Unknown Dataset Research Articles

Neural network-enhanced internal leakage analysis for efficient fault detection in heavy machinery hydraulic actuator cylinders

This work presents a method for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced into the hydraulic cylinder actuator, and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information such as location, height, and width. After the neural network has undergone the training process, it is deployed for the purpose of categorizing fault levels into three distinct categories: “a healthy system,”“a system with a low fault,” and “a system with a high fault.” The proposed technique utilizes pressure difference signals from the cylinder chambers and extracts features from the peak signals to reduce dimensionality. The extracted features are then used for supervised training of an artificial neural network using a feed-forward algorithm. The trained network is capable of predicting the leakage class of unknown datasets. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This study introduces a notably efficient approach, predicated on artificial neural networks, for the identification of internal leakage faults in hydraulic cylinders, with specific emphasis on faults arising from component wear and seal damage. The practical value of this research lies in its potential to significantly improve the reliability and efficiency of hydraulic systems used in heavy machinery. By utilizing neural networks for internal leakage detection, this research addresses a critical issue that can lead to costly downtime and maintenance in industrial operations.

Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations

Rationale and ObjectivesThe aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions. Materials and MethodsWe analyzed 124 histologically confirmed lesions in 114 patients who underwent breast MR with BI-RADS 4 or 5 assessment. The dataset comprised 10 imaging parameters and one clinical observation. Using the IDTs method (algorithm C5.0) combined with the data balancing method SMOTE (Synthetic Minority Oversampling Technique) and a corresponding new method called LCC (Leveling of Cases per Class), we developed corresponding 3-class classification models (Benign, B3, or Malignant). The training set used for classification model development consists of 112 cases with 11 variables, and the model’s performance was assessed using 10-fold Cross-Validation and Leave One Out methods (utilizing the training set) and the Use Test Set method (testing on an unknown (for the models) dataset of 12 cases with 12 variables). ResultsThis preliminary study demonstrates the potential for IDTs to accurately distinguish between benign, B3 and Malignant lesions based on extracted data from breast MRI exams with a high classification accuracy (88.70%), mean sensitivity of 97.18% and specificity of 98.59% achieved by the optimal classification model, derived from the combination of the IDTs method and the LCC data balancing method. ConclusionThe present study represents a pioneering approach for three-category classification of breast lesions in MRI images using Artificial Intelligence (AI) which can be utilized as second-opinion tool in daily clinical practice by radiologists.

Prediction of Snatch and Clean and Jerk Performance From Physical Performance Measures in Elite Male Weightlifters.

Sandau, I and Kipp, K. Prediction of snatch and clean and jerk performance from physical performance measures in elite male weightlifters. J Strength Cond Res XX(X): 000-000, 2024-This study aimed to build a valid model to predict maximal weightlifting competition performance using ordinary least squares linear regression (OLR) and penalized (Ridge) linear regression (penLR) in 29 elite male weightlifters. One repetition maximum (1RM) or 3RM test results of assistant exercises were used as predictors. Maximal performance data of competition and assistant exercises were collected during a macrocycle in preparation for a competition. One repetition maximum snatch pull, 3RM back squat, 1RM overhead press, and body mass were used to predict the 1RM snatch; and 1RM clean pull, 3RM front squat, 1RM overhead press, and body mass were used to predict the 1RM clean and jerk. Model validation was performed using cross-validation (CV) and external validation (EV; random unknown dataset) for the coefficient of determination and root mean square error (RMSE). Results revealed that penLR models present more plausible output in the relative importance of highly correlated predictors. Of note, the 1RM snatch pull is the most relevant predictor for the 1RM snatch, whereas the 1RM clean pull and 3RM front squat are the most relevant predictors for the 1RM clean and jerk. Validation-based absolute predictive error (RMSE) ranged between ≈ 3-9 kg for the 1RM snatch and ≈ 3-7 kg for the 1RM clean and jerk, depending on the model (OLR vs. penLR) and validation procedure (CV vs. EV). In conclusion, penLR models should be used over OLR models to analyze highly correlated predictors because of more plausible model coefficients and smaller predictive errors.

Crop yield estimation uncertainties at the regional scale for Saxony, Germany

AbstractIn times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early as possible in the event of a drought is crucial for decision‐makers in politics, government, and business. The aim of this study was to provide precise yield predictions at agricultural regions as early as possible with a minimum amount of weather data. Random forest models were used for this purpose. Although more than 290,000 datasets were available for analysis, all models tended to be heavily overfitting, which can be explained by the strong fragmentation of the input data by crop, region, and prediction time. The models reacted very differently to unknown datasets. It was found that the regionally trained models achieved lower (≥10%) relative root mean square errors (RRMSEs) than the supra‐regionally trained models. Rapeseed and barley achieved good predictions. Wheat had good potential, too. Corn, potatoes, and sugar beet achieved often too high RRMSEs. The results showed that targeted model selection for each region and an extension of the training time series could enable very good regional yield forecasts for rapeseed and cereals in the future.

Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting

Improving the accuracy of electric load forecasting is critical for grid stability, industrial production, and residents' daily lives. Traditional short-term load forecasting methods often struggle to fully capture the long-term dependencies and deep-seated features in unknown datasets, thus limiting their generalization ability. In this paper, we propose an algorithm for short-term power load forecasting based on the stacking integration algorithm of Convolutional Neural Network-Bidirectional Long Short-Term Neural Network-Attention Mechanism (CNN-BiLSTM-Attention) with Extreme Gradient Tree (XGBoost). First, an adaptive hierarchical clustering algorithm (AHC) selects a dataset with similar day characteristics. Then, combined with influencing factors, the Stacking integrated algorithm based on CNN-BiLSTM-Attention and XGBoost is employed for forecasting short-term load data. Finally, the integrated algorithm model was applied to the multi-feature load dataset in the Quanzhou area from 2016 to 2018. Comparative analysis showed that MAPE could be reduced by 5.88–69.40 % in the four selected typical days compared to the comparative algorithm, significantly improving load forecasting accuracy.

A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network.

Despite various measures across different engineering and social systems, network robustness remains crucial for resisting random faults and malicious attacks. In this study, robustness refers to the ability of a network to maintain its functionality after a part of the network has failed. Existing methods assess network robustness using attack simulations, spectral measures, or deep neural networks (DNNs), which return a single metric as a result. Evaluating network robustness is technically challenging, while evaluating a single metric is practically insufficient. This article proposes a multitask analysis system based on the graph isomorphism network (GIN) model, abbreviated as GIN-MAS. First, a destruction-based robustness metric is formulated using the destruction threshold of the examined network. A multitask learning approach is taken to learn the network robustness metrics, including connectivity robustness, controllability robustness, destruction threshold, and the maximum number of connected components. Then, a five-layer GIN is constructed for evaluating the aforementioned four robustness metrics simultaneously. Finally, extensive experimental studies reveal that 1) GIN-MAS outperforms nine other methods, including three state-of-the-art convolutional neural network (CNN)-based robustness evaluators, with lower prediction errors for both known and unknown datasets from various directed and undirected, synthetic, and real-world networks; 2) the multitask learning scheme is not only capable of handling multiple tasks simultaneously but more importantly it enables the parameter and knowledge sharing across tasks, thus preventing overfitting and enhancing the performances; and 3) GIN-MAS performs multitasks significantly faster than other single-task evaluators. The excellent performance of GIN-MAS suggests that more powerful DNNs have great potentials for analyzing more complicated and comprehensive robustness evaluation tasks.

Graph recurrent neural networks-integrated real-time prediction of key displacements for fire-induced collapse early warning of steel frames

This study proposes the FRAME-Net, which can extract spatial-temporal features of discrete structures like planar steel frames, for early warning systems of fire-induced building collapse. FRAME-Net, based on graph neural networks and recurrent neural networks, seamlessly integrates feature extraction techniques, advanced graph convolution operations, and an enhanced graph long short-term memory mechanism, resolving the critical issue that the actual state of burning buildings, e.g., fire scenario, load case, material properties, etc., cannot be identified accurately and rapidly. The temperatures of steel components and easy-to-measure displacements are inputs, and hard-to-measure displacements at the top and interior of steel frames under fire can be predicted in real time. Numerical examples indicate that the trained agent accurately predicts the hard-to-measure displacements for the trained steel frame under completely unknown datasets, showcasing its potential in real-world unknown fire scenarios. Besides, the trained intelligent agent can be directly applied to untrained steel frames with different topological forms without re-training, which significantly reduces computational costs and is beneficial for swift emergency responses. Subsequently, satisfactory results predicted by the trained agent without re-training for the steel frame under the real fire test further underscores the promising generalization capability of the trained agent to adapt to unknown fire scenarios in real-world steel frames. Finally, the feasibility of the proposed framework to facilitate the implementation of the early-warning systems of fire-induced collapse is proved by comparing the predicted and actual remaining building collapse time, thereby assisting in reducing casualties during the rescue process and contributing to smart firefighting advancements.

Exploring Static–Dynamic ID Matching and Temporal Static ID Inconsistency for Generalizable Deepfake Detection

Identity‐based Deepfake detection methods have the potential to improve the generalization, robustness, and interpretability of the model. However, current identity‐based methods either require a reference or can only be used to detect face replacement but not face reenactment. In this paper, we propose a novel Deepfake video detection approach based on identity anomalies. We observe two types of identity anomalies: the inconsistency between clip‐level static ID (facial appearance) and clip‐level dynamic ID (facial behavior) and the temporal inconsistency of image‐level static IDs. Since these two types of anomalies can be detected through self‐consistency and do not depend on the manipulation type, our method is a reference‐free and manipulation‐independent approach. Specifically, our detection network consists of two branches: the static–dynamic ID discrepancy detection branch for the inconsistency between dynamic and static ID and the temporal static ID anomaly detection branch for the temporal anomaly of static ID. We combine the outputs of the two branches by weighted averaging to obtain the final detection result. We also designed two loss functions: the static–dynamic ID matching loss and the dynamic ID constraint loss, to enhance the representation and discriminability of dynamic ID. We conduct experiments on four benchmark datasets and compare our method with the state‐of‐the‐art methods. Results show that our method can detect not only face replacement but also face reenactment, and also has better detection performance over the state‐of‐the‐art methods on unknown datasets. It also has superior robustness against compression. Identity‐based features provide a good explanation of the detection results.

Robust Vascular Segmentation for Raw Complex Images of Laser Speckle Contrast Based on Weakly Supervised Learning.

Laser speckle contrast imaging (LSCI) is widely used for in vivo real-time detection and analysis of local blood flow microcirculation due to its non-invasive ability and excellent spatial and temporal resolution. However, vascular segmentation of LSCI images still faces a lot of difficulties due to numerous specific noises caused by the complexity of blood microcirculation's structure and irregular vascular aberrations in diseased regions. In addition, the difficulties of LSCI image data annotation have hindered the application of deep learning methods based on supervised learning in the field of LSCI image vascular segmentation. To tackle these difficulties, we propose a robust weakly supervised learning method, which selects the threshold combinations and processing flows instead of labor-intensive annotation work to construct the ground truth of the dataset, and design a deep neural network, FURNet, based on UNet++ and ResNeXt. The model obtained from training achieves high-quality vascular segmentation and captures multi-scene vascular features on both constructed and unknown datasets with good generalization. Furthermore, we intravital verified the availability of this method on a tumor before and after embolization treatment. This work provides a new approach for realizing LSCI vascular segmentation and also makes a new application-level advance in the field of artificial intelligence-assisted disease diagnosis.

Classification of Datasets Based on Combination Algorithm of Clustering and Neural Network

How to get the needed information from the data accurately and effectively for analysis is a hot research topic nowadays. Accurate classification of data is the basis for smooth data analysis. In order to classify data effectively, scholars have proposed some classification algorithms, and the most frequently mentioned one is k-means algorithm. However, in previous studies, scholars have directly determined the number of classes to be classified for the data set to be classified. Therefore, in this paper, a combinatorial algorithm is proposed to improve the classification of data with unknown group classes. The clustering algorithm and neural network are also combined to improve classification accuracy. The main elements of the algorithm proposed in this paper are as follows. First, one-third of a set of unknown group class data is selected as the sample data. In order to accurately assess the characteristics of a set of anonymous group class data, it is important first to choose a sample of the data. A sample of one-third of the total data set should be sufficient to provide a reliable representation of the entire population. This sample should be selected at random in order to ensure that the results of the assessment are as accurate as possible. The number of classes is determined by using hierarchical clustering method on the sample data. Then, the non-hierarchical clustering k-means method is used to classify the sample data. Finally, the classification results are trained as the training items of the neural network, and then the model generated after the training is used to classify the overall data. This paper selects three datasets with different kinds, different numbers of variables, and different amounts of data for the experiments and analysis. This paper presents a comprehensive analysis of three distinct datasets. Each dataset has its own unique characteristics, such as its type, the number of variables, and the amount of data contained therein. By leveraging the properties of these datasets, the experiments, and analysis conducted in this paper will provide valuable insights into the data structures and trends contained within. Furthermore, the results from this analysis will serve as a foundation for further research and experimentation. The experimental results show that the combination of clustering algorithm and neural network algorithm will help to improve the accuracy of data classification and identification effectively. This research provides a new way to accurately and effectively perform data classification. Keywords: hierarchical clustering; k-means; neural network; data classification.

DL-PPI: a method on prediction of sequenced protein–protein interaction based on deep learning

PurposeSequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets.ResultsIn this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.

Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models

Recent research advances in crop breeding and bioinformatics have revealed several genes that determine the flowering (heading) time in some crops. In this study, we calibrated an integrated rice phenology model that uses the genetic information of the cultivar to estimate the parameters of the models. Differences in the prediction of heading stage were examined using different phenology models and parameter estimation methods. We also proposed a novel approach to construct a dual-integrated ensemble model using both multiple crop phenology and regression models to estimate their parameters based on the haplotype combinations of 11 genes related to the heading date of rice. We used the data of 144 Japanese rice (Oryza sativa L.) cultivars to compare the prediction accuracy of the proposed approach with that of individual integrated phenology models. Subsequently, 4,083 ensemble patterns were analyzed using four rice phenology models (Beta, simplified Beta, ORYZA2000, and SIMIRIW) and three machine learning-based parameter regression methods (support vector, random forest, and ridge regression). The accuracy of the individual integrated model depended on the phenology model and its parameter regression method used. In some cases, the RMSE differed by more than one day. When the individual integrated model with the highest prediction accuracy in the training data was applied to the known and unknown environmental test data sets, the prediction accuracies were about 7.0 d and 8.2 d, respectively in the cross-validation. The prediction accuracy of the dual ensemble model with five ensemble members was approximately 5.9 d and 6.5 d in known and unknown environments. Our results suggest that the dual ensemble approach can enhance crop phenology prediction accuracy and present the importance of ensemble parameterization as well as combining multiple crop models when integrating genomic prediction with crop phenology models.

Use of tree-based machine learning methods to screen affinitive peptides based on docking data.

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

Estimating residential buildings’ energy usage utilising a combination of Teaching–Learning–Based Optimization (TLBO) method with conventional prediction techniques

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R2 value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R2 (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings’ cooling load.

The impact of thermal insulating materials in heat loss control in smart green buildings using experimental and swarm intelligent analysis.

The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and promote sustainability. The characteristics of non-stationary and non-linear heat loss through environmentally friendly materials make it challenging to anticipate accurately. At the same time, many of the industry's presently accessible computational models have been created with this in mind; the majority call for powerful computers and time-consuming computations. The artificial neural network (ANN) has been utilized for prediction, and ground-breaking research has shown the viability of this strategy. This research proposes an artificial neural network (ANN) prototype to estimate construction cooling load usage. ANN is integrated with the vortex search algorithm (VS), stochastic fractal search (SFS), and multi-verse optimizer (MVO) models to compare the three models' outcomes and suggest a more accurate strategy. These techniques make a linear mapping among the output and input parameters, often utilized for modeling and regression. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.9464, 0.99827, and 0.99522 for VS-MLP, SFS-MLP, and MVO-MLP, respectively, with an unknown dataset which is acceptable. The training RMSE amounts for VS-MLP, SFS-MLP, and MVO-MLP are 0.06433, 0.00619, and 0.01028 for the unknown dataset, which is acceptable. According to the MAE values of 0.0082902, 0.0047834, and 0.0076534 in the training phase for VS-MLP, SFS-MLP, and MVO-MLP approaches and the values of testing MAE error of 0.029107, 0.018167, and 0.029212 for VS-MLP, SFS-MLP, and MVO-MLP approaches, respectively, it is obtained that the SFS-MLP has a lower MAE value. The lowest RMSE value and the higher R2 value indicate the favorable accuracy of the SFS-MLP technique.

Analyzing the Effects of Plasma Treatment Process Parameters on Fading of Cotton Fabrics Dyed with Two-Color Mix Dyes Using Bayesian Regulated Neural Networks (BRNNs)

ABSTRACT This study used Bayesian Regulated Neural Networks (BRNN) with 10-fold cross-validation to accurately forecast fading effects of plasma treatment on cotton fabrics for a given set of parameters. By training six independent BRNN models, a reduction in model complexity and an enhancement in generalizability to unknown datasets were achieved. The input comprises plasma treatment parameters and color measurements of the cotton fabric before fading, while the output comprises color measurements after fading. The plasma treatment parameters included color depth, air (oxygen) concentration, water content and treatment time. Color measurements included CIE L*a*b*C*h and K/S values. Furthermore, 162 datasets derived from two-color mixed-dye cotton fabrics were utilized for training and testing. The outcomes revealed superior prediction performance of the BRNN compared to the Levenberg-Marquardt Neural Networks, with R2 values approaching 1 and 82.35% to 94.12% of the sample predictions lying within the acceptable color difference range. Through global sensitivity analysis, the impact of treatment parameters on fading effects was quantified, providing a scientific basis for parameter adjustment. This study not only elucidated the mechanism of plasma treatment-induced fading but also offers effective prediction tools for the intelligent and digital development of the fashion clothing fading domain.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine (TBM) has been widely applied for underground construction worldwide; however, how to ensure the TBM tunneling process safe and efficient remains a major concern. Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction, for which a reliable prediction helps optimize the TBM performance. Here, we develop a hybrid neural network model, called Attention-ResNet-LSTM, for accurate prediction of the TBM advance rate. A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model. The evolutionary polynomial regression method is adopted to aid the selection of input parameters. The results of numerical experiments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error. Further, parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy. A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters. The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata. Finally, data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model. The results indicate that, compared to the conventional ResNet-LSTM model, our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain.

Mountain soils have received significant attention due to their profound influence on ecological processes and environmental factors. However, mapping these soils in digital soil mapping technique encounters several challenges, including high local variability, non-linear relationships between environmental covariates and soil properties, limited accessibility in complex topographical settings, and the absence of universally applicable covariates for soil formation. To address these issues, this study integrates soil-forming factors of the scorpan model to map soil organic carbon (SOC) and soil texture in the mid-Himalayas. By considering over 100 environmental covariates, with a focus on terrain parameters relevant to mountainous environments, the study aims to enhance the accuracy of ML regression models through augmentation techniques that overcome data insufficiency. Using augmented soil observations and covariates, a non-parametric random forest regression model is trained and applied to predict soil variables across the study area, generating a continuous fine-resolution map. The model's performance, evaluated against an unknown dataset, was significant with an R-square of 0.80, 0.79, 0.72, and 0.84 for clay, sand, silt, and SOC, respectively. Furthermore, a sensitivity analysis of the environmental covariates and their impact on the model revealed that all the soil-forming factors make a significant contribution to the model's effectiveness. The insights gained from this research contribute to a better understanding of mountain soils and facilitate the development of effective conservation and sustainable management strategies for mountainous regions.

Machine learning methods for anomaly classification in wastewater treatment plants

Modern wastewater treatment plants base their biological processes on advanced control systems which ensure compliance with discharge limits and minimize energy consumption responding to information from on-line probes. The correct readings of probes are particularly crucial for intermittent aeration controllers, which rely on real-time measurements of ammonia and oxygen in biological tanks. These data are also an important resource for developing artificial intelligence algorithms that can identify process or sensor anomalies, thus guiding the choices of plant operators and automatic process controllers. However, using anomaly detection and classification algorithms in real-time wastewater treatment is challenging because of the noisy nature of sensor measurements, the difficulty of obtaining labeled real-plant data, and the complex and interdependent mechanisms that govern biological processes. This work aims at thoroughly exploring the performance of machine learning methods in detecting and classifying the main anomalies in plants operating with intermittent aeration. Using oxygen, ammonia and aeration power measurements from a set of plants in Italy, we perform both binary and multiclass classification, and we compare them through a rigorous validation procedure that includes a test on an unknown dataset, proposing a new evaluation protocol. The classification methods explored are support vector machine, multilayer perceptron, random forest, and two gradient boosting methods (LightGBM and XGBoost). The best performance was achieved using the gradient boosting ensemble algorithms, with up to 96% of anomalies detected and up to 84% and 62% of anomalies classified correctly on the first and second datasets respectively.

Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence.

Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence.