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In recent years, frequency domain spectroscopy (FDS) is often used to evaluate oil paper insulation state in power transformer bushing. But it is still very difficult to evaluate the moisture content accurately and quickly. In order to solve this problem, this paper proposes an intelligent algorithm based on random forest regression (RFR) to construct an efficient evaluation method through segmented FDS curves. Furthermore, the characteristics of FDS curves were studied and the intelligent method was compared with support vector regression (SVR) and deep neural networks (DNN). The results show that the dielectric loss, the real part and imaginary part of complex capacitance all move upward with the moisture increasing, so they can be used as the input feature of the evaluation model; The moisture content evaluation accuracy of the RFR model in the whole frequency band is higher than that of SVR and DNN models; With the increase of lower cut off frequency (FDS test stop frequency), the FDS test time is greatly shortened, and the accuracy of the RFR model can still meet the evaluation requirements. Therefore, the data in a compromise frequency band can be used to evaluate the moisture content of oil paper insulation accurately and quickly. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation. [ABSTRACT FROM AUTHOR]

In order to explore the international competitiveness of China's agricultural product supply chain, this paper combines machine learning algorithms to optimize the data processing process of agricultural product supply chain, and proposes a data processing method for agricultural product supply chain based on the intra‐column rules and inter‐column rules. According to the problems of my country's agricultural product supply chain, this paper establishes a new agricultural product supply chain management model based on the various modules of the supply chain link, and constructs a corresponding intelligent analysis model. Moreover, this paper combines machine learning algorithms to study the improvement path of the international competitiveness of China's agricultural product supply chain, and builds an intelligent model to improve the international competitiveness of China's agricultural products. Finally, after constructing the model, this paper obtains the path to improve the international competitiveness of China's agricultural products through the simulation operation model, which also verifies the effectiveness of the intelligent algorithm of this paper from the side. [ABSTRACT FROM AUTHOR]

An array of atmospheric profile observations consists of three‐dimensional vectors representing pressure, temperature, and humidity, with each profile forming a continuous curve in this three‐dimensional space. In this paper, the Signature method, which can quantify a profile's curve, was adopted for the atmospheric profiles, and the accuracy of profile representations was investigated. The description of profiles by the signature was confirmed with adequate accuracy. The machine‐learning‐based model, developed using the signature, exhibited a high level of annual accuracy with minimal absolute mean differences in temperature and water vapor mixing ratio (<2.0 K or g kg−1). Notably, the model successfully captured the vertical structure and atmospheric instability, encompassing drastic variations in water vapor and temperature, even during intense rainfall. These results indicate the Signature method can comprehensively describe the vertical profile with information on how ordered values are correlated. This concept would potentially improve the representation of the atmospheric vertical structure. Plain Language Summary: The atmospheric profile can be visualized as a three‐dimensional curve representing pressure, temperature, and humidity. By utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphere. In this paper, we confirmed the accuracy of atmospheric profile representation using signatures and introduced the characteristics of signatures revealed through machine‐learning models. The description of profiles by the signature was confirmed with adequate accuracy. Moreover, the model demonstrated robust annual accuracy, with minimal temperature and water vapor discrepancies. It effectively captured the vertical structure and instability of the atmosphere, even during heavy rainfall, characterized by significant temperature and water vapor content fluctuations. Key Points: The utilization possibility of the Signature method for atmospheric profiles was confirmedBy utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphereThe machine‐learning model developed with the signature can predict the profiles with high annual accuracy [ABSTRACT FROM AUTHOR]

The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice. [ABSTRACT FROM AUTHOR]

Advances in data assimilation (DA) methods and the increasing amount of observations have continuously improved the accuracy of initial fields in numerical weather prediction during the last decades. Meanwhile, in order to effectively utilize the rapidly increasing data, Earth scientists must further improve DA methods. Recent studies have introduced machine learning (ML) methods to assist the DA process. In this paper, we explore the potential of a four‐dimensional variational (4DVar) constrained neural network (NN) method for accurate DA. Our NN is trained to approximate the solution of the variational problem, thereby avoiding the need for expensive online optimization when generating the initial fields. In the context that the full‐field system truths are unavailable, our approach embeds the system's kinetic features described by a series of analysis fields into the NN through a 4DVar‐form loss function. Numerical experiments on the Lorenz96 physical model demonstrate that our method can generate better initial fields than most traditional DA methods at a low computational cost, and is robust when assimilating observations with higher error outside of the distributions where it is trained. Furthermore, our NN‐based DA model is effective against Lorenz96 physical models with larger variable numbers. Our approach exemplifies how ML methods can be leveraged to improve both the efficiency and accuracy of DA techniques. Plain Language Summary: The use of machine learning (ML) to approximate mappings from data has made a significant impact on numerical weather prediction. In the data assimilation (DA) process, several recent studies have applied ML to accelerate or improve the accuracy of DA output. In this paper, we investigate the potential of employing physical constraints based on four‐dimensional variational (4DVar) DA to further enhance the accuracy of an end‐to‐end ML‐based DA model. Our objective is to determine whether the 4DVar‐constrained ML model can perform the DA task more efficiently and produce comparable accuracy to the traditional DA methods. We trained our NN‐based model without true values as labels and tested it on the Lorenz96 physical model. Several experiments have been applied to verify that the 4DVar‐constrained ML model can be used as a potential substitute for the DA process. Key Points: A physics‐informed neural network trained without ground truths can provide accurate initial fields for numerical predictionsThe system's kinetic features are embedded into the model through our four‐dimensional variational form loss functionWe show on Lorenz96 that the proposed method can be used directly for accurate data assimilation as a low computational cost [ABSTRACT FROM AUTHOR]

Malware developers install malware on mobile users' devices and steal their personal information without their knowledge. According to recent studies, it has been observed that malware developers are now targeting Android mobile devices. Researchers have examined the issues of detecting malware in these devices and proposed different methods and techniques. This study's main goal is to aid researchers in gaining a basic understanding of Android malware and its numerous detection methods. Earlier experiments that used machine learning to detect Android malware will be carefully reviewed in this paper. This in‐depth review article thoroughly examines the origins, evolution, and sustainability of Android malware detection. It offers an in‐depth literature review that includes the most recent approaches and research trends for detecting malware, from static analysis to dynamic analysis, machine learning, and deep learning. Additionally, we review current approaches' shortcomings and difficulties and suggest possible paths for further investigation. The paper aims to stimulate further innovation in this essential field by providing researchers and practitioners with a comprehensive overview of the current status of Android malware detection. [ABSTRACT FROM AUTHOR]

With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy. [ABSTRACT FROM AUTHOR]

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

Liquefaction‐induced lateral spread is a kind of ground deformation caused by soil liquefaction, which is a danger to houses, roads and other infrastructures. In order to systematically investigate the influence law of different parameters on lateral displacement, this paper establishes a numerical model of the seismic responses of gently sloping grounds through the open‐source finite element software OpenSees and carries out a large number of calculations considering various working conditions. Based on the calculation results, eXtreme Gradient Boosting (XGBoost) and random forest (RF) regression are used to build the proxy models for lateral spread displacement prediction. The finite element model was verified by using the multiple VELACS No. 2 centrifuge experiments. Finally, the model was interpreted using SHapley Additive exPlanation (SHAP) method. The results of the model training showed that both models were able to achieve a good fit to the numerical calculation results, with the RF model having a smaller prediction error for the centrifuge experiments. The model interpretation results showed that the modified cumulative absolute velocity (CAV5) was the most important input variable in the model, and the importance of ground slope (S), relative density (Dr) and thickness of liquefiable soil layer (HL) was relatively high. In addition, the influence of each parameter on the lateral displacement is consistent with the actual situation, reflecting the rationality of the model prediction process. In addition, the results showed that there is a threshold for the modified cumulative absolute velocity (CAV5) and Arias intensity (Ia) that leads to a significant increase in lateral displacement. The thresholds of CAV5 and Ia are 1.3 g s and 1.0 m/s, respectively. These thresholds are in good agreement with the thresholds for triggering the overall initial liquefaction of the soil layer determined by related studies, which provides a reference for the evaluation of liquefaction‐induced lateral displacement. [ABSTRACT FROM AUTHOR]

Data on infrastructure project costs are often unstructured and lack consistency. To enable costs to be compared within and between organizations, large amounts of data must be classified to a common standard, typically a manual process. This is time‐consuming, error‐prone, inconsistent, and subjective, as it is based on human judgment. This paper describes a novel approach for automating the process by harnessing natural language processing identifying the relevant keywords in the text descriptions and implementing machine learning classifiers to emulate the expert's knowledge. The task was to identify "extra over" cost items, conversion factors, and to recognize the correct work breakdown structure (WBS) category. The results show that 94% of the "extra over" cases were correctly classified, and 90% of cases that needed conversion, correctly predicting an associated conversion factor with 87% accuracy. Finally, the WBS categories were identified with 72% accuracy. The approach has the potential to provide a step change in the speed and accuracy of structuring and classifying infrastructure cost data for benchmarking. [ABSTRACT FROM AUTHOR]

Visual surveillance systems have been playing an important role in monitoring and managing at public areas. However, the computational complexity of video compression in these applications is still a great challenge. To meet practical requirements, the authors propose in this paper a low‐complexity surveillance video coding solution in which the most recent Versatile Video Coding (VVC) standard is improved with a novel learning adaptive motion search algorithm. The proposed algorithm is designed based on the temporal motion and spatial texture characteristics of surveillance videos. First, the authors study and define a list of spatial and temporal features which indicates the motion and texture characteristics of surveillance video. These features are used together with a machine learning algorithm to appropriately assign a search range for the VVC motion search. Second, to reduce search points, the authors propose an adaptive Test Zone (TZ) search in which TZ steps are early terminated following the variation of spatial–temporal features. Performance evaluation conducted for a rich set of surveillance videos and relevant benchmarks have shown the superiority of the proposed method, notably with around 33% of encoding time saving when compared with the state‐of‐the art VVC solution and relevant benchmarks while asking for negligible compression loss. [ABSTRACT FROM AUTHOR]

Both optimism and pessimism are intricately intertwined with an individual's inherent personality traits and people of all personality types can exhibit a wide range of attitudes and behaviours, including levels of optimism and pessimism. This paper undertakes a comprehensive analysis of optimistic and pessimistic tendencies present within Hindi textual data, employing transformer‐based models. The research represents a pioneering effort to define and establish an interaction between the personality and attitude chakras within the realm of human psychology. Introducing an innovative “Chakra” system to illustrate complex interrelationships within human psychology, this work aligns the Myers‐Briggs Type Indicator (MBTI) personality traits with optimistic and pessimistic attitudes, enriching our understanding of emotional projection in text. The study employs meticulously fine‐tuned transformer models—specifically mBERT, XLM‐RoBERTa, IndicBERT, mDeBERTa and a novel stacked mDeBERTa—trained on the novel Hindi dataset ‘मनोभाव’ (pronounced as Manobhav). Remarkably, the proposed Stacked mDeBERTa model outperforms others, recording an accuracy of 0.7785 along with elevated precision, recall, and F1 score values. Notably, its ROC AUC score of 0.7226 underlines its robustness in distinguishing between positive and negative emotional attitudes. The comparative analysis highlights the superiority of the Stacked mDeBERTa model in effectively capturing emotional attitudes in Hindi text. [ABSTRACT FROM AUTHOR]

Tsunami simulations require powerful computational resources to be performed efficiently. Although the modern graphics processing units (GPUs) allow the acceleration of this kind of simulations, they can still last many minutes or even hours for simulations which have to deal with very high spatial resolutions or simulation times. In this paper, we propose a method to predict the alert or inundation level of a tsunami generated by an earthquake using deep learning methods. In particular, we train multilayer perceptron (MLP) neural networks for predicting the alert level due to a tsunami at given coastal locations. Ensemble methods are used to improve the predictions of the neural networks. Tsunamis caused by ruptures of several fault segments at different time instants, application to real events, probabilistic forecasting and comparison with other machine learning algorithms are also addressed. Results on realistic scenarios confirm that good accuracies are obtained. The inference times of the trained networks and ensembles are also very low, lasting less than one second to predict the results of thousands of simulations. The proposed method could be used in a tsunami early warning system along with the application of scaling laws. Plain Language Summary: In tsunami early warning systems (TEWS) it is crucial to perform fast predictions of the risk of a tsunami in coastal areas in order to take appropriate measures before the arrival of the tsunami. Currently, tsunami simulations may take many minutes or even hours despite using modern computational resources. In this work we propose a method based on deep learning techniques to predict the alert level of a tsunami generated by an earthquake at given coastal locations in a fraction of a second. This method could be used in TEWS along with techniques to estimate the values of some parameters of the earthquake. Application to real events and probabilistic predictions are also addressed. Key Points: A deep learning method to predict different tsunami alert levels is proposedTsunamis caused by the rupture of multiple fault segments at different time instants are supportedApplication to real events with probabilistic predictions are addressed [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

The die shear test is a feasible and conventional method to characterize the shear strength of die‐attaching layer materials in electronic packaging. A new method for determining cohesive zone model (CZM) parameters using deep neural networks (DNN) and die shear tests is proposed, different from classical fracture framework or lap shear test‐based methods. With the sintered nano‐silver die shear test, the results show that the bilinear CZM inversion results agree well with the experimental results. It is found that the DNN model has high accuracy in predicting and identifying the maximum shear traction strength τmax, separation displacement of the interface δf, and the interface stiffness k1 of CZM parameters for sintered nano‐silver adhesive layer through die shear test load versus displacement curves. The presented DNN‐aided inverse identifying method through the die shear test in this paper could provide an alternative and convenient method for extracting CZM parameters of various kinds of adhesive materials in electronic packaging. Highlights: Die shear tests were used for the inverse identification of CZM parameters.The die shear test P–δ curves were established as the dataset.A DNN‐aided CZM inverse identification method was proposed.The DNN‐aided model can accurately identify the CZM parameters. [ABSTRACT FROM AUTHOR]

Very high‐cycle fatigue life (VHCF) prediction of high‐strength steel based on machine learning (ML) was investigated in this paper. First, a total of 173 sets of experimental data on the VHCF of high‐strength steel were collected to train the ML model. The sensitivity coefficient analysis indicated that inclusion size and maximum stress were the strongest correlation parameters with fatigue life and selected as the input features for the final model training. The S–N curve predicted by the obtained ML model closely aligns with the actual S–N curve. Among the three algorithm models, namely, random forest, XG boost, and gradient boosting, the gradient boosting model exhibited superior performance and achieved the highest accuracy in predicting the VHCF life of high‐strength steel. A comparison between the Murakami model and the gradient boosting model was conducted. It is indicated that ML exhibits superior predictive performance with high efficiency and excellent accuracy. Highlights: S–N curve predicted by machine learning model closely aligns with that by experiments.Gradient boosting model exhibited superior performance in predicting the VHCF life.Machine learning model outperforms the Murakami model in the terms of accuracy. [ABSTRACT FROM AUTHOR]

Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors. Highlights: AI was used for the first time to evaluate fatigue life of shear connectors in bridges.AI predicted fatigue life of shear connectors more accurately than the current methods.GPR and DT algorithms were the best algorithms for assessing fatigue life of connectors.The current equations in AASHTO for fatigue may need to be redeveloped based on AI. [ABSTRACT FROM AUTHOR]

Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed. [ABSTRACT FROM AUTHOR]

Progression‐free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan–Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so‐called joint model enables model‐based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine‐learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model‐based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials. [ABSTRACT FROM AUTHOR]

In the past years, multimedia content has improved in realism and plausibility owing to the development of deep learning techniques, particularly the generative adversarial networks and variational auto‐encoders. Though digital content, especially digital movies shot from a certain viewpoint gives a true representation of reality, yet the ubiquitous usage of content manipulation techniques casts doubt on its veracity. Deepfaking an AI based tampering technique, is able to map facial and acoustic features of a source person onto the target with an intention to make target say or enact the things that has not happened in real. Numerous approaches have been proposed in the literature for detection of image and video deepfakes. With technological advancement, researchers have also started to examine audio deepfakes and ways to detect them. As there is currently no comprehensive overview of audio deepfake generation and detection techniques, this paper aims to provide a survey of the relevant literature in this area. This survey paper intends to help research fraternity about the available audio generation and detection approaches for design of reliable detection models in future to classify fake and real audios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method based on continuum damage mechanics (CDM) and machine learning (ML) models to evaluate the vibration fatigue behavior of W1‐type railway fastener clips subjected to high‐frequency vibration. Firstly, static and fatigue tests are conducted on 60Si2Mn spring steel to acquire elastic modulus, tensile strength, and P‐S‐N curves. Subsequently, a CDM model is established, and numerical simulations are performed under various working conditions to obtain the fatigue characteristics of the clips. Finally, the ML model is trained using numerical simulation results, thereby establishing a mapping model between the working conditions and fatigue characteristics. The developed ML model demonstrates high accuracy in predicting the vibration fatigue life of the clips. Moreover, the Shapley Additive Explanations (SHAP) algorithm is employed to elucidate the ML model, revealing that the vibration frequency has a greater impact on the fatigue life of the clips compared to the vibration displacement. Highlights: A novel approach is developed for vibration fatigue assessment of fastener clip.The approach effectively combines the continuum damage model and ML (ANN) model.Static and fatigue tests of 60Si2Mn spring steel are conducted.Fatigue characteristic of clips subjected to high‐frequency vibration is studied. [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

Recently, graph neural networks (GNNs) have attracted much attention in the field of machine learning due to their remarkable success in learning from graph‐structured data. However, implementing GNNs in practice faces a critical bottleneck from the high complexity of communication and computation, which arises from the frequent exchange of graphic data during model training, especially in limited communication scenarios. To address this issue, we propose a novel framework of federated graph neural networks, where multiple mobile users collaboratively train the global model of graph neural networks in a federated way. The utilization of federated learning into the training of graph neural networks can help reduce the communication overhead of the system and protect the data privacy of local users. In addition, the federated training can help reduce the system computational complexity significantly. We further introduce a greedy‐based user selection for the federated graph neural networks, where the wireless bandwidth is dynamically allocated among users to encourage more users to attend the federated training of neural networks. We perform the convergence analysis on the federated training of neural networks, in order to obtain some more insights on the impact of critical parameters on the system design. Finally, we perform the simulations on the coriolis ocean for reAnalysis (CORA) dataset and show the advantages of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Stock prices forecasting has always been a challenging task. Although many research projects adopt machine learning and deep learning algorithms to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper we introduce a hybrid model that improves stock price prediction by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices across time windows, while LSTM analyses the initial time‐series data with the combination of dependencies which considered as residuals. In predicting the SSE Composite Index, our experiment reveals that when the closing price data for the previous five consecutive trading days is used as the input, the performance of the model (ResNLS‐5) is optimal compared to those with other inputs. Furthermore, ResNLS‐5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of prediction accuracy. It also demonstrates at least a 20% improvement over the current state‐of‐the‐art baselines. To verify whether ResNLS‐5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The experimental results show that the trading strategy based on predictions from ResNLS‐5 can successfully mitigate losses during declining stock prices and generate profits in the periods of rising stock prices. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses. However, the two traditional measurement methods, mid‐upper arm circumference (MUAC) and weight‐for‐height (WFH), are impractical in conflict and catastrophic disaster situations. They require well‐trained enumerators, cumbersome equipment, and close supervision. The Method for Extremely Rapid Observation of Nutritional Status (MERON) addresses the problem, using simple facial photographs. Facial features are extracted to predict Body Mass Index (BMI) in adults and Weight for Height Z Score (WFHZ) in children under five. MERON correctly predicts adult BMI classification with 78% accuracy. A variant of the model, trained on a sample of 3167 children in Kenya, successfully classified 60% of cases. On most measures, MERON was easier and more culturally acceptable to use than the traditional measurement methods. If MERON were to be trained and validated on a larger sample, with more extreme cases, it would provide a practical solution to a recurrent humanitarian problem. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyse whether the Transformer neural network, which has become one of the most influential algorithms in Artificial Intelligence over the last few years, exhibits predictive capabilities for high‐frequency Forex data. The prediction task is to classify short‐term Forex movements for six currency pairs and five different time intervals from 60 to 720 min. We find that the Transformer exhibits high predictive power in the context of intraday Forex trading. This performance is slightly better than for the carefully selected benchmark – ResNet‐LSTM, which currently is a state‐of‐the‐art algorithm. Since intraday Forex trading based on deep learning models is largely unexplored, we offer insight on which currency pair and time interval are amenable to devising a profitable trading strategy. We also show that high predictive accuracy can be misleading in real world trading for short time intervals, as models trained on OHLC data tend to report the highest accuracy when the spread cost is the highest. This renders assessment based on typical machine learning metrics overly optimistic. Therefore, it is critical to backtest frequent intraday Forex trading strategies with realistic cost assumptions, which is rarely the case in empirical literature. Lastly, sensitivity analysis shows that the length of the time interval used for training does not play a critical role in the Transformer's predictive capabilities, whereas features derived from technical analysis are essential. [ABSTRACT FROM AUTHOR]

Aim: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug‐resistant epilepsy. Method: This was a prospective, single‐arm, interventional study (Idea, Development, Exploration, Assessment, and Long‐Term Follow‐Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG‐defined seizure‐onset zone (SOZ). Results: Twenty patients (median age 12 years, range 4–18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. Interpretation: We demonstrate early‐stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms. What this paper adds: The focal cortical dysplasia detection algorithm collocated with the seizure‐onset zone (SOZ) in 4 out of 19 patients.The algorithm changed the resection boundaries in 1 of 19 patients undergoing stereoelectroencephalography for drug‐resistant epilepsy.The patient with an altered resection due to the algorithm was seizure‐free 1 year after resective surgery.Overall, the algorithm did not increase the proportion of patients in whom SOZ was identified. [ABSTRACT FROM AUTHOR]

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high‐value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction. [ABSTRACT FROM AUTHOR]

Imbalanced image datasets consist of image datasets where there is a significant disparity in the number of samples across different classes. With imbalanced image datasets, learning algorithms often tend to be biased toward the majority class samples. This leads to poor classification of minority class samples as their training is not properly conducted. It becomes more complicated when the number of samples in the minority class is very low. In this paper, a novel hierarchical algorithm is proposed for generating new data using Capsule Generative Adversarial Networks (CapsGAN) to address the class imbalance problem in imbalanced image datasets. Unlike common GAN models, the proposed method incorporates an auxiliary CapsNet to identify high‐value images in both minority and majority classes. This identification is based on the ability to detect complex relationships between low‐level and high‐level features present in capsule networks. Furthermore, the proposed CapsGAN model is conditioned to generate minority class samples based on feature vectors of last capsule layer to achieve a more balanced image dataset. For evaluating the performance of the proposed model, an image dataset called CICS was collected and introduced. Extensive experiments were also conducted using various online image datasets from different domains, with varying numbers of classes and data sizes. The experimental results demonstrated that the proposed model can generate high‐quality samples in cases where the image dataset or the number of minority class samples is relatively small. Furthermore, the proposed model has maintained an accuracy of over 80% in an imbalanced ratio of 1:60. [ABSTRACT FROM AUTHOR]

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase‐identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al–Si–Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well‐trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al–Si–Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample. [ABSTRACT FROM AUTHOR]

Introduction: Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations. Methods: We conducted a systematic review via a PubMed search for the period 1990–2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications. Results: Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine‐related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen‐specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research. Conclusions: Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere. [ABSTRACT FROM AUTHOR]

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER. [ABSTRACT FROM AUTHOR]

The reversible pump‐turbine plays an important role in hydropower stations, but pressure pulsation during their operation affects their performance and lifespan. Accurate prediction of pressure pulsation signals can provide an important basis for energy planning and stable operation of pumped storage units, thereby promoting sustainable development of the environment. This study introduces an optimization method that combines long short‐term memory (LSTM) and variable mode decomposition (VMD) to enhance the prediction accuracy of pressure pulsation signals. First, by decomposing the pressure pulsation signal into multiple relatively stable subsequence components using VMD, the characteristics of the original signal become more distinct. Subsequently, individual LSTM‐based time series prediction models were constructed for each modal function, and the hyperparameters related to subsequence were optimized using the sparrow search algorithm. To validate the efficacy of the proposed approach, this paper conducted experiments using pressure pulsation signals of a pump‐turbine obtained through numerical simulation. The experimental data was divided into training and testing sets, with the former used to train the LSTM model and the latter used for validation. The experimental results show that the optimized VMD with an optimized LSTM method can effectively improve the prediction accuracy of pressure pulsation signals in reversible pump‐turbine. [ABSTRACT FROM AUTHOR]

Ransomware is a serious security concern to mobile devices, as it prevents the use of the device and its contents until a ransom is paid, resulting in considerable financial losses for both people and corporations. The existing anti‐malware measures have shown to be inadequate in combatting new malware variants that utilize advanced evasion strategies like Polymorphic, Metamorphic, Dynamic Code Loading, Time‐based evasion, and Reflection. Furthermore, these primary defences have also suffered from low detection rates, significant false positives, high processing times, and excessive processing and power consumption that is inappropriate for smartphones. This paper offers the binary JAYA (BJAYA) for ransomware detection in Android mobile devices using the BJAYA optimization‐based algorithm. The developed algorithm's effectiveness has been assessed against two datasets, the 0–1 knapsack, and real ransomware dataset. The proposed BJAYA method surpassed the other algorithms on 85% of the 0–1 knapsack datasets. The suggested BJAYA method was also tested on a ransomware dataset in two phases. In the first stage of testing, BJAYA outperformed other standard classifiers with sensitivity and Gmean values of 97% and 98.2%, respectively. In the second stage of testing, BJAYA outperformed other GA, FPA, and PSO metaheuristic algorithms in terms of specificity, sensitivity, and Gmean. These findings indicate the applicability of the suggested BJAYA algorithm for ransomware detection. [ABSTRACT FROM AUTHOR]

Nanofluids are thermoelectric substance with a greater thermal transmission effectiveness than traditional thermal fluids. Nanofluid's specific heat capacity (SHC) is a crucial thermophysical parameter since it controls the fluid's heat transfer coefficient. Surprisingly few research investigations have been done on the prognostic modelling of the specific heat capacity of fusion nanofluids, despite the fact that numerous experiments have been conducted on the heat transfer capacitance of blended nanofluids. This study focuses on the use of algorithms based on machine learning procedures (MLP) to estimate the SHC of blended nanofluids. Numerous numbers of hybrid nanofluids are investigated in this investigation. Total of 984 samples of hybrid nanofluids for specific heat capacity has been collected from nine experimental research papers. Various MLP techniques were utilized in this analysis, including extreme boost gradient boost regression (XGB), support vector regression augmented with a genetic algorithm (support vector regression [SVR]‐genetic algorithm [GA]), grid search optimized based gradient boost regression algorithm (GBR) (GBR‐GSO), and voting ensemble procedure (VE). The obtained correlation coefficients of the SVR‐GA, XGB, VE and GBR‐GSO models for the testing dataset are 98.43%, 96.29%, 94.4% and 96.55% respectively. The SVR‐GA model showed a better predictive accuracy relative to other ML models. This SVR‐GA anticipated model could be used for quick and reliable prediction of the SHC of blended nanofluids which reduces the burden associated with experimental measurement possible future work includes applying a machine‐learning strategy to the problem of determining the diffusivity of hybrid nanofluids. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) believes to be a major cause of transience and indisposition worldwide. Early diagnosis and timely intervention are critical in preventing the progression of CVD and improving patient outcomes. Machine learning (ML) algorithms have emerged as powerful tools in CVD recognition, with the potential to assist physicians in making accurate and efficient diagnoses. This research paper explores the combination of multiple ML algorithms for CVD recognition, utilizing diverse datasets such as the Cleveland, Hungarian, Switzerland, statlog, and VA Long Beach datasets. Additionally, a CVD dataset comprising 12 attributes and 70,000 records is employed, demonstrating improved results through the proposed and trained model compared to previous prediction techniques for CVD. The performance of various ML techniques, including support vector machines (SVM), naive Bayes (NB), K‐nearest neighbour (KNN), random forest (RF), and logistic regression (LR), is evaluated and compared. The impact of feature selection and feature scaling on the models' performance is also examined. An ensemble bagging technique is applied which is being embedded with other classifiers. LR classifier embedded with bagging techniques proved to be our proposed model. The findings reveal that the proposed Hybrid Linear Regression Bagging Model (HLRBM) outperforms other models. Furthermore, the study highlights the significance of data preprocessing techniques, such as data normalization and class balancing, which significantly enhance the performance of all models. To this end, standard scalar and synthetic minority over‐sampling technique (SMOTE) are employed. The study emphasizes the importance of selecting an appropriate ensemble technique in conjunction with various ML algorithms and preprocessing methods for CVD prediction. Overall, the research provides valuable insights into the potential of ML in improving CVD risk assessment. [ABSTRACT FROM AUTHOR]

Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score. [ABSTRACT FROM AUTHOR]

We introduce a "symbiotic" ocean modeling strategy that leverages data‐driven and machine learning methods to allow high‐ and low‐resolution dynamical models to mutually benefit from each other. In this work we mainly focus on how a low‐resolution model can be enhanced within a symbiotic model configuration. The broader aim is to enhance the representation of unresolved processes in low‐resolution models, while simultaneously improving the efficiency of high‐resolution models. To achieve this, we use a grid‐switching approach together with hybrid modeling techniques that combine linear regression‐based methods with nonlinear echo state networks. The approach is applied to both the Kuramoto–Sivashinsky equation and a single‐layer quasi‐geostrophic ocean model, and shown to simulate short‐term and long‐term behavior better than either purely data‐based methods or low‐resolution models. By maintaining key flow characteristics, the hybrid modeling techniques are also able to provide higher quality initial conditions for high‐resolution models, thereby improving their efficiency. Plain Language Summary: Models of the ocean vary in complexity. Some are very detailed and manage to show oceanic vortices, whereas others are very efficient but coarse, and unable to compute such vortices. The idea in this paper is to let these different model types work together and benefit from each other, as if in a symbiosis. With knowledge of differences between the detailed and coarse model we can use machine learning techniques to improve the coarse model. In this way a coarse model can be used to provide good quality predictions and to aid a detailed model by taking over part of its computations. We apply our ideas to the Kuramoto–Sivashinsky (KS) model and a quasi‐geostrophic (QG) ocean model, where we show that promising short‐term KS results may generalize to models of the ocean. Long‐term equilibrium experiments with QG show in addition how the correction strategies let a coarse model produce correct flow properties, where standalone physics‐ or data‐based approaches fail. These improved coarse models are computationally cheap, yet good enough to give initial conditions for the fine model, showcasing the symbiotic modeling idea. Key Points: We propose a symbiotic ocean modeling framework in which models of different complexities benefit from each otherUnresolved processes are represented through hybrid machine learning methods using data from the symbiotic frameworkHybrid correction strategies with imperfect physics as control input improve the representation of key long‐term flow properties [ABSTRACT FROM AUTHOR]

In the context of federated learning, this paper focuses on managing inherent dynamics and uncertainties, and optimizing energy management for devices in real‐world industrial environments. The problem is formulated by proposing an online deep reinforcement learning algorithm that optimizes model iteration updates between clients and servers, and an energy harvesting strategy that enhances device performance and extends lifespan. Through these innovative methods, solutions are provided for the dynamics and uncertainties in federated learning and a new approach is devised for energy usage optimization and task scheduling. Numerical results demonstrate the efficiency of the approach in transitioning federated learning from theory to practical application, and offering diversified application scenarios. In summary, this research contributes to the development of federated learning by providing novel insights and methodologies, and highlights the challenges and opportunities for future studies in the field. [ABSTRACT FROM AUTHOR]

In this paper, we present a spatiotemporal deep learning (STDL) network to conduct binary phase scintillation forecasting at a high-latitude global navigation satellite systems (GNSS) station. Historical measurements from the target and surrounding GNSS stations are utilized. In addition, external features such as solar wind parameters and geomagnetic activity indices are also included. The results show that the STDL network can adaptively incorporate spatiotemporal and external information to achieve the best performance by outperforming a naive method, three conventional machine learning algorithms (logistic regression, gradient boosting decision tree, and fully connected neural network) and a machine learning algorithm known as long short-term memory that incorporates temporal information. [ABSTRACT FROM AUTHOR]

This paper presents a Convolutional Neural Network (CNN) architecture named OlapNet, which incorporates implicit operations of OLAP cubes (or data cubes). OLAP cubes are produced from database tables or spreadsheets and they allow particular operations that support performing complex queries efficiently. OlapNet permits evaluating various combinations of these OLAP operations in its search space and thus, it enables, in part, to automate the data transformation step in the knowledge discovery process. A sample of data from an actual database containing anonymized data on the debt history of customers of a financial institution has been used to evaluate our proposal. A predictive classification problem to estimate the probability of any given customer contracting new credits in the next three months has been modelled from these data. Then, traditional methods of Machine Learning and CNN were applied. The results showed that CNN, using the OlapNet architecture, outperforms traditional methods in almost all cases, indicating that the proposed architecture is quite promising. [ABSTRACT FROM AUTHOR]

Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management. [ABSTRACT FROM AUTHOR]

The target coverage problem is considered as one of the major issues in directional sensor networks (DSNs), which is caused by the nature of these networks, including their limited angle of view. Due to the fault tolerance characteristic of some coverage applications, the target coverage is required to be performed using multiple sensors. This challenge is discussed in the literature under the title of k‐coverage problem. Under certain conditions, the number of sensors may suffer some changes due to various factors such as power depletion of the sensors, sensors' malfunctioning, and harshness of the environment. This can result in unavailability of adequate sensors for providing k‐coverage for all targets. The network suffering from such problem is referred to as under‐provisioned network. This paper was aimed at studying such networks by adopting the network conditions to the real environments. To solve this problem, the present paper proposes a hybrid model integrating the genetic algorithm (GA) and Tabu search (TS). The proposed algorithm generally aimed to identify a subset of sensors with appropriate working directions in order to provide a balanced coverage for all the targets available in the network. In order to evaluate the performance of the algorithm several experiments were conducted and the results have been compared with greedy and learning automat‐abased algorithms.. The results of the experiments show the superiority of the algorithm. This paper was aimed at studying such networks by adopting the network conditions to the real environments. To solve this problem, the present paper proposes a hybrid model integrating the genetic algorithm (GA) and Tabu search (TS). Such hybridization was performed to get benefits of GA in the exploration of solution space and the benefits of TS in the solution exploitation. In the proposed model, GA is regarded as the main algorithm and TS is used to improve the current solutions in the population. [ABSTRACT FROM AUTHOR]

This paper studies the iterative learning control (ILC) for a class of second‐order nonlinear hyperbolic impulsive partial differential systems. Firstly, to follow the discontinuous desired output, a P‐type learning law is adopted, and sufficient conditions for the convergence of the tracking error is established under identified initial state value. The rigorous analysis is also given using the impulsive Gronwall inequality. Secondly, the tracking error of output trajectory is considered in systems with state initial values shifting based on an initial learning algorithm. These results of this paper show that the tracking error on the finite time interval can uniform converge to 0 as the iteration index goes to infinity if impulse number of the systems is only a finite numbers. Finally, two numerical simulation examples are given to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

LINKED CONTENT: This article is linked to Verma et al papers. To view these articles, visit https://doi.org/10.1111/apt.17891 and https://doi.org/10.1111/apt.17913 [ABSTRACT FROM AUTHOR]

In this paper, a novel model with a parallel structure is proposed to predict NOx emissions from coal‐fired boilers by using historical operational data, coal properties, and convolutional neural networks. The model inputs are processed and passed into three parallel subnetworks with well‐designed building blocks. The features learned by the three subnetworks are fused and used to predict NOx emissions from a 330‐MW pulverized coal‐fired utility boiler. A comprehensive comparison of different prediction models based on deep learning algorithms shows that the prediction model proposed in this paper outperforms other prediction models in terms of root mean square error criteria. The results show that the parallel structure is key to obtaining accurate predictions while reducing model complexity. This suggests that the model's performance can be improved by designing the model architecture. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi‐scale dynamics. By considering a toy climate model, namely, the two‐scale Lorenz96 model and producing experiments in perfect‐model setting, we explore in detail how several built‐in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non‐uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research. Plain Language Summary: Climate models are computer simulation codes that incorporate centuries of human knowledge of the physics of planet Earth. They are used to understand the past, the present and make projections about the future of our climate. To validate a climate model, scientists tune a number of its parameters so that it yields a simulated climate resembling real‐life observations as much as possible. The main challenge in this tuning task is the extreme cost of climate models which limits a lot the number of tuning experiments scientists can run. In this paper we are interested in a technique that uses artificial intelligence in order to replace the expensive climate model with a cheaper surrogate. We experiment on a simplified model to assess the strengths and weaknesses of this semi‐automatic technique, and show that it can be more efficient when combined with human expertise. Key Points: The History Matching method is explained in detail then used for tuning a toy coupled model: the Lorenz 96 modelThe importance of several design choices is demonstrated, especially when considering forced experiments such as Atmospheric Model Intercomparison Protocol and Ocean Model Intercomparison ProjectWe argue that this tuning method is semi‐automatic & highlight the importance of human expertise when considering it for real coupled models [ABSTRACT FROM AUTHOR]

It is shown that a recently developed hybrid modeling approach that combines machine learning (ML) with an atmospheric global circulation model (AGCM) can serve as a basis for capturing atmospheric processes not captured by the AGCM. This power of the approach is illustrated by three examples from a decades‐long climate simulation experiment. The first example demonstrates that the hybrid model can produce sudden stratospheric warming, a dynamical process of nature not resolved by the low resolution AGCM component of the hybrid model. The second and third example show that introducing 6‐hr cumulative precipitation and sea surface temperature (SST) as ML‐based prognostic variables improves the precipitation climatology and leads to a realistic ENSO signal in the SST and atmospheric surface pressure. Plain Language Summary: This paper introduces and tests schemes for efficiently enabling significant expansion of the utility and scope of a recently introduced hybrid modeling technique that combines machine learning with an atmospheric global circulation model (AGCM). Simulation experiments are carried out with an implementation of the approach on a low resolution simplified AGCM. An examination of the simulated atmospheric circulation suggests that the hybrid model can capture dynamical process not captured by the AGCM. Moreover, the addition of precipitation and sea surface temperature (SST) as machine learning predicted physical quantities to the model improves the precipitation climatology and leads to a realistic El Niño‐La Niña signal in the SST and atmospheric surface pressure. Key Points: A hybrid system combining an atmospheric global circulation model (AGCM) with a machine‐learning component can capture processes not captured by the AGCMMachine learning provides a flexible framework to introduce additional prognostic variables into the hybrid modelThe prototype hybrid model tested in the paper is stable and has a realistic climate in decades‐long simulation experiments [ABSTRACT FROM AUTHOR]