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Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices. [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]

Clarifying the soil nutrient dynamics caused by forest type variations in the coastal region helps scientifically to apply fertilizer to forest plantations and enhance the carbon (C) sink capacity. Pure forests of Ligustrum and Metasequoia, as well as their mixed forests, in a coastal region of China were investigated by collecting 0–20 and 20–40 cm soil samples and analyzing their differences in bulk density, water content, pH, soil organic matter (SOM), ammonium (NH4+–N), nitrate (NO3––N) and total nitrogen (TN), available phosphorus (AP) and potassium (AK), microbial biomass C (MBC) and N (MBN), and enzyme activity. The results demonstrated that different forest types had no significant (p ≥ 0.05) effect on 0–20 cm soil bulk density, water content, pH, NH4+–N, and SOM. However, the surface soil NO3––N, TN, AP, and AK contents as well as enzyme activity changed significantly (p < 0.05), in which the soil AK content of the ligustrum × metasequoia mixed forest was 47.5% and 65.5% higher than that of the ligustrum and metasequoia pure forest, respectively. The mixed forest soil had the highest MBN content, which was significantly (p < 0.05) 25.1% higher than that in the pure metasequoia forest. Meanwhile, soil phosphatase activities in ligustrum and metasequoia pure forests were significantly (p < 0.05) lower than those in the mixed forests by 17.4% and 43.1%, respectively. However, soil NO3––N and AP contents in the metasequoia pure forest were significantly (p < 0.05) higher than those in the ligustrum pure forest and mixed forests. Soil MBC content and reductase NO3––N activity were significantly (p < 0.05) higher in ligustrum pure forest than in metasequoia pure forest and mixed forests. In addition, the results of two-way ANOVA showed that there were no significant (p ≥ 0.05) differences in nutrient contents (e.g., NH4+–N, AP, AK, and SOM) in different soil layers (0–20 and 20–40 cm) within the same forest type, except for NO3––N. However, forest types had a significant (p < 0.05) impact on NO3––N and AP contents in 20–40 cm soil layer. Combining the two factors of forest stand and soil layer, there was a significant (p < 0.05) interaction effect for their soil NH4+–N, NO3––N, AP, and AK contents. In conclusion, significant (p < 0.05) differences were observed in nutrient contents in 0–20 cm soil layer from different forest types, with soil fertility indices inmixed forests generally higher than in pure forests. Therefore, establishing mixed forests in coastal saline region is recommended to retain soil fertility and to enhance the C sink capacity of forestry. [ABSTRACT FROM AUTHOR]

Vibration monitoring and control have been the main concern of machining industries. Traditional monitoring processes have several drawbacks such as; higher computational time, manual feature detection and requirement of supervision. To overcome these difficulties, this study proposes a hybrid approach based on Self-Organizing Maps (SOM) and multi-layer perceptron – back propagation neural network (MLP-BPNN) using sound signals for monitoring self-induced tool vibration (chatter) and metal removal rate (MRR) in milling operation. Initially, acquired vibration signals are decomposed for extracting desired machining data. Further, SOM has been applied on the reconstructed signals for data mapping and automatic feature selection. Selected features have been used as an input in MLP-BPNN training for the development of prediction models of machining quality and MRR. Finally, it has been observed that the proposed data-driven methodology can be well adapted for the automatic feature selection and can predict machining quality and MRR with nearly 98% accuracy. [ABSTRACT FROM AUTHOR]

Understanding the stability of soil organic matter (SOM) is essential for making accurate predictions regarding carbon release rates. However, there is limited information on the role of chemical composition of dissolved organic matter (DOM) in SOM stability. To address this gap, the peatland soil profile in the discontinuous frozen soil region of Northeast China was selected as the focus of this research, and a comprehensive analysis was conducted on the differences between the molecular composition of DOM and the stability of SOM. The results indicate a significant carbon accumulation phenomenon near the permafrost table. Through analyses using TG-50, δ13C, and δ15N, it was determined that SOM near the permafrost table exhibits high stability, whereas SOM within the permafrost layer demonstrates poor stability. Investigations utilizing UV-vis, 3D-EEM, FT-IR, and 1H-NMR technologies revealed that DOM near the permafrost table is of high quality and highly aromatic. Furthermore, compared to near the permafrost table, humic acid materials in the permafrost layer decreased by 17%, while protein materials increased by 17%. These findings offer a novel perspective on the understanding of SOM stability in peatland soil profiles within discontinuous permafrost regions. [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]

Abstract Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices.

Abstract The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies MBSD and DCSD exhibited high similarity and achieved a maximum effective porosity (PHIE) value of ≥ 15%, indicating good reservoir rock typing (RRT) features. The density-based spatial clustering of applications with noise (DBSCAN) showed minimum outliers through meta cluster attributes and confirmed the reliability of the generated cluster results. Shapley Additive Explanations (SHAP) model identified PHIE as the most significant parameter and was beneficial in identifying payable and non-payable clustering zones. Additionally, this strategy highlights the importance of unsupervised AI in managing profitable cluster distribution across various geological formations, going beyond simple reservoir characterization.

Current methodologies of genome‐wide single‐nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well‐studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non‐model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self‐organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM‐derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open‐source application programmed in Python3 and with a user‐friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals. [ABSTRACT FROM AUTHOR]

Urban sprawl leads to the degradation of aquatic environments and, consequently, to the destruction of biodiversity. With the aim of highlighting the distribution profile of benthic macroinvertebrates in the city of Yaoundé and its surroundings according to the level of degradation, this study was carried out in seven rivers. A total of 144 taxa of benthic macroinvertebrates, belonging to 74 families, 15 orders, five classes, and three phyla, were collected from seven rivers in urban, peri-urban, and forest environments on Yaoundé and its surroundings. The self-organizing map (SOM) analysis tool was used to group the collected taxa from all stations into three clusters or affinity cores. The indicator value analysis (IndVal) method was employed to determine, based on their ecological preferences, which organisms were most likely to belong to each group. Out of the 144 collected taxa, only 44 were indicated to represent the three different groups. Thus, three communities were defined: the Hydropsyche community, with Hydropsyche sp. as the predominant taxon in Group III, characterizing well-oxygenated and low-mineralized stations; the Hydrocyrius community, where the species Hydrocyrius sp. predominates in Group I, describing stations with low oxygenation and moderate mineralization; and the Lumbriculidae community, where Lumbriculidae is the taxon associated with environments with high mineralization and critical oxygenation. These two methods contribute to the biomonitoring of tropical aquatic environments, firstly by grouping organisms by affinity and then identifying those that reflect the environment conditions. This facilitates the detection of changes in the quality of hydrosystems and guides management and conservation efforts. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture. Compared with traditional chemical methods, the hyperspectral estimation is superior in rapidly estimating SOM content. Soil grain size affects soil spectral reflectance, thereby affecting the accuracy of hyperspectral estimation. However, the appropriate soil grain size for the hyperspectral analysis is nearly unknown. This study propose a best hyperspectral estimation method for determining SOM content of farmland soil in the Ibinur Lake Irrigation Area (ILIA) of the northwest arid zones of China. The original spectral reflectance of the 20-mesh (0.85 mm) and 60-mesh (0.25 mm) sieved soil were obtained, and the feature wavebands were selected using five types of spectral transformations. Then, hyperspectral estimation models were constructed based on the partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) models. Results show that the SOM content had relatively higher correlation coefficient with spectral reflectance of the 0.85 mm sieved soil than that of the 0.25 mm sieved soil. The transformation of original spectral reflectance of soil effectively enhanced the spectral characteristics related to SOM content. Soil grain size obviously affected spectral reflectance and the accuracy of hyperspectral estimation models. The overall stability and estimation accuracy of RF model was significantly higher compared with the PLSR, SVM, and XGBoost. Finally, the RF model combined with the root mean first-order differentiation (RMSFD) of spectral reflectance of the 0.85 mm sieved soil (R2 = 0.82, RMSE = 2.37, RPD = 2.27) was identified as the best method for estimating SOM content of farmland soil in the ILIA. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms--it helps the soil's biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is both cost and timeefficient. Because of its composition and biophysical features, SOM has a distinct spectral reflectance; this relationship has been effectively exploited to estimate and predict SOM. The purpose of this study is to investigate the link between SOM concentration and laboratory-based soil spectral reflectance in the Emakhosaneni subplace of uThukela District, KwaZulu-Natal, South Africa. We collected 13 random soil samples from each of the four major land use types (agricultural, residential, eroded land, and rangeland), totalling 52 samples. To assess the SOM content, we oven-dried the materials overnight (105 ℃), crushed them, sieved them (1 mm), and analysed them using the Walkley-Black method. The spectral reflectance of the soil samples was then acquired using the Analytical Spectral Device (ASD) FieldSpec3 and pre-processed for noise reduction before analysis. We found that the area's average SOM content was 2.19%, with agricultural land having the greatest average SOM content (2.98%), followed by rangeland (2.46%), residential (1.77%), and eroded land area (1.56%). However, the average reflectance of the spectra was higher on eroded areas and decreased to a minimum on agricultural areas. This is attributed to the relationship between soil colour and SOM. Correlation analysis demonstrated a moderately negative relationship between SOM concentration and spectral reflectance over the whole spectral range covered (400-2400 nm). Our partial least squares regression analysis revealed that pre-processed spectra data models (FDT; R2 = 0.46 and SG; R2 = 0.34) performed better than raw data models, in both calibration and validation data sets, respectively. Despite the influence of noise on raw data model performance, they modestly predicted SOM content, particularly on the validation data set. However, the developed model cannot be used due to the very low coefficient of determination. Our results highlight the importance of spectroscopy to assess SOM content, and further research can be carried out on a larger scale. [ABSTRACT FROM AUTHOR]

The manufacturing industry serves as the mainstay of the modern economic system and enhancing the resilience of the manufacturing supply chain (SC) is crucial in the face of global instability. Numerous research frameworks have been devised to evaluate supply chain resilience（SCR）. However, these frameworks primarily concentrate on the internal SC performance, such as supplier on-time delivery rate and inventory turnover. This paper establishes a four-dimensional evaluation index system for assessing China's manufacturing SCR at the macro level, including risk-resistance, recovery, responsiveness, and learning capabilities. The Self-Organizing Map (SOM) model is used for clustering analysis. Then the empirical analysis is conducted by using data from Chinese manufacturing listed companies between 2011 and 2020. The findings reveal that: ①The overall resilience of China's manufacturing SC is suboptimal: only 3.23% exhibit a superior resilience level, followed by a high resilience level of 6.45%, a medium resilience level of 54.84%, and the low resilience level reaching 35.48%. ②Significant regional disparities exist in the resilience of China's manufacturing SC. This paper proposes several recommendations for enhancing China's manufacturing SCR. [ABSTRACT FROM AUTHOR]

Studying the impact of urbanization on changes in the soil organic matter (SOM) content of farmland plays an important role in determining the influence mechanism of urbanization regarding regional environmental change. Taking the farmland in Yushu City, northeast China, as the research area, in May 2019, 68,393 sample plots (each plot: 60 m × 60 m) were set up in farmland and sampled to measure the SOM content of each plot while combining image data from the same period in the study area (resolution: 60 m). This investigation was based on 17 levels divided by the size of areas occupied by residences, using residential areas as the center and a radius of 60 m. Through a gradually buffered extrapolation method combined with mathematical functions, the influence of rural urbanization on the changes in SOM content was revealed. These results showed that the slope of the linear function between the SOM content and the residential area level was greater than zero and that with the continuous advancement of urbanization, the SOM content had an increasing trend. When urbanization advanced to the stage of larger cities, large-scale mechanized production led to land degradation. When urbanization advanced to the stage of towns, intensive cultivation was beneficial for land restoration. The findings of this study provide a reference basis for future studies of the relationship between rural urbanization and agricultural mechanization around the world. [ABSTRACT FROM AUTHOR]

Soil compaction has been recognized as a form of soil degradation that mostly deteriorates agricultural soil health worldwide from various climatic conditions and soil management. Heavy compaction can occur in agricultural soils due to intensive tillage, where its effects on soil properties have been the focus of many studies. The present study was undertaken to assess whether soil compaction occurs in agricultural soils adopting no-tillage systems and how the link between soil compaction and chemical-biological characteristics could be. A field investigation was carried out in two different locations that have different soil types, Entisol in Maitara Island and Ultisol in Halmahera Island, North Maluku Province. Sixteen undisturbed and disturbed soil samples were collected from each location grown with cassava and taken to the laboratory for analysis. Soil components observed included soil texture, bulk density, particle density, total porosity, soil permeability, soil organic matter (SOM), total nitrogen (N), phosphorous (P) availability, and phosphate-solubilizing bacteria (PSB). The findings revealed linear relationships between soil compaction and sand fraction, SOM, PSB, and available P, but none were found in soil total N. These results confirmed the importance of soil compaction management in food crop agricultural systems, particularly when soil carbon is low, to promote soil health. As the inconsistent correlation of soil compaction and chemical-biological properties was found in these two soil types, further investigation is necessary.

The stability of Wireless Sensor Networks (WSNs) is a crucial requirement in real-time applications such as military, defense, and other surveillance systems. Clustering in WSNs is one of the most predominant techniques, offering benefits such as minimizing communication time, optimizing energy utilization, lengthening the network lifespan, and ensuring network stability. Moreover, achieving the balance between energy consumption and maintaining network stability is significantly influenced by the cluster size. This research paper addresses the challenges associated with clustered-based routing and cluster formation paradigm, introducing a novel algorithm employing the principle of Artificial Neural Networks (ANN). In particular, the proposed algorithm integrates a hybrid strategy of Self Organizing Map (SOM) and K-mean clustering to form energy-efficient clusters, even ensuring the energy distribution evenly among them. The validity of the proposed algorithm has been confirmed through experimental analysis conducted using MATLAB. The simulation results demonstrate that the hybrid combination of SOM and K-means clustering is highly efficient in minimizing energy consumption and maintaining network stability. The findings also reveal an average of 80% stable network lifetime and achieve a packet reception ratio (PRR) of 98% which is much higher than two other protocols i.e. LEACH and MODLEACH protocols.

Model projections predict tropical forests will experience longer periods of drought and more intense precipitation cycles under a changing climate. Such transitions have implications for structure-function relationships within microbial communities. We examine how throughfall exclusion might reshape prokaryotic and fungal communities across four lowland forests in Panama with a wide variation in mean annual precipitation and soil fertility. Four sites were established across a 1000 mm span in Mean Annual Precipitation (MAP: 2335–3421 mm). We expected microbial communities at sites with lower MAP to be less sensitive to throughfall exclusion than sites with higher MAP and fungal communities to be more resistant to disturbance than prokaryotes. At each location, partial throughfall exclusion structures were established over 10 × 10 m plots to reduce direct precipitation input. After short-term (∼3–9 months) throughfall exclusion, prokaryotic communities showed no change in composition. However, prolonged (12–18 months) throughfall exclusion resulted in divergent prokaryotic community responses, reflecting MAP and soil fertility. We observed the emergence of a “drought microbiome” within infertile sites, whereby the community structure of the experimental throughfall exclusion plots at the lower MAP sites diverged from their respective control sites and converged towards overlapping assemblages. Furthermore, under throughfall exclusion, taxa increasing in relative abundance at the wettest site reflected that endemic to control plots at the lowest MAP site, suggesting a shift toward communities with life-history traits selected for under a lower MAP. By contrast, fungal community composition across sites was resilient to throughfall exclusion; however, biomass diverged in response to throughfall exclusion, increasing at two sites while decreasing in the other two. Broadly, our results suggest that microbial communities' sensitivity to frequent drying and rewetting periods in tropical forest soils will depend on climate history and soil fertility, with infertile sites likely to respond readily to changes in precipitation.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data. [ABSTRACT FROM AUTHOR]

• This study proposed an effective scheme of PM 2.5 and O 3 control strategy. • This multi-technique-integrated scheme markedly improves air quality. • PM 2.5 and O 3 control calls for spatial-temporally differentiated strategies. • Emphasis should be put on areas with sensitive pollutant-precursor responses. Previous air pollution control strategies didn't pay enough attention to regional collaboration and the spatial response sensitivities, resulting in limited control effects in China. This study proposed an effective PM 2.5 and O 3 control strategy scheme with the integration of Self-Organizing Map (SOM), Genetic Algorithm (GA) and WRF-CAMx, emphasizing regional collaborative control and the strengthening of control in sensitive areas. This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management, with SOM identifying the collaborative subregions, GA providing the optimized subregion-level priority of precursor emission reductions, and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions. With Beijing-Tianjin-Hebei and the surrounding area (BTHSA, "2 + 26" cities) as the case study area, the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM 2.5 -dominant seasons, and strengthen VOCs reductions but moderate NOx reductions in O 3 -dominant season. The spatiotemporally differentiated control strategy, without additional emission reduction burdens than the 14th Five-Year Plan proposed, reduced the average annual PM 2.5 and MDA8 O 3 concentrations in 28 cities by 3.2%–8.2% and 3.9%–9.7% respectively in comparison with non-differential control strategies, with the most prominent optimization effects occurring in the heavily polluted seasons (6.9%–18.0% for PM 2.5 and 3.3%–14.2% for MDA8 O 3 , respectively). This study proposed an effective scheme for the collaborative control of PM 2.5 and O 3 in BTHSA, and shows important methodological implications for other regions suffering from similar air quality problems. [Display omitted] [ABSTRACT FROM AUTHOR]

Currently, revealing soil management strategies that store the maximum atmospheric CO2 in the soil is a major issue. This is best explored by investigating long-term experiments, like the Skierniewice (Poland) field trial, established in 1921 on sandy loam Luvisol. In this trial, the variants analyzed included control (CON), manure (MAN), legumes (LEG), and manure + legumes (MAN + LEG). Soil samples from the A horizon were analyzed for total organic carbon (TOC), carbon content of humic acids (HA), fulvic acids (FA), and humin (HUM), as well as for spectroscopic properties of bulk soil and isolated HUM. Compared to the control, all other treatments caused an increase in TOC, while the application of manure resulted in an increase in the amount of HUM. Legume application caused an increase in UV-Vis absorbance and fluorescence emission. Thermochemolysis and gas chromatography/mass spectrometry showed that HUM was enriched in carbohydrates in almost all pairs of soil and HUM. Compared to the CON, the largest proportion of carbohydrate in HUM was found in MAN + LEG. Different long-term soil management strategies not only altered TOC, but also, surprisingly, the chemical composition of HUM, which is considered to be particularly stable and a long-term sink of atmospheric carbon. [ABSTRACT FROM AUTHOR]

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The ISO/IEC MPEG and ITU-T Video Coding Experts Group formed the Joint Collaborative Team on 3D Video Coding to develop the new 3D video coding standard 3D High-Efficiency Video Coding (3D-HEVC) which is based on MultiView plus Depth map data format which associates a depth map to each texture frame. The depth map intra-prediction provides the conventional HEVC intra modes and adds the depth modeling modes as new coding tools for depth map intra-predictions, which enables high coding efficiency, but also results in a very high encoding complexity and excludes the 3D-HEVC from the real-time applications. In this paper, we proposed a fast mode decision algorithm for depth map intra-coding based on efficient features. We used constructed training data and a self-organizing algorithm in the test model to skip unnecessary depth map intra modes for each prediction unit. The experimental results show that the proposed model brings a good complexity reduction with negligible loss of rate-distortion performance. [ABSTRACT FROM AUTHOR]

Doped‐metal oxide‐based memristors, with the potential for improved switching performance and capability for multi‐bit information storage, are attractive candidates in the implementation of artificial neural network (ANN) hardware systems. However, performance and process considerations such as switching behavior and complementary‐metal‐oxide‐semiconductor (CMOS) process compatibility remain a challenge. This study shows that amorphous Zr‐doped HfO2 (HZO) memristors fabricated via a co‐sputtering approach improve the switching performance by providing a controllable knob to modulate defects in the switching layer. At the same time, it satisfies the CMOS process compatibility requirements for industry adoption. HZO memristors with optimized stoichiometry exhibit 30% reduced switching voltages and 50% faster switching as compared to control HfO2 memristors. Concurrently, this study shows that high linearity analog states tuning is achievable via a programming scheme that utilizes voltage pulses with increasing amplitudes. This study further shows via simulation evaluation that HZO memristors implemented in a self‐organizing‐map (SOM) network for Fashion MNIST database classification, achieve an accuracy of 92% with short training cycles. The results thus pave a potential pathway for further development of CMOS process compatible HZO memristors for use in future storage and computing applications. [ABSTRACT FROM AUTHOR]

Today, automatic emotion recognition in speech is one of the most important areas of research in signal processing. Identifying emotional content in Arabic speech is regarded as a very challenging and intricate task due to several obstacles, such as the wide range of cultures and dialects, the influence of cultural factors on emotional expression, and the scarcity of available datasets. This study used a variety of artificial intelligence models, including Xgboost, Adaboost, KNN, DT, and SOM, and a deep-learning model named SERDNN. ANAD was employed as a training dataset, which contains three emotions, "angry", "happy", and "surprised", with 844 features. This study aimed to present a more efficient and accurate technique for recognizing emotions in Arabic speech. Precision, accuracy, recall, and F1-score metrics were utilized to evaluate the effectiveness of the proposed techniques. The results showed that the Xgboost, SOM, and KNN classifiers achieved superior performance in recognizing emotions in Arabic speech. The SERDNN deep learning model outperformed the other techniques, achieving the highest accuracy of 97.40% with a loss rate of 0.1457. Therefore, it can be relied upon and deployed to recognize emotions in Arabic speech. [ABSTRACT FROM AUTHOR]

Soil compaction has been recognized as a form of soil degradation that mostly deteriorates agricultural soil health worldwide from various climatic conditions and soil management. Heavy compaction can occur in agricultural soils due to intensive tillage, where its effects on soil properties have been the focus of many studies. The present study was undertaken to assess whether soil compaction occurs in agricultural soils adopting no-tillage systems and how the link between soil compaction and chemical-biological characteristics could be. A field investigation was carried out in two different locations that have different soil types, Entisol in Maitara Island and Ultisol in Halmahera Island, North Maluku Province. Sixteen undisturbed and disturbed soil samples were collected from each location grown with cassava and taken to the laboratory for analysis. Soil components observed included soil texture, bulk density, particle density, total porosity, soil permeability, soil organic matter (SOM), total nitrogen (N), phosphorus (P) availability, and phosphatesolubilizing bacteria (PSB). The findings revealed linear relationships between soil compaction and sand fraction, SOM, PSB, and available P, but none were found in soil total N. These results confirmed the importance of soil compaction management in food crop agricultural systems, particularly when soil carbon is low, to promote soil health. As the inconsistent correlation of soil compaction and chemical-biological properties was found in these two soil types, further investigation is necessary. [ABSTRACT FROM AUTHOR]

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets. [ABSTRACT FROM AUTHOR]

The aim of this study is to verify the stability of the profitability–liquidity relationship over time, as well as to determine this relationship in terms of its level and structure. In this context, three main research questions were formulated. First, is the profitability–liquidity relationship stable in times of crisis? Second, what is the profitability of companies with high and low liquidity? Third, what is the liquidity of companies with high and low profitability? This study uses a self-organizing map (SOM), a data visualization technique that is a type of artificial neural network trained in an unsupervised manner. A dataset covering the period from 2019 to 2021, consisting of 300 Polish companies from the wholesale and retail sectors, was used. The main results of this study indicate that: (1) companies with a balanced profitability–liquidity relationship in the pre-crisis period (2019) maintained this relationship in the crisis (2020) and post-crisis periods (2021); (2) companies in the clusters with the relatively highest and lowest profitability have the relatively lowest and moderate liquidity both before and after the crisis period; (3) the majority of companies during non-crisis periods demonstrate that profitability is not reliant on liquidity, suggesting an absence of a clear relationship; (4) in the post-crisis period, companies with the relatively lowest operating cash flow margin (OCFM) exhibited the relatively highest net profit margin (NPM) and other profitability ratios, as opposed to the pre-crisis and crisis periods. This study fills the gap resulting from the incomplete—most of all static—understanding of the relationship between profitability and liquidity. Moreover, this study employs a self-organizing map (SOM) which has not been used in the literature regarding the research area undertaken. [ABSTRACT FROM AUTHOR]

O objetivo deste artigo é identificar, com base numa teoria psicoacústica, o tipo de experiência sonora da narrativa The Midnight gospel (2020), abordando-a enquanto elemento que confere profundidade e multidimensionalidade à narrativa. Recorre-se à noção de níveis de realidade e à ideia de sonosfera para analisar o universo sonoro da série, de modo a elucidar o jogo de imersão musical, vocal e eletrônica que põe à prova a atenção da audiência. As conclusões levam a três aspectos importantes do alcance cognitivo, pedagógico e psicológico da série em análise cujo título já prediz que seus autores querem dar uma “boa notícia à meia-noite”. 1) Percebe-se que, se por um lado, a complexidade sonora mergulha o espectador no ambiente narrativo, ela também o convida a um esforço cognitivo de atenção e compreensão que o retira da condição imersiva. 2) Pedagogicamente, medita-se ouvindo música ou pronunciando sons, amplia-se a percepção fruindo variados repertórios musicais; abre-se o corpo à ampliação da consciência e à escuta do daimon. 3) a imersão musical, o retorno à protomúsica e o “efeito sereia” aparecem como estimulantes psíquicos capazes de devolver ânimo e entusiasmo para a própria existência do personagem, mas também, por um processo de identificação, da audiência.

Este artigo, como apresentamos em sua versão de apresentação oral, tem por objetivo analisar a interação entre o conceito de ponto de escuta, os ruídos e os sons ambientes. Para tanto, teremos como centro o longa-metragem Memória (Apichatpong Weerasethakul, 2021). Nos interessa demonstrar a relação possível entre a personagem principal e nós, como espectadores, pela partilha de um incerto ponto de escuta que transita entre sonoridades subjetivas e objetivas. Temos como hipótese que tal incerteza sobre o ponto de escuta, por parte da personagem e estendida aos espectadores, relativiza dicotomias da teoria do cinema sobre a localização espacial ou subjetiva dos sons. Nossa análise tem a intenção de abrir possiblidades de diferentes resultados quanto à recepção e identificação sonoras.

Durante a pandemia de coronavírus, as mídias sociais desempenharam um importante papel no combate à desinformação. Foi também nesse período que o Brasil se tornou o terceiro mercado consumidor do TikTok no mundo. Nesse panorama, destaca-se o perfil Fatos Curiosos com Antonio Miranda, criado em 2020 pelo estudante e tiktoker Antonio Miranda. Atualmente com 1,6 milhões de seguidores e 13,5 milhões de curtidas, o jovem transmite conhecimentos científicos de maneira leve e criativa em seus vídeos. Neste artigo, investiga-se como os recursos semióticos sonoros instanciados no vídeo mais curtido e comentado do perfil de Antonio contribuem para comunicar, de maneira clara e acessível, um conteúdo supostamente complexo, para fins de popularização da ciência no ambiente digital. Para tanto, o referencial teórico-metodológico proposto é o da Semiótica Social Multimodal, no que se refere às qualidades do som e da voz (VAN LEEUWEN, 1999). Os principais resultados encontrados demonstram que os recursos sonoros instanciados na vinheta contribuem para construir uma atmosfera de humor, leveza e descontração; ao passo que as qualidades da voz cumprem o papel de transmitir credibilidade, enfatizar palavras-chave e oportunizar a fluidez na compreensão da explicação proferida por Antonio Miranda.

Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.

This study investigates the patterns of extreme winds and the correlation between synoptic patterns in Türkiye throughout the winter season, using the cluster analysis technique. We utilized the k-means algorithm to detect the surface patterns of extreme winds. Additionally, we deployed the Self-Organizing Map (SOM) technique to identify clusters of geopotential height at the 500 hPa level, average temperature at the 850 hPa level, and mean sea level pressure. We adopted the dataset from the New European Wind Atlas (NEWA) project for analyzing surface-level weather conditions and the ERA5 datasets for studying upper-level weather conditions. The k-means algorithm identifies six distinct clusters when applied to the ground-level data in Türkiye. These clusters are predominantly located around the Taurus Mountain ranges, which stretch in an east-west and northeastern direction along the Black Sea coast. The formation of these clusters is controlled by the characteristics of the land and its physical features. The higher-level clusters, consisting of nine SOM nodes, are unaffected by terrain and weather systems, which are characteristic of the macro-Mediterranean climate. These clusters are detected in the Eastern Mediterranean, Black Sea, and inner Aegean areas, emphasizing the impact of topography on surface patterns. [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. [ABSTRACT FROM AUTHOR]

During the coronavirus pandemic, social media has played an important role in combating misinformation. It was also during this period that Brazil became the third TikTok consumer market in the world. In this panorama, the profile Fatos Curiosos com Antonio Miranda stands out, created in 2020 by student and tiktoker Antonio Miranda. Currently with 1.6 million followers and 13.5 million likes, the young man transmits scientific knowledge in a light and creative way in his videos. In this article, is investigated how the sound aspects instantiated in the most liked and commented video on Antonio's profile contribute to communicating, in a clear and accessible way, supposedly complex content, for the purposes of popularizing science in the digital environment. To this end, the theoretical-methodological framework proposed is that of Multimodal Social Semiotics, with regard to the qualities of sound and voice (VAN LEEUWEN, 1999). The main results found demonstrate that the sound resources instantiated in the vignette contribute to building an atmosphere of humor, lightness and relaxation; while the qualities of the voice fulfill the role of transmitting credibility, emphasizing key words and providing fluidity in understanding the explanation given by Antonio Miranda. [ABSTRACT FROM AUTHOR]

Rolling bearings typically operate under time-varying conditions, which present challenges for fault diagnosis due to the presence of modulation effect and noise component in the bearing vibration signal. This paper introduces a novel feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD), Teager-Kaiser energy operator (TKEO), and self-organizing map (SOM) to enhance the identification accuracy of bearing characteristics by removing unwanted components from the vibration signal. Firstly, CEEMD and TKEO are used to generate demodulated instantaneous energies of the raw vibration signal and each decomposed mode, which represent bearing features. Next, relevant features are extracted by computing similarity distances using the SOM network. The feature signal is then reconstructed, and its power spectrum is used to evaluate the visualization of the characteristic frequencies of rolling bearings. The efficiency of the proposed method is confirmed through the analysis of simulated and experimental vibration signals collected from two types of bearings with different geometries. The results demonstrate that the proposed method can effectively suppress noise, extract characteristics, and identify the fault condition of rolling bearings compared to previous studies. [ABSTRACT FROM AUTHOR]

The stability of Wireless Sensor Networks (WSNs) is a crucial requirement in real-time applications such as military, defense, and other surveillance systems. Clustering in WSNs is one of the most predominant techniques, offering benefits such as minimizing communication time, optimizing energy utilization, lengthening the network lifespan, and ensuring network stability. Moreover, achieving the balance between energy consumption and maintaining network stability is significantly influenced by the cluster size. This research paper addresses the challenges associated with clustered-based routing and cluster formation paradigm, introducing a novel algorithm employing the principle of Artificial Neural Networks (ANN). In particular, the proposed algorithm integrates a hybrid strategy of Self Organizing Map (SOM) and K-mean clustering to form energy-efficient clusters, even ensuring the energy distribution evenly among them. The validity of the proposed algorithm has been confirmed through experimental analysis conducted using MATLAB. The simulation results demonstrate that the hybrid combination of SOM and K-means clustering is highly efficient in minimizing energy consumption and maintaining network stability. The findings also reveal an average of 80% stable network lifetime and achieve a packet reception ratio (PRR) of 98% which is much higher than two other protocols i.e. LEACH and MODLEACH protocols. [ABSTRACT FROM AUTHOR]

Copyright of MATRIZes is the property of Universidade de Sao Paulo, Programa de Pos Graduacao em Ciencias da Comunicacao 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.)

Copyright of Epistemus. Journal of Music, Cognition & Culture Research is the property of Facultad de Psicologia Universidad Autonoma de Madrid 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.)

Technological advances in the internet of things (IoT) allowed a low-cost, yet small sensor device to operate with limited power in a dynamic harsh environment where human intervention is impossible. The wireless sensor network (WSN) is an example of the IoT in which physical devices' software and sensors can interconnect to provide application services. It is important that such applications be dependable to meet the required quality of service (QoS) and function as expected. Consequently, the multi-objective optimization (MOO) problem in WSNs aims to address the trade-off among coverage, connectivity, and network lifetime requirements. Node scheduling is one approach of many used to optimize energy in WSNs. The contribution of this work is the proposal of a self-organizing feature map (SOFM) to enhance the node scheduling in WSNs. The proposed SOFM node-scheduling algorithm aims to spatially explore the state space domain and obtain an optimal solution. In our experiment, the proposed SOFM node-scheduling algorithm is evaluated against a comparable algorithm, namely the BAT node-scheduling algorithm, via MATLAB simulator. The results showed that the SOFM node-scheduling algorithm outperformed the latter by 27% and 28% for the maximum and minimum coverage, respectively, with similar performance of 99% of connectivity and network lifetime. [ABSTRACT FROM AUTHOR]

Copyright of Revista Iberoamericana de Educación (Version impresa) is the property of Organizacion de Estados Iberoamericanos (OEI) 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.)

Synoptic fingerprinting of flood driving rainfall events over the Hyderabad city, India were assessed through an unsupervised machine learning technique—self‐organizing maps (SOM) for the period 1979–2020. Flood dates were identified using nonscientific data sources, and then large‐scale climate variables, that is, integrated vapour transport (IVT) and geopotential height at 500 hPa (Z500) along with rainfall at daily scale were considered. SOM was applied on IVT and results indicated large spatial scale‐ and season‐specific mechanisms in addition to local circulation systems—this highlights SOM's ability to cluster circulation mechanisms as per the relevance. The rainy seasons, that is, monsoon and postmonsoon showed well‐known moisture pathways. While synoptic systems can be easily linked with monsoon and postmonsoon mechanisms, it is interesting to note that the local systems exist even during the monsoon and postmonsoon season in addition to the summer season. Spatial patterns of Z500 corresponding to SOM nodes of IVT showed the presence of low‐pressure systems (LPSs), tropical cyclones and trough systems. Further analysis was performed, and a total of 35 atmospheric rivers (ARs) were identified. Plots of back trajectories for the selected AR events indicated that Arabian Sea is the main source of moisture in the monsoon season followed by Bay of Bengal for postmonsoon and tropical cyclones in summer season events. Additionally, moisture from land is also observed as another source of moisture. Back‐trajectory analysis further indicated AR‐LPS interaction during the monsoon and ARs feeding moisture to LPSs. Overall, well‐known and season‐specific synoptic systems that brought significant rainfall to the study region were identified by the SOM. The proposed framework is adaptable for different locations, and as floods in cities across the globe are on the rise, these findings will have a prominent role in urban flood forecasting and management. [ABSTRACT FROM AUTHOR]