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Copyright of Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi is the property of Itobiad: Journal of the Human & Social Science Researches and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A procedure combined SOM was developed to identify new refrigeration system faults. • Defrosting fault was due to abnormal action of gas-powered suction stop valve. • The diagnostic accuracy for DLL, DML, and DHL were 93.8 %, 91.2 % and 88.6 %, respectively. • Resolution of defrosting issues resulted in up to 18.3 % energy consumption reduction. Refrigeration systems in large cold stores frequently operate suboptimally due to component faults, leading to significant energy wastage and high carbon emissions. This study introduces a novel procedure that leverages data mining to automatically analyze and identify faults, thereby enhancing the intelligence of refrigeration equipment. The research focused on abnormal suction temperatures of compressors during the defrosting of air coolers in a large cold store. Through theoretical analysis and key data acquisition, the root cause of defrosting issues was traced to the abnormal operation of gas-powered suction stop valves, causing leakage of high-pressure hot gas. Clustering methods, Self-Organizing Maps (SOM), were utilized to classify system states and achieved high accuracy rates of 88.6 % to 93.8 % for the three fault modes during the defrosting process, respectively. The resolution of defrosting faults resulted in an energy consumption reduction of up to 18.3 %, aligning with global sustainability initiatives. The study also evaluated the carbon emission reduction, providing a comprehensive approach to improving the efficiency and environmental impact of cold store operations. [ABSTRACT FROM AUTHOR]

Smart meter data provide an in-depth perspective on household energy usage. This research leverages on such data to enhance demand response (DR) programs through a novel application of ensemble clustering. Despite its promising capabilities, our literature review identified a notable under-utilization of ensemble clustering in this domain. To address this shortcoming, we applied an advanced ensemble clustering method and compared its performance with traditional algorithms, namely, K-Means++, fuzzy K-Means, Hierarchical Agglomerative Clustering, Spectral Clustering, Gaussian Mixture Models (GMMs), BIRCH, and Self-Organizing Maps (SOMs), across a dataset of 5567 households for a range of cluster counts from three to nine. The performance of these algorithms was assessed using an extensive set of evaluation metrics, including the Silhouette Score, the Davies–Bouldin Score, the Calinski–Harabasz Score, and the Dunn Index. Notably, while ensemble clustering often ranked among the top performers, it did not consistently surpass all individual algorithms, indicating its potential for further optimization. Unlike approaches that seek the algorithmically optimal number of clusters, our method proposes a practical six-cluster solution designed to meet the operational needs of utility providers. For this case, the best performing algorithm according to the evaluation metrics was ensemble clustering. This study is further enhanced by integrating Explainable AI (xAI) techniques, which improve the interpretability and transparency of our clustering results. [ABSTRACT FROM AUTHOR]

Social media platforms generate a large amount of content users create, which requires methods for suggesting relevant content. In current empirical research introduces an approach to improving social content recommendations using the Deep Self Organizing Map (DSOM) algorithm and hierarchical clustering. The study uses a database that includes user posts, comments, likes, shared content, and user profiles. The DSOM algorithm analyzes and organizes the data, while hierarchical clustering enhances performance. By utilizing the insights gathered from this social content database, we can significantly improve the accuracy and relevance of recommendations. This improvement will ultimately increase user engagement and satisfaction on social media platforms. The findings of this research have implications for recommendation systems on social media platforms and strategies related to promoting content and analyzing user behavior.

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of “unknown common” compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure. [ABSTRACT FROM AUTHOR]

The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and sequence similarity networks have been used to display and analyze sequences of protein families. These methods aim to shed light on key computational biology problems such as sequence classification and functional inference. Here, we present a new methodology, AlignScape, based on self-organizing maps. AlignScape is applied to three large families of proteins: the kinases and GPCRs from human, and bacterial T6SS proteins. AlignScape provides a map of the similarity landscape and a tree representation of multiple sequence alignments These representations are useful to display, cluster, and classify sequences as well as identify functional trends. The efficient GPU implementation of AlignScape allows the analysis of large MSAs in a few minutes. Furthermore, we show how the AlignScape analysis of proteins belonging to the T6SS complex can be used to predict coevolving partners., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Filella-Merce, Mallet, Durand, Nilges, Bouvier and Pellarin.)

The majority of the KP-based copy-move forgery (CMF) detection techniques have a high computational cost due to their large feature descriptor sets and numerous KPs. Furthermore, the accuracy of the results may be impacted by the identified KPs not spreading over all regions of the image and the classical clustering approaches not efficiently classifying the feature space into cluster space. This article therefore attempts to employ both KAZE & fast hessian matrix (FHM) techniques for identifying KPs that spread over the entire image region, SURF for evaluating feature descriptors, Network based Dimensionality Reduction (NDR) for reducing the dimension of each feature descriptor and Self-Organizing Map (SOM) for clustering the feature vectors for avoiding sub-optimal clusters. It portrays the superior performances of the proposed forgery detection scheme on a standard image database “MICC-F220” and medical records like fundas images. [ABSTRACT FROM AUTHOR]

Modern wide field radio surveys typically detect millions of objects. Manual determination of the morphologies is impractical for such a large number of radio sources. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm that projects a many-dimensional dataset onto a two- or three-dimensional lattice of neurons. This dimensionality reduction allows the user to visualize common features of the data better and develop algorithms for classifying objects that are not otherwise possible with large datasets. To this aim, we use the PINK implementation of a SOM. PINK incorporates rotation and flipping invariance so that the SOM algorithm may be applied to astronomical images. In this cookbook we provide instructions for working with PINK, including preprocessing the input images, training the model, and offering lessons learned through experimentation. The problem of imbalanced classes can be improved by careful selection of the training sample and increasing the number of neurons in the SOM (chosen by the user). Because PINK is not scale-invariant, structure can be smeared in the neurons. This can also be improved by increasing the number of neurons in the SOM. We also introduce pyink , a Python package used to read and write PINK binary files, assist in common preprocessing operations, perform standard analyses, visualize the SOM and preprocessed images, and create image-based annotations using a graphical interface. A tutorial is also provided to guide the user through the entire process. We present an application of PINK to VLA Sky Survey (VLASS) images. We demonstrate that the PINK is generally able to group VLASS sources with similar morphology together. We use the results of PINK to estimate the probability that a given source in the VLASS QuickLook Catalogue is actually due to sidelobe contamination. [ABSTRACT FROM AUTHOR]

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

As the need for effective and efficient power systems grows, so does the need for more advanced methods to find and study partial discharge (PD) sources, which can be a sign that high-voltage equipment's protection is breaking down. This study suggests using Support Vector Machines (SVMs), Random Forests (RF), Self-Organizing Maps (SOM), and Back-propagation Neural Networks (BPNN) together to make PD cause discovery more accurate and useful. SVMs are very good at sorting complicated patterns into groups. They provide a strong framework for telling PD events apart from background noise. By mixing several decision trees, RF, which is known for its ability to learn in groups, helps make generalization better. A strong autonomous learning method called SOM helps group and show how PD sources are spread out in space. A popular artificial neural network design called BPNN is used because it can model complicated relationships and change to trends that don't follow a straight line. Putting these methods together in a way that makes the best use of their individual strengths and weaknesses creates a complete and reliable framework for PD investigations. Using the combined knowledge of these advanced machine learning algorithms, the suggested method can correctly find and spot PD sources, which will eventually make high-voltage systems work better and be more reliable. Comprehensive models and testing validations show that the proposed method works, showing that it could be used in real life for power system upkeep and diagnosis. This study is a big step forward in improving the most up-to-date methods for finding and detecting PD. It will help build stronger and longer-lasting power grids. [ABSTRACT FROM AUTHOR]

The paper is devoted to testing Kohonen self‐organising maps, with elliptic Fourier coefficients as quantitative variables, for the task of morphological classification of fragmented and non‐standardised ceramics. The advantage of the methodology used is its ability to account for the systematic statistical relationships inherent in the dataset, build models of varying degrees of generalisation and visualise multivariate data. The approbation of the method was carried out on materials from the Hlyboke Ozero‐2 settlement in Eastern Ukraine. The results are compared with the results obtained using principal component analysis + k‐means clustering. [ABSTRACT FROM AUTHOR]

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

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]

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]

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]

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]

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]

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]

Context: Ubiquitin-like with PHD and RING finger domain containing protein 1 (UHRF1) is responsible for preserving the stability of genomic methylation through the recruitment of DNA methyltransferase 1 (DNMT1). However, the interaction between Developmental pluripotency associated 3 (DPPA3) and the pre-PHD-PHD (PPHD) domain of UHRF1 hinders the nuclear localization of UHRF1. This disruption has implications for potential cancer treatment strategies. Drugs that mimic the binding pattern between DPPA3 and PPHD could offer a promising approach to cancer treatment. Our study reveals that DPPA3 undergoes dissociation from the C-terminal through three different modes of helix unfolding. Furthermore, we have identified key residue pairs involved in this dissociation process and potential drug-targeting residues. These findings offer valuable insights into the dissociation mechanism of DPPA3 from PPHD and have the potential to inform the design of novel drugs targeting UHRF1 for cancer therapy. Methods: To comprehend the dissociation process and binding patterns of PPHD-DPPA3, we employed enhanced sampling techniques, including steered molecular dynamics (SMD) and conventional molecular dynamics (cMD). Additionally, we utilized self-organizing maps (SOM) and time-resolved force distribution analysis (TRFDA) methodologies. The Gromacs software was used for performing molecular dynamics simulations, and the AMBER FF14SB force field was applied to the protein. [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]

The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses. [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]

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]

The article examines the impact of artificial intelligence (AI) technologies on global sustainable development. Artificial intelligence affects all three pillars of sustainable development: economic, social, and environmental. Based on the generalization of academic works and authoritative expert assessments, it is shown that this impact is ambiguous. By increasing technological capabilities and enhancing the efficiency of business, public administration, and the provision of administrative and social services, AI creates a number of socio-economic problems, primarily in the labor market, when hundreds of professions are discredited and may disappear. It has been confirmed that almost all sectors of the economy, including education and medicine, are subject to the large-scale impact of AI. AI is able to optimize the use of resources and increase energy efficiency, reducing waste, thus affecting the environmental pillar of sustainable development. The purpose of the article is a systematic study of the intellectual and technological landscape of the global economy with a cross-country analysis of its key indicators using the Kohonen algorithm. The author has positioned artificial intelligence in the technological paradigm of the twenty-first century. If scientific progress in materials science, energy, and mathematical computing led to the digital transformation with the emergence of Industry 4.0, then in synergy with bio- and quantum technologies, AI will form Industry 5.0, i.e., essentially a smart economy, through a technological explosion. To recreate the current global intellectual and technological landscape, the study used the Kohonen algorithm with the Deductor Studio package to analyze 128 countries by 5 indicators. The modeling allowed us to group them into 5 clusters, which makes a real comparative analysis possible. Similar modeling with the implementation of the AI indicator (number of robots per 10,000 population) for 18 countries allowed them to be grouped into 3 clusters according to the level of readiness of governments and society to interact with AI. [ABSTRACT FROM AUTHOR]

Clustered extreme precipitation (CEP) events draw worldwide attention due to their non-neglectable impacts on socio-economic activities. This study focuses on the typical circulations associated with CEP events in Northern China and investigates the role of hydro-thermal processes over Eurasia. Based on the Self-organizing map method, there are two types of circulations closely related to the occurrence of CEP events over Northern China. One of them features a short wave and a subtropical high. Another shows a northern cyclone and high pressure around the sea of Okhotsk. The anomalous soil moisture (SM) over the Eastern Caspian Sea (ECS) and Northern Tibet Plateau (NTP) at the quasi-biweekly time scale dominates the typical circulations and CEP events over Northern China, rather than local forcing. On the one hand, decreased SM over the ECS induces the eastward movements of short-wave disturbances, along with the westward extension of the strengthened Western Pacific Subtropical High, favoring the increased precipitation over Northern China. On the other hand, increased SM over the NTP contributes to the meridional circulation and enhanced long-wave ridge, coupled with the northern cyclone at the lower level, finally leading to increases in precipitation over Northern China. The results may be useful for the predictions of CEP events in Northern China. [ABSTRACT FROM AUTHOR]