusion: Findings highlight the importance of assessing depressive symptoms in children and adolescents with ASD who experience anxiety. Identifying anxiety profiles among children and adolescents with ASD may prove beneficial in clinical practice by facilitating the development of tailored interventions that aid in managing anxiety and depressive symptoms. Furthermore, strengthening social communication skills may improve peer relationships and could aid in managing depressive symptoms among children and adolescents with ASD who experience anxiety. [ABSTRACT FROM AUTHOR]

The increased occurrence of extreme weather events worldwide has changed the way power system reliability is determined. The effect of high intensity weather events has catastrophic effects on power system operation, and the determination of its effect is a very important and timely requirement. The conventional reliability evaluation methods used in power systems require knowledge of historical datasets, which may not be available in the case of an extreme event as these events have a low probability of occurrence. Hence, new methods to quantify power system resilience are needed. This paper uses a self-organizing map (SOM) to compute the resilience of any network using only system information, no historical data are required. The use of SOM makes the resilience quantification process very fast, and hence resilience can be evaluated in real-time during any catastrophic event, and correspondingly, action can be taken to improve the resilience of the system using available resources in the most suitable way so that the system can glide through the extreme event with the best possible performance. The paper first details the SOM-based resilience quantification method and then proposes a two-stage resilience improvement strategy using existing resources connected to the system based on the resilience value calculated in real-time during the progression of the event. The proposed quantification method and the resilience improvement strategy are tested on the IEEE 33 bus and 69 bus distribution systems. The results show that the resilience improved from 0.45 to 0.95 and 0.65 to 0.85 for the two systems, respectively, which proves the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV's yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system. [ABSTRACT FROM AUTHOR]

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21–42.59%. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis is a crucial element of intelligent education that aims to assess the proficiency of specific skills or traits in students at a refined level and provide insights into their strengths and weaknesses for personalized learning. Researchers have developed numerous cognitive diagnostic models. However, previous studies indicate that diagnostic accuracy can be significantly influenced by the appropriateness of the model and the sample size. Thus, designing a general model that can adapt to different assumptions and sample sizes remains a considerable challenge. Artificial neural networks have been proposed as a promising approach in some studies. In this paper, we propose a cognitive diagnosis model of a neural network constrained by a Q-matrix and named QNN. Specifically, we employ the Q-matrix to determine the connections between neurons and the width and depth of the neural network. Moreover, to reduce the human effort in the training algorithm, we designed a self-organizing map-based cognitive diagnosis training framework called SOM-NN, which enables the QNN to be trained unsupervised. Extensive experimental results on simulated and real datasets demonstrate that our approaches are effective in both accuracy and interpretability. Notably, under unsupervised conditions, our approach has significant advantages on small sample datasets with high levels of guessing and slipping, especially on the pattern-wise agreement rates. This work bridges the gap between psychometrics and machine learning and provides a realistic and implementable reference solution for classroom instructional assessment and the cold start of personalized and adaptive assessment systems. [ABSTRACT FROM AUTHOR]

The purpose of this research was to model areas prone to erosion in the Gol-Mehran catchment in southern Iran. For this purpose, the soil erosion map was determined using membership functions and analytic hierarchy process (AHP) determined the soil erosion map. Additionally, using the self-organizing map (SOM) and principal component analysis (PCA) methods, the most crucial parameters affecting gully erosion were extracted. Finally, soil erosion was predicted using a multilayer perceptron (MLP) and radial basis function. The results of the fuzzy AHP method with all data and the selected data with SOM and PCA demonstrated that areas located in the center of the region were prone to gully erosion. The results of this research also demonstrated that urban lands have expanded significantly, while vegetation has decreased from 1990 to 2019, which has had a significant impact on soil erosion. The results also showed that the MLP model, with R2 = 0.97, could accurately predict soil erosion. [ABSTRACT FROM AUTHOR]

Groundwater is a dynamic component of the global water cycle with important social, economic, ecological, and Earth system functions. We present a new global classification and mapping of groundwater systems, which we call groundwaterscapes, that represent predominant configurations of large‐scale groundwater system functions. We identify and map 15 groundwaterscapes which offer a new lens to conceptualize, study, model, and manage groundwater. Groundwaterscapes are derived using a novel application of sequenced self‐organizing maps that capture patterns in groundwater system functions at the grid cell level (∼10 km), including groundwater‐dependent ecosystem type and density, storage capacity, irrigation, safe drinking water access, and national governance. All large aquifer systems of the world are characterized by multiple groundwaterscapes, highlighting the pitfalls of treating these groundwater bodies as lumped systems in global assessments. We evaluate the distribution of Global Groundwater Monitoring Network wells across groundwaterscapes and find that industrial agricultural regions are disproportionately monitored, while several groundwaterscapes have next to no monitoring wells. This disparity undermines the ability to understand system dynamics across the full range of settings that characterize groundwater systems globally. We argue that groundwaterscapes offer a conceptual and spatial tool to guide model development, hypothesis testing, and future data collection initiatives to better understand groundwater's embeddedness within social‐ecological systems at the global scale. Key Points: Groundwaterscapes are presented as landscape units representing configurations of groundwater's social‐ecological and Earth system functionsA two‐stage self‐organizing map clustering method is implemented to derive 15 groundwaterscapes at the global scaleAll large aquifer systems of the world contain multiple groundwaterscapes [ABSTRACT FROM AUTHOR]

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is fundamental in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a "two-step refinement zoning method" to address this gap, first using a self-organizing feature mapping method to delineate EFZs at a township scale, and then applying a hotspot overlay analysis to refine the resulting EFZs by designating them with different grades at the village scale. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ES combinations and land use compositions. Furthermore, 5.23% of villages were identified as level I areas of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in multi-view approaches based on their ability to manage data from several sources. However, regarding unsupervised learning, most multi-view approaches are clustering algorithms suitable for analyzing vector data. Currently, only a relatively few SOM algorithms can manage multi-view dissimilarity data, despite their usefulness. This paper proposes two new families of batch SOM algorithms for multi-view dissimilarity data: multi-medoids SOM and relational SOM, both designed to give a crisp partition and learn the relevance weight for each dissimilarity matrix by optimizing an objective function, aiming to preserve the topological properties of the map data. In both families, the weight represents the relevance of each dissimilarity matrix for the learning task being computed, either locally, for each cluster, or globally, for the whole partition. The proposed algorithms were compared with already in the literature single-view SOM and set-medoids SOM for multi-view dissimilarity data. According to the experiments using 14 datasets for F-measure, NMI, Topographic Error, and Silhouette, the relevance weights of the dissimilarity matrices must be considered. In addition, the multi-medoids and relational SOM performed better than the set-medoids SOM. An application study was also carried out on a dermatology dataset, where the proposed methods have the best performance. [ABSTRACT FROM AUTHOR]

Introduction: Degeneracy in the brain-behavior code refers to the brain's ability to utilize different neural configurations to support similar functions, reflecting its adaptability and robustness. This study aims to explore degeneracy by investigating the non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC). Methods: The study analyzed RSFC data from 500 subjects to uncover the underlying neural configurations associated with various psychometric outcomes. Self-organized maps (SOM), a type of unsupervised machine learning algorithm, were employed to cluster the RSFC data. And identify distinct archetypal connectivity profiles characterized by unique within- and betweennetwork connectivity patterns. Results: The clustering analysis using SOM revealed several distinct archetypal connectivity profiles within the RSFC data. Each archetype exhibited unique connectivity patterns that correlated with various cognitive, physical, and socioemotional outcomes. Notably, the interaction between different SOM dimensions was significantly associated with specific psychometric profiles. Discussion: This study underscores the complexity of brain-behavior interactions and the brain's capacity for degeneracy, where different neural configurations can lead to similar behavioral outcomes. These findings highlight the existence of multiple brain architectures capable of producing similar behavioral outcomes, illustrating the concept of neural degeneracy, and advance our understanding of neural degeneracy and its implications for cognitive and emotional health. [ABSTRACT FROM AUTHOR]

This paper combines self-organizing mapping (SOM) and a long short-term memory network (SOM-LSTM) to construct an audio-based motor-fault diagnosis system for identifying the operating states of a rotary motor. This paper first uses an audio signal collector to measure the motor sound signal data, uses fast Fourier transform (FFT) to convert the actual measured sound–time-domain signal into a frequency-domain signal, and normalizes and calibrates the frequency-domain signal to ensure the consistency and accuracy of the signal. Secondly, the SOM is used to further analyze the characterized frequency-domain waveforms in order to reveal the intrinsic structure and pattern of the data. The LSTM network is used to process the secondary data generated via SOM. Dimensional data aggregation and the prediction of sequence data in long-term dependencies accurately identify different operating states and possible abnormal patterns. This paper also uses the experimental design of the Taguchi method to optimize the parameters of SOM-LSTM in order to increase the execution efficiency of fault diagnosis. Finally, the fault diagnosis system is applied to the real-time monitoring of the motor operation, the work of identifying the motor-fault type is performed, and tests under different loads and environments are attempted to evaluate its feasibility. The completion of this paper provides a diagnostic strategy that can be followed when it comes to motor faults. Through this fault diagnosis system, abnormal conditions in motor equipment can be detected, which can help with preventive maintenance, make work more efficient and save a lot of time and costs, and improve the industry's ability to monitor motor operation information. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is an emerging privacy-preserving technology that enables training a global model beneficial to all participants without sharing their data. However, differences in data distributions among participants may undermine the stability and accuracy of the global model. To address this challenge, recent research proposes client clustering based on data distribution similarity, generating independent models for each cluster in order to enhance FL performance. Nevertheless, due to the uncertainty of participant identities, FL struggles to rapidly and accurately determine the clusters. Most of the existing algorithms distinguish clients by iterative clustering, which not only increases the computing cost of the server but also affects the convergence speed of the federation model. To address these shortcomings, in this paper, we propose a novel clustering-based FL method, SoFL. SoFL introduces SOM networks, improves the quality of cluster data, and eliminates redundant categories through secondary clustering, encouraging more similar clients to train together. Through this mechanism, SoFL completes the clustering task in one round of communication and speeds up the convergence of federated model training. Simulation results demonstrate that SoFL accurately and swiftly adapts to determine the clusters. In different non-IID settings, SoFL's model accuracy improvements ranged from 9 to 18% compared to FedAvg and FedProx. [ABSTRACT FROM AUTHOR]

To comprehensively explore flow control method of profiled end wall for turbine stage at low Reynolds numbers, a surrogate model optimization platform including non-uniform rational B-spline surface parameterization method, support vector regression, and improved chaos particle swarm optimization algorithm is integrated. Optimization designs have been carried out for stator profiled end walls, rotor profiled end wall, and combined end walls, respectively. The results indicate that under the constraint of the output power, the application of various profiled end wall design cases all can effectively improve the aerodynamic performance of the turbine stage. By organizing the flow field of downstream rotor, the profiled end wall of stator can significantly affect the stage efficiency. The flow control benefits of the profiled end wall of the rotor is from the obstruction of the cross migration of the pressure side leg of the horseshoe vortex. The application of profiled end wall on stator has the most practical engineering value. Self-organizing maps and Shapley methods are used to explore potential correlation information of aerodynamic parameters and summarize design experience. The sensitive design variables of profiled end walls are extracted. Based on the local controllability of NURBS surfaces, the regions that affect the stage efficiency are mainly concentrated in the middle of the stator passage, near the stator trailing edge and near the rotor leading edge. The regions with a significant impact on the output power of the turbine stage are near the trailing edge of the rotor and stator. The corresponding design rules of end walls modeling are summarized. [ABSTRACT FROM AUTHOR]

This article explores the efficiency of various clustering methods for image segmentation under different luminosity conditions. Image segmentation plays a crucial role in computer vision applications, and clustering algorithms are commonly used for this purpose. The search for an adaptive clustering mechanism aims to ensure the maximum symmetry of real objects with objects/segments in their digital representations. However, clustering method performances can fluctuate with varying lighting conditions during image capture. Therefore, we assess the performance of several clustering algorithms—including K-Means, K-Medoids, Fuzzy C-Means, Possibilistic C-Means, Gustafson–Kessel, Entropy-based Fuzzy, Ridler–Calvard, Kohonen Self-Organizing Maps and MeanShift—across images captured under different illumination conditions. Additionally, we develop an adaptive image segmentation system utilizing empirical data. Conducted experiments highlight varied performances among clustering methods under different luminosity conditions. This research enhances a better understanding of luminosity's impact on image segmentation and aids the method selection for diverse lighting scenarios. [ABSTRACT FROM AUTHOR]

The onset of the rainy season in Vietnam displays distinct characteristics compared to other Asian summer monsoon (ASM) regions due to its elongated shape running from south to north, coupled with its complex terrain and the influences of multiple weather systems. Determining the onset is challenging, although numerous empirical indices exist. Here, we proposed an alternative approach to determine the onset by examining the large-scale weather patterns associated with the abrupt increase in rainfall in the country. In this new approach, the evolutions of the ASM system were interpreted as consecutive occurrences of multiple weather patterns, detected by a clustering algorithm Self Organizing Maps (SOMs). Specifically, SOMs were applied on outgoing longwave radiation, 850 hPa wind, and geopotential height over the ASM regions from April to July. The primary features of these atmospheric variables were generalized to 16 patterns. A high-resolution Vietnam Gridded Precipitation (VnGP) dataset was employed to investigate the linkage between these large-scale patterns and local-scale rainfall. The large-scale patterns associated with the onset were distinguished, which are characterized by the development of westerly winds and large-scale convections. Then, the onset of summer rainfall in Vietnam is identified based on the initial appearance of wet-condition weather patterns. This approach offers several advantages: it is objective and does not require additional thresholds of rain or wind to determine the monsoon onset. It also more effectively captures the characteristics of large-scale circulation and deep convection compared to relying on predefined monsoon indices alone. Additionally, this approach helped to elucidate two types of weather patterns associated with the onset: those associated with the northeastward development of deep convection and westerly wind from the Indian Ocean to the Indochina Peninsula and activities of the Boreal Summer Intraseasonal Oscillation. [ABSTRACT FROM AUTHOR]

The objectives of this study were to examine the importance of variables such as perceived quality, low price, social interaction, consumer knowledge, and packaging on purchase intention for beer in the Brazilian market; and to identify consumer groups with a distinct profile based on their behavior concerning these important variables. We found that perceived quality, low price, and social interaction were predictors of the willingness to buy beer. We also found that consumer product knowledge and packaging moderated the effects of perceived quality and low price on purchase intention. Our findings showed that the more product knowledge a consumer possesses, the less perceived quality would impact a beer brand's purchase intention; and the greater the influence of packaging, the less impact a low price will have on the purchase intention of a beer brand. Our research also identified three distinct groups of beer consumers: Bohemians, Tasters, and NOBELs (NOt a BEer Lover). [ABSTRACT FROM AUTHOR]

This paper assesses the importance of incorporating care dimensions into activity-travel segmentation to understand daily life mobility strategies. The data came from six neighbourhoods in Concepción, Chile, and included detailed information on activity-travel time use and interaction and classification schemes to identify care purposes. The study uses self-organizing maps to build incremental behavioural segments from weekly mobility and time use variables, adding care activities to assess their role in this segmentation. The results identify groups with a higher burden on care than others, emphasizing the role of transport mode and time use patterns. The result remarks caregiving activities hidden within other categories, identifying groups of caregivers, including domestic workers and women who work and have intense accompanying activities with children. The results highlight the differences between mobility patterns between different segments to make more invisible care and other related activities disproportionately performed by groups of women. [ABSTRACT FROM AUTHOR]

Ecosystems offer natural resources and habitats for humans, serving as the foundation for human social development. Taking the Tianshan Mountains as the study area, this study investigated the changing trends, hot spots, and driving factors of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ), in the Tianshan region, from 1990 to 2020. To determine the trade-offs and synergies between the ESs, we employed the Spearman correlation coefficient, geographically weighted regression, the self-organizing map (SOM), and other methods. Five main results were obtained. (1) There were similar spatial distribution patterns for WY, HQ, CS, and SC, with high-value areas mainly concentrated in grassland zones, forest zones, river valleys, and the intermountain basins of the mountain range, while regions with low value were clustered in desert zones and snow/ice zones. (2) According to the hotspot analysis, areas with relatively strong ES provisioning for WY, HQ, CS, and SC, were primarily concentrated in the BoroHoro Ula Mountains and Yilianhabierga Mountains. In contrast, areas with relatively weak ES provisioning were mainly located in the Turpan Basin. (3) Precipitation was the primary explanatory factor for WY. Soil type, potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) were the primary explanatory factors for HQ. Soil type and NDVI were the primary explanatory factors for CS. PET was the primary explanatory factor for SC. (4) There were synergistic relationships between the WY, HQ, CS, and SC, with the strongest synergies found between CS–HQ, WY–HQ, and WY–SC. (5) Six ES bundles were identified through the SOM method, with their composition varying at different spatial scales, indicating the need for different ES management priorities in different regions. Our analysis of ESs, from various perspectives, offers insights to aid sustainable ecosystem management and conservation efforts in the Tianshan region and other major economic areas worldwide. [ABSTRACT FROM AUTHOR]

Mechanisms underlying grapevine responses to water(-deficient) stress (WS) are crucial for viticulture amid escalating climate change challenges. Reanalysis of previous transcriptome data uncovered disparities among isohydric and anisohydric grapevine cultivars in managing water scarcity. By using a self-organizing map (SOM) transcriptome portrayal, we elucidate specific gene expression trajectories, shedding light on the dynamic interplay of transcriptional programs as stress duration progresses. Functional annotation reveals key pathways involved in drought response, pinpointing potential targets for enhancing drought resilience in grapevine cultivation. Our results indicate distinct gene expression responses, with the isohydric cultivar favoring plant growth and possibly stilbenoid synthesis, while the anisohydric cultivar engages more in stress response and water management mechanisms. Notably, prolonged WS leads to converging stress responses in both cultivars, particularly through the activation of chaperones for stress mitigation. These findings underscore the importance of understanding cultivar-specific WS responses to develop sustainable viticultural strategies in the face of changing climate. [ABSTRACT FROM AUTHOR]

Background and objective: Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns. Methodology: This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method. Results: The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies–Bouldin clustering index and a ∼60% increase in the classification specificity score. Conclusion: A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015–2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015–2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015–2022 ozone increase in Guangzhou. [ABSTRACT FROM AUTHOR]

In this paper, we proposed VGASOM, a neural network approach for community detection. Community detection refers to discovering similar nodes in a graph that form a community having similar features or attributes as opposed to nodes from other communities. The proposed approach combines the capabilities of auto-encoder neural networks, specifically a Variational graph auto-encoder (VGAE) with self-organizing maps (SOM) clustering. VGAEs have achieved great success in learning the latent representation of graphs and therefore encoding them into lower-dimensional embeddings. The self-organizing map based on competitive learning is used to find communities in the graphs' embeddings obtained by the VGAE model which further reduces its dimensionality and divides the input space into clusters that correspond to the communities in the graph. We conducted experiments to evaluate our model compared to several baseline models, our model shows promising results for the community detection task. It outperforms the state-of-the-art methods by 3.29% in terms of the accuracy and 9% in terms of F1 metric. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) is a promising technology for controlling traffic on roads. Nowadays, heavy traffic is a major issue, and the presence of attackers exacerbates the situation. The most important challenge in VANET is its security from malicious vehicles. In order to defend against distributed denial of service DDoS attacks, we propose a comprehensive verification header format bandwidth detection (CVHB) in VANET. The behavior of a DDoS attack is unknown for all the other normal nodes in network. The header format of packer contains all the information of nodes that are actively participating in routing. The attacker infection probability measured by and or (10> 0.9). If both the parameters are high means attacker presence confirm in network. The CVHB scheme checks the packet header format of the attacker node, and only the attacker is one of the nodes whose sequence number is frequently changing. So, CVHB blocks the flooding of unwanted packets that consume the limited bandwidth of a wireless link and identify packets that contain no useful information. To measure the performance of the network, the basic performance metrics that are used are dropping percentage, packet delivery ratio (PDR), throughput and delay. The result of CVHB is showing improvement as compared to multilayer distributed self-organizing maps (MSOM) in VANET. [ABSTRACT FROM AUTHOR]

Abstract Understanding local patterns of rainfall variability is of great concern in East Africa, where agricultural productivity is dominantly rainfall dependent. However, East African rainfall climatology is influenced by numerous drivers operating at multiple scales, and local patterns of variability are not adequately understood. Here, we show evidence of substantial variability of local rainfall patterns between 1981 and 2021 at the national and county level in Kenya, East Africa. Results show anomalous patterns of both wetting and drying in both the long and short rainy seasons, with evidence of increased frequency of extreme wet and dry events through time. Observations also indicate that seasonal and intraseasonal variability increased significantly after 2013, coincident with diminished coherence between ENSO (El Nino Southern Oscillation) and rainfall. Increasing frequency and magnitude of rainfall variability suggests increasing need for local-level climate change adaptation strategies.

The degradation of sediments in urban environments worldwide is driven by population growth, urbanization, and industrialization, highlighting the need for thorough quality assessment and management strategies. As a result of these anthropogenic activities, benzene, toluene, ethylbenzene, xylenes, and styrene (BTEXS) are persistently released into the environment, polluting sediment. This study employed self-organizing maps (SOMs), positive matrix factorization (PMF), and Monte Carlo simulation of source-oriented health risks to comprehensively investigate sediment in an urban shallow lake in a mid-sized city in central Serbia. The results indicated a mean ∑BTEXS concentration of 225 µg/kg, with toluene as the dominant congener, followed by m,p-xylene, benzene, ethylbenzene, o-xylene, and styrene. Three contamination sources were identified: waste solvents and plastic waste due to intensive recreational activities, and vehicle exhaust from heavy traffic surrounding the lake. Both non-carcinogenic and carcinogenic health risks were below the permissible limits. However, children were more susceptible to health risks. Benzene from vehicle exhaust is the most responsible for non-carcinogenic and carcinogenic health risks in both population groups. The results of this study can help researchers to find a suitable perspective on the dynamics and impacts of BTEXS in lake sediments. [ABSTRACT FROM AUTHOR]

Featured Application: Increased cognitive demands during motor tasks emerged as a critical discriminator of functional patterns among older adults, shedding light on the intricate interplay between cognitive function and motor performance in fall prevention strategies in community-dwelling older adults. A wide range of outcomes makes identifying clinical and functional features distinguishing older persons who fall from non-fallers challenging, especially for professionals with less clinical experience. Thus, this study aimed to map a high-dimensional and complex clinical and functional dataset and determine which outcomes better discriminate older adults with and without self-reported falls. For this, clinical, functional, and cognitive outcomes of 60 community-dwelling older adults classified as fallers and non-fallers were selected based on self-report of a single fall in the last 12 months. An unsupervised intelligent algorithm (Self-Organizing Maps—SOM) was used to cluster and topographically represent the data studied. The SOM model mapped and identified two different groups (topographic error: 0.00; sensitivity: 0.77; precision: 0.42; accuracy: 0.53; F1-score: 0.55) based on self-report of a single fall. We concluded that although two distinct groups were mapped and clustered by the SOM, participants were not necessarily fallers or non-fallers. The increased cost of cognitive demands regarding a motor task (Timed Up and Go Test) and the effect of the Trail Making Test (TMT) Part B regarding TMT Part A could discriminate the functional and cognitive patterns in community-dwelling older adults. Therefore, in clinical practice, identifying patterns involving the interaction between cognition and motor skills, even in once-only faller older adults, can be an efficient approach to assessment and, consequently, to compound intervention programs to prevent falls in this population. [ABSTRACT FROM AUTHOR]

It is challenging to interpret hydrogeochemical datasets with complex natural and anthropogenic genesis in intensive industrial areas. This paper elucidates the hydrogeochemical characteristics and pollution sources of groundwater in an industrial park, East China, combining the self-organizing map (SOM), hydrochemical graphs, and correlation analysis. The results show that the total dissolved solids of groundwater range from 73.45 to 997.92 mg/L and can be regarded as freshwater. The pH varies greatly from 6.44 to 9.90, most of samples belonging to weakly acidic-weakly alkaline. The groundwater can be classified into five clusters by SOM, representing the non- or least-polluted groundwater (cluster D), high salt groundwater (cluster A), high NH4+-N and HCO3− groundwater (cluster B), high Fe and Mn groundwater (cluster C), and high pH groundwater (cluster E), which were contaminated by industrial salts, historical agriculture activity, industrial reducing substances, and industrial alkali, respectively. The natural evolution of groundwater (cluster D) in the study area is mainly controlled by mineral weathering/dissolution. The contributions of calcite, dolomite, gypsum, halite, and silicate mineral to groundwater solute are 55.8–66.3%, 15.1–18.0%, 9.0–10.7%, 2.5–10.1%, and 2.3–9.4%, respectively, based on the mass conservation. The contaminated groundwaters (all other clusters except for cluster D) have different hydrochemical characteristics associated with the pollution sources. In addition, the relatively reductive environment in quaternary flu-lacustrine sediments favored the formation of high level of Fe, Mn, and NH4+-N in groundwater. This study provides a new insight into the characteristic contaminants and their distributions in groundwater and the associated pollution sources based on the large datasets in an intensive industrial area. The data evaluation methods and results of this study could be useful to the groundwater usage management and pollution control in this area and other industrial areas. [ABSTRACT FROM AUTHOR]

This study verifies the capability of evolutionary circulation to characterize the movement of rain belt based on Self-Organizing Map (SOM) and further explores the interdecadal variation of rain belt's northward advancement during flood season in eastern China. The results show that the evolving path of circulation patterns can effectively describe the trajectory of rain belt's movement with a significant correlation coefficient of 0.91. By employing the time required for circulation evolution to represent the velocity of rain belt's movement, the northward velocity of rain belt emerges interdecadal shifts around the mid-late1970s and the late 1990s, which is affected by the sea surface temperature anomaly of Pacific Decadal Oscillation (PDO) pattern. During negative PDO phases, the warming of western North Pacific and the cooling in equatorial middle-east Pacific trigger the northward westerly jet and Western Pacific Subtropical High, leading to the northward vertical upward motion. This configuration encourages the circulation pattern corresponding to Yangtze-Huaihe River basin rain belt to transfer to that representing more precipitation in North and Northeast China, resulting in the accelerated northward advance of rain belt. Conversely, during positive PDO phases, the moisture brought by westward subtropical high and upward motion driven by the southward upper jet stream increase the likelihood of persistent precipitation in Yangtze-Huaihe River Basin, slowing the advancement of rain belt. During the phase transition of PDO, the northward leap of circulation systems in July has been proved to play a pivotal role in the interdecadal variation of rain belt's overall advancing velocity. [ABSTRACT FROM AUTHOR]

Anthropogenic activities predominantly affect environmental Pb pollution, especially during waste lead-acid battery (LAB) recycling operations. In this study, the presence of Pb and nine other potentially toxic elements (PTEs) in the soil at an abandoned LAB recycling site was investigated. The focus was on spatial and vertical distributions and potential health issues related to PTEs. Average concentrations of Cd, As, Hg, Pb, Al, Zn, Cu, and Sb were elevated at all investigated soil depths, whereas the concentrations of Zn, Cu, and Sb were significant only on the soil surface. Positive matrix factorization, correlation and cluster analyses, as well as self-organizing maps, identified four primary pollution sources: recycling activities (Cd, Hg, Pb, and Sb), mixed anthropogenic sources (Zn and Cu), the soil parent material (As, Cr, and Ni), and surface runoff combined with sand application (Al and pH). While the non-carcinogenic risk results revealed a negligible risk for adults, the hazard index (HI) values for children were greater than one in 26% of the samples. For adults and children, the total carcinogenic risk (TCR) values were acceptable for 98% and 94% of the samples, respectively. Geospatial analysis identified the main hotspot in the battery disposal area. Source-specific non-carcinogenic and carcinogenic risks were most influenced by recycling activities. Monte Carlo simulation (MCS) of total HI for children showed that the risk value exceeded the threshold level (HI > 1) at the 10th percentile, whereas the maximum value of total HI for adults was 0.2. Regarding carcinogenic risk, the TCR values at the 95th percentile of all four sources for adults and children were below the limit value (1 × 10−4), indicating a low probability of cancer development. [ABSTRACT FROM AUTHOR]

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies. [ABSTRACT FROM AUTHOR]

Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making. [ABSTRACT FROM AUTHOR]

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV's position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV's 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure. [ABSTRACT FROM AUTHOR]

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. [ABSTRACT FROM AUTHOR]

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as 'mild', 'moderate', or 'severe' based on neurological impairment. Yet, stroke survivors argue that the classification of 'mild' does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment profiles among stroke survivors classified as 'mild'. We used survivors of mild stroke' data from the START longitudinal stroke cohort (n = 73), with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work, and social adjustment over 12 months. Given the multisource, multigranular, and unlabeled nature of the data, we utilized a structure-adapting, unsupervised machine learning approach, the growing self-organizing map (GSOM) algorithm, to generate distinct clinical profiles. These diverse impairment profiles revealed that survivors of mild stroke experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified 'mild' stroke. This emphasizes the necessity of creating a holistic and more comprehensive representation of survivors of mild stroke' needs over the first year after stroke to improve rehabilitation and poststroke care. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the most important aspects of medical image processing. PET and PET/CT scanning are well-known imaging tools in medical image processing, which are used for figuring out the inner anatomy of the internal organs in a noninvasive manner. In this study, an unsupervised artificial neural network Hierarchical Self-Organizing Feature Map (HSOFM) is presented to isolate and extract abnormal regions in PET and PET/CT images. This method was implemented on three, images in which the tumor size decreases with time after receiving treatment, and then many mathematical models were tested to elect the proper one that more adequate to simulate the change behavior in order to predict the change in the tumor size in the coming time. The results indicate that this method was adequate to extract tumor areas with percentage the percent relative difference between the results of the proposed method and the radiologist work ranged from 0.00% to 33.766% for PET images and ranged from 5.809% to 34.459% for PET/CT scan images. The behavior of the abnormality size change with time was correctly simulated using exponential and linear mathematical models and the predicated tumor size values in the coming times were calculated the tumor size with over time for PET and PET/CT images. The resultant values of the coefficient of Determination (R2) for the exponential model were 0.93, while for the linear model equation was 0.89. This is evidence of the success of the regression model in predicting future results. [ABSTRACT FROM AUTHOR]

The traceability of groundwater nitrate pollution is crucial for controlling and managing polluted groundwater. This study integrates hydrochemistry, nitrate isotope (δ15N-NO3− and δ18O-NO3−), and self-organizing map (SOM) and end-member mixing (EMMTE) models to identify the sources and quantify the contributions of nitrate pollution to groundwater in an intensive agricultural region in the Sha River Basin in southwestern Henan Province. The results indicate that the NO3−-N concentration in 74% (n = 39) of the groundwater samples exceeded the WHO standard of 10 mg/L. According to the results of EMMTE modeling, soil nitrogen (68.4%) was the main source of nitrate in Cluster-1, followed by manure and sewage (16.5%), chemical fertilizer (11.9%) and atmospheric deposition (3.3%). In Cluster-2, soil nitrogen (60.1%) was the main source of nitrate, with a significant increase in the contribution of manure and sewage (35.5%). The considerable contributions of soil nitrogen may be attributed to the high nitrogen fertilizer usage that accumulated in the soil in this traditional agricultural area. Moreover, it is apparent that most Cluster-2 sampling sites with high contributions of manure and sewage are located around residential land. Therefore, the arbitrary discharge and leaching of domestic sewage may be responsible for these results. Therefore, this study provides useful assistance for the continuous management and pollution control of groundwater in the Sha River Basin. [ABSTRACT FROM AUTHOR]

As neuroscience states, the ability of our brain to process visual information is similar to that of neurons. Our cognitive ability carries out this process using the functional magnetic resonance imaging (fMRI) profiles that it collects during the presentation of visual stimuli around us. However, this process poses its own set of challenges due to complexities like structure and noise levels. To address these challenges, high-quality computational approaches have been executed. This work proposes a hybrid computational approach, that combines feature extraction and conditional generative adversarial network techniques. This research suggests the use of the hybrid form incorporating Kohonen Network and Information Maximizing Generative Adversarial Networks (KNInfoGAN) to map the fMRI data to visual stimuli. For feature extraction, the Kohonen network is used, and the visual stimuli are reconstructed using the InfoGAN approach. A series of experiments were conducted on dual synthetic fMRI datasets using a KNInfoGAN model, and the results were obtained with an accuracy rate of 99% for handwritten numbers and 87% for handwritten English characters, approximately. An accuracy rate of 73% has been obtained upon utilizing the ImageNet database to examine the accomplishments of the suggested approach for highly complex natural stimulus. The suggested technique in this study outperforms the previously existing techniques in image reconstruction, with enhanced quality and a lower computational cost. The same has been verified based on the comparative examination of a wide range of tests conducted in this study. [ABSTRACT FROM AUTHOR]

The quantitative relationship between Mesoscale Convective Systems (MCSs) and floods over East Asia has not been established. In this study, MCSs are clustered into four types with Self‐Organizing Map approach. Floods in June‐August of 2000–2021 are linked with different types of MCS by automated algorithms we constructed. We find that among the major floods (potential flood peak periods), 91% (87%) are related to MCS, 65% (78%) are dominated by MCS, and 38% (20%) are dominated by multi‐types of MCS. Types 1 and 2 MCS have higher flood‐inducing efficiencies than common MCS (Type‐4). Type‐1 MCS, characterized by the least number (2% of the total number), the largest precipitation volume, longest lifetime, slowest moving, strongest precipitation, can most efficiently produce floods. Type‐2 MCS, characterized by the second largest precipitation volume, more numerous than Type‐1 particularly over land, can induce floods not only relatively efficiently but also more frequently than Type‐1. Plain Language Summary: Mesoscale Convective Systems (MCS) can cause large flood events but how they work in East Asia at climatic scale remains unexplored. In this study, we tracked MCS over the time period of 2000–2021 and grouped them into four types using the Self‐Organizing Map approach. We connected summer floods with the four types of MCS using automated algorithms. We found that most large flood events and potential flood peak periods are dominated by MCS. Type‐1 MCS, which features large land‐rain area, longest lifetime, slowest movement and strongest precipitation, is most efficient in inducing floods. A Longer lifetime not only contributes to greater total precipitation volume but also increases the overlap area, thereby enhancing the precipitation volume per area. The slow movement further increases the overlap area. The high overlap area helps reduce the proportion of precipitation absorbed by the soil. Type‐2 MCS, which features with the second largest precipitation volume, occurs more frequently than Type‐1 especially over land, and can cause floods more frequently than Type‐1. These findings can enhance our understanding of flood formation over East Asia. Key Points: Among the major floods (potential flood peak periods) occurred in 2000–2021 summer, 91% (87%) are related to Mesoscale Convective Systems (MCS), and 66% (78%) are dominated by MCSType‐1 MCS is the most efficient among the four types in causing floods for its strongest precipitation rate, longest lifetime and slowest movementLong lifetime and slow movement increase the overlap area, boosting precipitation per area and reducing the proportion of rainfall absorbed by soil [ABSTRACT FROM AUTHOR]

In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self‐organizing maps applied to the Climate Prediction Center global precipitation data set. This reveals distinct subregions, shaped by the inhomogeneous geographic features of the NAM area, with distinct extreme precipitation character and drivers. Next, defining EPEs as days when subregion‐mean precipitation exceeds the 95th percentile of rainy days, five synoptic features and one mesoscale feature are investigated as potential drivers of EPEs. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining unclassified. This analysis shows the dominant role of Gulf of California moisture surges, mesoscale convective systems and frontal systems in generating NAM extreme precipitation. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. The findings demonstrate that the co‐occurrence of multiple features does not necessarily enhance the EPE probability. Plain Language Summary: Extreme precipitation is of great importance for both geophysical and socioeconomic reasons. This study first identifies geographic subregions of the North American Monsoon within which extreme precipitation exhibits distinct characteristics. The extreme precipitation events in each subregion are then associated with at least one candidate atmospheric driver, revealing the dominant precipitation drivers among subregions. Depending on the subregions and driver, precipitation rates may increase or decrease when two candidate factors co‐occur. Several such double driver combinations are examined. Key Points: Seven subregions of the North American Monsoon with distinct precipitation characters are identifiedGulf of California moisture surges, mesoscale convective systems and frontal systems are primary drivers of extreme precipitationA probability analysis reveals the nonlinear and non‐additive characters of the interactions among extreme precipitation event drivers [ABSTRACT FROM AUTHOR]