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Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities. [ABSTRACT FROM AUTHOR]

This document highlights two studies published in the Asian Journal of Ecotoxicology. The first study focuses on the development of machine learning models to screen chemicals with hepatotoxicity, or liver toxicity. The models were trained using a dataset of 4014 chemicals and achieved good performance in predicting hepatotoxicity. The second study explores the use of machine learning methods to screen chemicals that induce autonomic dysfunction, a condition affecting the autonomic nervous system. The study developed a model using a dataset of 466 positive and 427 negative samples and identified structural alerts associated with autonomic dysfunction. Both studies provide valuable tools for screening and evaluating toxic chemicals. [Extracted from the article]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [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]

This document is a guest editorial for a special issue on network/traffic optimization towards the 6G network. It highlights six research papers that focus on various topics related to network and traffic optimization in the context of 6G. These topics include hotspot performance in vehicular communication, ad-hoc networks (FANETs), cooperative relaying using MIMO-NOMA technology, network resource allocation using machine learning, cyber attacks and countermeasures in VoWi-Fi, and network management. The papers present theoretical analyses, propose novel models and schemes, and discuss practical applications and experiments. The authors express their gratitude to the researchers and reviewers involved in the publication of these papers. [Extracted from the article]

This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]

With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting. [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 aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data‐driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data‐driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data. The authors' review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. Our review paper identifies a gap in the current understanding of how to effectively utilize SCADA data in wind turbine applications. It emphasizes the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, we highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. [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]

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]

Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models. Plain Language Summary: Total electron content (TEC) is an important parameter to describe the ionosphere. The prediction of TEC is crucial for short‐wave communication systems. Geomagnetic storms disturb the ionosphere, thereby affecting the prediction of TEC. In this paper, the existing multi‐step auxiliary prediction model is used to predict the TEC during geomagnetic storms. Using the International Reference Ionosphere‐2016 model as the reference, the storm‐time performance and time‐varying characteristics of Multi‐Step Auxiliary Prediction (MSAP) are studied. It is found that the MSAP model captures variations with solar activity, month, and Universal Time during geomagnetic storms. Key Points: The MSAP model can predict total electron content (TEC) with high accuracy and stabilityThe MSAP model perform differently in different phases of geomagnetic storms [ABSTRACT FROM AUTHOR]

This paper explores the use of Artificial Neural Networks (ANN) for the rocking problem. The paper adopts rigid rocking blocks of different sizes and slenderness, which undergo rocking motion without sliding and bouncing when subjected to recorded earthquakes. This research focuses on the cases where the blocks overturn or safely return to their initial (rest) position at the end of the ground shaking. An ANN model is trained to efficiently categorise the response into overturning or safe rocking using the structural parameters, ground motion characteristics, and the coefficient of restitution as input. The results show the substantial contribution of velocity and frequency characteristics of the ground motion to overturning. In addition, ANN is used to predict the response amplitude and identify the most critical input variables that govern safe rocking. The analysis reveals that rocking amplitude is governed by a combination of duration, frequency, and intensity characteristics of the ground excitation. Interestingly, the maximum incremental velocity (MIV), a novel intensity measure for the rocking literature, shows a substantial correlation with the rocking amplitude. In this context, this paper proposes closed‐form expressions using the most influential input variables to provide a quick, yet adequately accurate, response prediction. Finally, this study pays special attention to the contribution of the coefficient of restitution, which, in general, is less critical to the peak safe rocking response, while it becomes more important to the overturning response. [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]

In the context of Heterogeneous Wireless Networks (HWN), achieving the best connectivity is crucial for users. With the availability of multiple Radio Access Technologies (RATs), users are constantly looking to be "Always Best Connected" (ABC) by connecting to the best possible RAT. This challenge is also important in Next‐Generation Networks (NGN), where researchers are proposing models and algorithms to make the ABC paradigm a reality. One such challenge is the network selection problem, which involves the automatic and transparent selection of the best RAT at a given time. Many papers in this field have used Multi‐Attribute Decision‐Making (MADM) methods, which have been implemented with various objectives such as increasing quality of service (QoS), reducing energy consumption and costs etc. This paper proposes a modified machine learning algorithm, specifically an adapted K‐means algorithm, to address this challenge. The results show that this proposal outperforms legacy MADM methods in terms of latency, jitter, packet loss, and data rate. So, the paper presents a new approach to network selection in HWN and NGN, and the results suggest that it could be a promising solution to this important challenge. [ABSTRACT FROM AUTHOR]

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation‐associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival‐SVM). Then, in the train cohort, the prediction model was fitted using a leave‐one cross‐validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C‐index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C‐index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning‐based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C‐index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C‐index and the highest C‐index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C‐index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen‐presenting anti‐tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen‐presenting model obtained by the RSF + Lasso method has the best C‐INDEX, which plays a key role in developing antigen‐presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients. [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]

This paper proposes a deep reinforcement learning algorithm to achieve complete coverage path planning for deep‐sea mining vehicle clusters. First, the mining vehicles and the deep‐sea mining environment are modeled. Then, this paper implements a series of algorithm designs and optimizations based on Deep Q Networks (DQN). The map fusion mechanism can integrate the grid matrix data from multiple mining vehicles to get the state matrix of the complete environment. In this paper, a preprocessing method for the state matrix is also designed to provide suitable training data for the neural network. The reward function and action selection mechanism of the algorithm are also optimized according to the requirements of cluster cooperative operation. Furthermore, the algorithm uses distance constraints to prevent the entanglement of underwater hoses. To improve the training efficiency of the neural network, the algorithm filters and extracts training samples for training through the sample quality score. Considering the requirement of cluster complete coverage mission, this paper introduces Long Short‐Term Memory (LSTM) based on the neural network to achieve a better training effect. After completing the above optimization and design, the algorithm proposed in this paper is verified through simulation experiments. [ABSTRACT FROM AUTHOR]

We propose an autoregressive weighted network (ARWNet) time series forecasting model inspired by the idea of ensemble learning. The model adopts the classical autoregressive analysis to optimize weak learners. Meanwhile, the combined weight optimization method is used to construct an efficient, strong learner. With these methodological foundations, the scalability of the framework is greatly enhanced by the possibility of experimenting with other learners to assist in decision‐making. Machine learning can provide great utility and acceptable cost in the prediction process of electricity transformers. Over the years, many research papers on time series prediction have been reported. This work will focus on the analysis using the potential properties in the series: long‐term, continuity, periodicity, and delay. In our experiments, the ETT‐small dataset is used to compare the prediction accuracy of ARWNet and other mainstream models. All results suggest that the proposed ARWNet model demonstrates strong generalization ability and high predicting accuracy with delay characteristics, which outperform current popular time‐series prediction methods. [ABSTRACT FROM AUTHOR]

As electrochemical research undergoes rapid technological progression, the acquisition of substantial amounts of electrochemical impedance spectra (EIS) becomes increasingly feasible. Yet, this advancement introduces intricate challenges in data processing, automation, and interpretation. This paper delves into the sufficiency of unsupervised machine learning (ML) and in particular dimensionality reduction methods in decoding EIS complexities, examining its strengths, limitations, and potential pathways for optimization. As we navigated the intricacies of non‐linear dimensionality reduction, spotlighting t‐distributed stochastic neighbor embedding (t‐SNE) and uniform manifold approximation and projection (UMAP) algorithms, a pattern emerged: these techniques excel at categorizing divergent impedance spectra but show limitations when faced with analogous circuit configurations, especially those substituting a capacitor with a constant phase element. This observation not only underscores a limitation but also accentuates that unsupervised ML approaches, alone, may not fully unravel the nuances of EIS spectra. In the concluding section of our manuscript, we discuss the implications of this finding from a practical standpoint, particularly for electrochemists seeking to apply these methods in their work. [ABSTRACT FROM AUTHOR]

This paper aims to study the phased influencing factors of renminbi (RMB) exchange rate (CNY against USD) and investigate the predictability of the factors selected by multimodel. We first take the time points when China's main exchange reform policies are launched as the demarcation points and divide the entire sample from July 2005 to December 2020 into three periods. Then, we select the potential predictors using several sources, including all factors (without any selection), the factors selected by each of the five commonly used machine learning methods, the significantly correlated factors selected by traditional regression analysis method, and multimodel‐driven factors. Finally, we predict the exchange rate based on the above selected factors and compare the prediction results. The research results show that the main influencing factors are different in different periods, and the influence of phase events cannot be ignored. Even if their influence on the exchange rate has decreased as a result of the "811" exchange rate reform, the money supply and foreign exchange reserves continue to be the primary drivers of RMB exchange rates during the whole period of the sample. Additionally, RMB exchange rate forward is a robust influencing factor in all periods. By comparing the forecast errors, we find that the prediction accuracy of the factors selected based on multimodel is higher than that of the factors selected based on a single method or the tradition method. The findings of this paper provide the following insights for exchange rate managers: In exchange rate risk management, it is important to pay attention to the impact of macroeconomic factors such as foreign exchange reserves and the impact of staged events, and market expectations of exchange rates are equally important. At the technical level, it is recommended to improve the forecasting accuracy by forecasting exchange rates based on common factors selected by multiple better machine learning methods simultaneously rather than those selected by a single method. [ABSTRACT FROM AUTHOR]

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Background: Driven by the ongoing need to provide high‐quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to evaluate the maturity of research discussing LA‐driven LD improvements in higher education. Methods: The systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA‐LD integration theoretical backgrounds) and substantial (LA‐driven LD improvements, types of data used, LA software development) features of the papers. Results and Conclusions: The findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments. Takeaways: The research area discussing LA‐driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA‐driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software. Lay Description: What is already known about this topic: Universities show an increased interest in using LA for improving LD.The evidence of such improvements being efficient is scarce.Learning analytics is weakly grounded in learning and teaching. What this paper adds: LA‐LD research presented in the reviewed papers cannot yet be considered fully mature.Almost half of the research was carried out using a case study approach, intrinsically challenging in terms of replication and validity. As few as three out of the reviewed studies applied an experimental approach, capable of stating the cause–effect relationship between LA and LD.All the studied papers mentioned improvements in LD, but only around a third of them reported actual improvements, another one‐third suggested potential improvements, and the rest did not come down to describing any.The LA software presented in the papers was mostly at the testing or implementation stages, and the impact of implemented software on LD has yet to be evaluated. Implications for practice and/or policy: Future LA‐LD research to become more mature may well elaborate on theoretical footing, contribute to the cause–effect relationship between LA and LD utilising experimental research design, ensure sufficient sample size.Practitioners' efforts should be directed to operationalising LD for efficient measurements, addressing pedagogical challenges when applying LA, and assessing LA‐driven improvements introduced into LD. [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]

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL‐based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients. [ABSTRACT FROM AUTHOR]

Summary: A difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%). [ABSTRACT FROM AUTHOR]

XGBoost is the optimization of gradient boosting with the best overall performance among machine learning algorithms. By introducing a regularization term into the loss function of gradient boosting, XGBoost can effectively limit the complexity of the model, improve the generalization ability, and solve the overfitting problem. In this paper, XGBoost is first introduced into modeling radio‐frequency (RF) power amplifiers (PA) under different temperatures. Furthermore, the modeling effect of XGBoost is mainly dependent on hyperparameters. As traditional grid search is time‐consuming and labor‐intensive, this paper combines particle swarm optimization (PSO) with XGBoost for searching hyperparameters. The experimental results show that XGBoost can effectively suppress the overfitting problem in gradient boosting while modeling RF PAs in different ambient temperatures. In addition, compared to classic machine learning algorithms, including support vector regression (SVR), gradient boosting, and XGBoost, the proposed PSO‐XGBoost can increase the modeling accuracy by one order of magnitude or more while also increasing the modeling speed by more than one magnitude or more. The PSO‐XGBoost model proposed in this paper can be introduced into modeling other microwave/RF devices and circuits to improve modeling accuracy and reduce modeling time. [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]

In this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30‐day and 90‐day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1‐hr TEC prediction at high‐ (80°W, 80°N), mid‐ (80°W, 40°N), and low‐ latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high‐ and mid‐ latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1‐day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC. Plain Language Summary: Due to the impact of the ionosphere on the propagation of radio waves and its significant influence on global navigation satellite systems, we have conducted modeling of the ionospheric total electron content (TEC), which describes the morphology and structure of the ionosphere. We propose the use of eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree to predict TEC in different geographical latitudes. We also evaluate the best prediction time of the XGBoost model in a day. Additionally, the XGBoost model was tested during a geomagnetic storm, and TEC prediction over the region between 20°N and 45°N and 70°E−120°E showed the average error of 2.74 TECU, indicating its stability and superiority. Key Points: Three ensemble learning models are employed to predict total electron content (TEC) in various latitudes using data spanning a solar cycleeXtreme Gradient Boosting model has less predicted errors than Gradient Boosting Decision Tree and Decision Tree models, and the previous TEC history dominants the predicted TEC valuesThe RMSE increases with the predicted time advance, and the average relative error is the minimum in the mid‐latitude [ABSTRACT FROM AUTHOR]

Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy‐preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open‐source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201‐based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor. [ABSTRACT FROM AUTHOR]

Spectral data describing product samples are typically composed of a large number of noisy and irrelevant wavelengths that tends to undermine the performance of multivariate predictive techniques. This paper proposes a two‐phase framework that integrates a preselection wavelength step oriented by wavelength clustering to a wrapper‐based strategy. The first phase performs a pruning process in the data that removes the less informative wavelengths relying on the spectral clustering, a technique deemed suitable to the Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy data at hand. The preselected wavelengths undergo a second phase of selection efforts based on the combination of different wavelength importance indices (i.e., Bhattacharyya distance, Chi‐square, ReliefF, and Gini) and classification techniques (i.e., support vector machine, k‐nearest neighbors, and random forest). When applied to 11 FTIR datasets from different domains, the recommended combination of importance index and classifier increased the average accuracy by 6.37% (from 0.863 to 0.918), while retaining average 3.84% of the original spectra. The framework also improved the selection process regarding computational time. This paper introduces a two‐phase framework merging wavelength preselection through clustering and a wrapper strategy. Initial spectral clustering eliminates less informative wavelengths. Subsequently, diverse wavelength importance indices and classification methods are integrated. Applied to 11 spectral datasets, the proposed combination (spectral clustering [SC]‐random forest [RF]+Gini [GI]) enhances average accuracy by 6.37%, retaining 3.84% of the original spectra, and reduces computational time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the utilization of artificial neural networks (ANNs) in learning models that address the nonlinear anisotropic flow and hysteresis retention behavior of deformable porous materials. Herein, the micro‐geometries of various networks of porous Bentheimer Sandstones subjected to several degrees of strain from the literature are considered. For the generation of the database required for the training, validation, and testing of the machine learning (ML) models, single‐phase and biphasic lattice Boltzmann (LB) simulations are performed. The anisotropic nature of the intrinsic permeability is investigated for the single‐phase LB simulations. Thereafter, the database contains the computed average fluid velocities versus the pressure gradients. In this database, the range of applied fluid pressure gradients includes Darcy as well as non‐Darcy flows. The generated output from the single‐phase flow simulations is implemented in a feed‐forward neural network, representing a path‐independent informed graph‐based model. Concerning the two‐phase LB simulations, the Shan‐Chen multiphase LB model is used to generate the retention curves of the cyclic drying/wetting processes in the deformed porous networks. Consequently, two different ML path‐dependent approaches, that is, 1D convolutional neural network and the recurrent neural network, are used to model the biphasic flow through the deformable porous materials. A comparison in terms of accuracy and speed of training between the two approaches is presented. Conclusively, the outcomes of the papers show the capability of the ML models in representing constitutive relations for permeability and hysteretic retention curves accurately and efficiently. [ABSTRACT FROM AUTHOR]

Cable trees are primarily employed in industrial products to facilitate energy transfer and information exchange among various components. When utilizing machines for assembly, it is essential to convert the wiring plan into a sequence of cable insertion operations executed by the machine under various constraints. This poses a combinatorial optimization problem. In this domain, constraint programming (CP) solvers often exhibit outstanding performance by leveraging their robust problem‐modelling capabilities, excellent scalability, and precise solving capabilities. However, CP solvers may achieve various performances for different problem instances. Selecting the most suitable CP solver for each problem instance is crucial. This paper introduces an automatic selection algorithm for CP solvers to solve the cable tree wiring problem (CTW). Firstly, a scoring system is used to conduct an in‐depth analysis and compare four well‐known CP solvers: CPLEX, Chuffed, OR‐Tools, and Gurobi. The results indicate that OR‐Tools and CPLEX outperform other solvers in performance. Moreover, these two solvers exhibit complementary advantages in quickly finding optimal and feasible solutions within specified time limits. Therefore, CP and machine learning are ingeniously integrated, harnessing their complementary advantages. 4240 instances covering various scenarios are randomly generated to form the problem space. This method incorporates decision trees, random forests, K‐nearest neighbours, and naive Bayes, utilizing these four machine learning techniques. The proposed method can achieve better results than traditional single CP solvers. Among all the evaluated machining learning techniques, the automatic solver selection methods based on decision trees and random forests can achieve accuracy rates of 91.29% and 84.15%, respectively. [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 paper studies generative adversarial networks (GANs) from the perspective of statistical inference. A GAN is a popular machine learning method in which the parameters of two neural networks, a generator and a discriminator, are estimated to solve a particular minimax problem. This minimax problem typically has a multitude of solutions and the focus of this paper are the statistical properties of these solutions. We address two key statistical issues for the generator and discriminator network parameters, consistent estimation and confidence sets. We first show that the set of solutions to the sample GAN problem is a (Hausdorff) consistent estimator of the set of solutions to the corresponding population GAN problem. We then devise a computationally intensive procedure to form confidence sets and show that these sets contain the population GAN solutions with the desired coverage probability. Small numerical experiments and a Monte Carlo study illustrate our results and verify our theoretical findings. We also show that our results apply in general minimax problems that may be nonconvex, nonconcave, and have multiple solutions. [ABSTRACT FROM AUTHOR]

Wind energy is one of the most significant renewable sources of energy while accurate and reliable wind power forecasting methods may greatly benefit power system planning and scheduling. Recently, many machine learning algorithms have shown significant advantages in how to extract temporal features for wind power forecasting. However, wind power curves in the time domain frequently display intermittent features and significant uncertainty, which is not favorable to precise and reliable forecasting. In this paper, the Diffusion and Channel Attention‐based Wind Power Forecasting (DC‐WPF) framework is proposed, which transforms wind power data into the frequency domain while applying advanced channel attention techniques to aid the model in capturing the frequency domain information and ultimately enhancing accuracy. With high‐accuracy results, DC‐WPF then proposes a diffusion‐based framework to transform the point forecasting results into probabilistic forecasts to capture the uncertainty. Finally, extensive experiments on six wind power plants show that our method can improve the point forecasting accuracy of wind power and provide advanced probabilistic forecasts at a multi‐time scale. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) modules are prone to short circuits, open circuits, cracks, which can bring serious harmful effects. It is difficult to establish the corresponding PV fault models to diagnose the status of PV strings. The paper proposes a machine learning‐based stacking classifier (MLSC) for accurate fault diagnosis of PV strings. Specifically, for the operating state of PV modules, the parameter sensitivity algorithm is used to analyze the impact of characteristic factors on the characteristics of PV modules. Then based on the characteristic factors (irradiance, temperature, current, and power), MLSC is proposed to realize the accurate fault diagnosis of PV strings. This structure of MLSC is to integrate all kinds of classifiers by stacking to play the characteristics of each classifier. It combines the characteristics of various types of machine learning algorithms to improve the overall classification effect by using the advantages of each classifier. Finally, experiments reveal that MLSC improves the accuracy of PV fault diagnosis. [ABSTRACT FROM AUTHOR]

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot. Plain Language Summary: In the past several years, machine learning and its subfield, deep learning, have attracted considerable interest. Computer vision, natural language processing, and social network analysis make extensive use of machine learning algorithms. Recent applications of these algorithms include the prediction of solar flares and the forecasting of geomagnetic indices. In this paper, we propose an innovative machine learning method that utilizes a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters to provide short‐term predictions of the SYM‐H index. In addition, we present techniques for quantifying both data and model uncertainties in the output of the proposed method. Key Points: SYMHnet is a novel deep learning method for making short‐term predictions of the SYM‐H index (1 or 2 hr in advance)With Bayesian inference, SYMHnet can quantify both aleatoric (data) and epistemic (model) uncertainties in making its predictionSYMHnet generally performs better than related machine learning methods for SYM‐H forecasting [ABSTRACT FROM AUTHOR]

Thermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low‐Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re‐entry predictions, orbital lifetime analysis, and space object cataloging. In this paper, we investigate the prediction accuracy of empirical density models (e.g., NRLMSISE‐00 and JB‐08) against black‐box machine learning (ML) models trained on precise orbit determination‐derived thermospheric density data (from CHAMP, GOCE, GRACE, SWARM‐A/B satellites). We show that by using the same inputs, the ML models we designed are capable of consistently improving the predictions with respect to state‐of‐the‐art empirical models by reducing the mean absolute percentage error (MAPE) in the thermospheric density estimation from the range of 40%–60% to approximately 20%. As a result of this work, we introduce Karman: an open‐source Python software package developed during this study. Karman provides functionalities to ingest and preprocess thermospheric density, solar irradiance, and geomagnetic input data for ML readiness. Additionally, it facilitates developing and training ML models on the aforementioned data and benchmarking their performance at different altitudes, geographic locations, times, and solar activity conditions. Through this contribution, we offer the scientific community a comprehensive tool for comparing and enhancing thermospheric density models using ML techniques. Plain Language Summary: Accurately modeling the density of the thermosphere is pivotal for spacecraft operations such as collision avoidance, re‐entry prediction, and orbital lifetime analysis. In this study, our aim is twofold. First, we want to study and compare the performance of data‐driven machine learning (ML) models in predicting thermospheric density data against standard empirical models used in the field, which are used as baseline. By training ML models using precise orbit determination‐derived satellite data, we show that they can achieve significant performance improvement compared to empirical models, with a reduction of 61% in the mean absolute percentage error. Second, we also provide the community with a shared software framework that supports the ingestion of solar irradiance, geomagnetic, and thermospheric density data, as well as a training and benchmarking framework to develop ML models. This framework allows researchers and operators to both train their ML models and to compare them at different periods of the solar cycle, geomagnetic storm conditions, geographical locations, and times. Key Points: Machine learning (ML) models can significantly outperform existing physics‐based thermospheric neutral density models on exactly the same inputsML models can improve over NRLMSISE‐00 and JB‐08 empirical density models by 61% and 39% respectively in the mean absolute percentage errorThe software allows the creation of ML‐ready data for training and benchmarking new models, supporting solar irradiance and geomagnetic data [ABSTRACT FROM AUTHOR]

Exploring methods to reconstruct the ocean interior from surface data is a crucial focus in the study of ocean processes and phenomena due to the shortage of subsurface and deep‐sea data. Nonetheless, the existing methods predominantly concentrate on either data‐driven or dynamical methodologies, with limited exploration of integrating the strengths of both approaches. To combine the advantages of these two methods for reconstructing the subsurface density field from surface data, a novel dynamics‐constrained deep operator learning network based on reduced‐order model is proposed. Encoding the mean‐squared residuals of the reduced‐order equation along with the mean‐squared error between the network outputs and targets into the loss function effectively merges the dynamical and data constraints during the training process. This integration makes the network outputs and inputs approximately satisfy a specific form of the equation, allowing for interpretability, and once the network is well‐trained, rapid reconstruction evaluation can be performed. The reduced‐order equation is established by the Galerkin projection of quasi‐geostrophic equation onto the low‐dimensional subspace identified via reduced‐basis, which explains the vertical variation of ocean density. The developed model can tackle the challenge of directly measuring subsurface potential vorticity and predicting subsurface density. Evaluation is conducted using simulation data from the Max‐Planck‐Institute ocean model, indicating that it can offer precise estimations, outperforms the purely data‐driven algorithm presented in the paper and the interior plus surface quasi‐geostrophic method, and enables model sharing across different regions. Plain Language Summary: Reconstructing the interior density of the ocean from surface data is a valuable way to make up for the lack of ocean observations. Most of the previous works are purely data‐driven or dynamics‐based methods. In order to employ the advantages of dynamic and data‐driven models at the same time, a physics‐informed deep operator learning algorithm is proposed to reconstruct the density field. The proposed method shows superior predictive performance and greater robustness when compared with the pure data‐driven and dynamics‐based methods presented in the paper. These findings underscore the potential of deep learning algorithms incorporating physical constraints for marine scientific research. Key Points: The deep operator learning algorithm based on physical constraints is proposed for density field inversionThe combination of deep learning and dynamics‐based methods provides interpretability while enabling fast density reconstructionThe proposed reconstruction framework outperforms proposed purely data‐driven algorithm and traditional dynamics algorithm [ABSTRACT FROM AUTHOR]

Forecasting of renewable energy plays a major role in deregulated power systems. The rapid change in climatic conditions poses many challenges in recent years throughout the globe to policymakers and due to this, the forecasting of solar energy has become quite difficult to forecast with conventional forecasting methods. As compared to conventional methods, machine learning algorithms have shown better accuracy due to their learning methods with uncertain parameters. The market participants such as solar photovoltaic (SPV), battery energy storage system (BESS), and thermal units undergo challenges with the optimal dispatch strategy under such uncertainties of renewable energy. In addition to the concerned integrated system, other uncertainties affect the optimal operation of the integrated system and these are line contingencies and SPV. In this paper, we have used supervised learning methods such as multilayer perceptron (MLP), recurrent neural network (RNN), and long short‐term memory (LSTM) to forecast the hourly SPV in the day‐ahead market. Among the three methods of machine learning, results show LSTM with dense has been a better forecasting method with high accuracy obtained. The role of BESS in the optimal operation of day‐ahead hourly forecasted SPV along with the hourly thermal unit dispatch in DC optimal power flow (OPF) has been investigated in the paper. The main objective of the paper is to optimally allocate the BESS in the SPV‐BESS‐Thermal unit integrated system forming an active distribution network (ADN) to minimize the operating cost under different uncertainties such as line contingencies and SPV. The hourly dispatch of thermal units, BESS, and forecasted SPV is obtained for the short‐term market. The proposed approach is validated by a modified IEEE 33 bus system and solved by mixed integer linear programming (MILP). [ABSTRACT FROM AUTHOR]

Due to the compact and complex internal structure of 110 kV three‐phase enclosed GIS disconnector, the voltage distribution between the contact breaks of each phase has an influence on the insulation performance of the GIS disconnector, and its internal insulation design is a difficult point to be solved. To solve this problem, this paper firstly establishes a three‐dimensional model of three‐phase enclosed GIS disconnecting switch and conducts a research on the distribution of electric field under a specific working condition, it is found that the outer corner of the static contact, the inner corner of the static arc contact, the inner corner of the outer end of the moving contact and the inner corner of the moving contact are the internal electric field concentration of the three‐phase enclosed GIS disconnector. Then, in order to further reduce the electric field strength and improve the insulation margin and performance, this paper proposes a method based on the combination of Extreme Learning Machine (ELM) and Multi‐objective Grasshopper Optimization Algorithm (MOGOA) to optimize the structural design of three‐phase enclosed GIS disconnector, and a ELM model is established, which takes the internal fillet radius of stationary arcing contact, the internal fillet radius of moving contact, the internal fillet radius of external end of dynamic and static contact, the radius of the outer corner of the two static side contacts and the opening distance of the grounding contact as the inputs, and takes the maximum electric field strength of the two static side contacts and the moving side contacts as outputs; Finally, the structural parameters are optimized by MOGOA algorithm. Compared to the pre‐optimization period, the electric field strengths of the bus bar static‐side contact, dynamic contact and static side contact of the B‐phase after optimization decreased from 20.2, 9.28 and 17.5 to 11.4, 6.65 and 11.5 kV/mm, respectively. The electric field strengths of the dynamic and static side contacts are significantly reduced, which is beneficial for improving the insulation performance of the three‐phase enclosed GIS disconnector. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [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]

Microplastics (MPs) are present not only in the environment but also in drinking water, food, and consumer products. These MPs being toxic, carcinogenic, endocrine disrupting, and genetic risk creators cause several diseases. Despite various approaches, the development of onsite applicable, facile, and quick MP detection methods is still challenging. Here, 3D‐plasmonic gold nanopocket (3D‐PGNP) nanoarchitecture is formed on a paper substrate for simultaneous MP filtration and detection. The paper‐based 3D‐PGNP is integrated with a syringe filter device, and then, MP‐containing solutions are injected through the syringe. Subsequent detection of the MPs using the surface‐enhanced Raman scattering (SERS) successfully identifies the MPs without pretreatment. The interface and volumetric hotspot generation of 3D‐PGNP around the captured MPs significantly improves the sensitivity, which is confirmed by finite‐difference time‐domain simulation. Then, the SERS mapping images obtained from a portable Raman spectrometer are transformed into digital signals via machine learning (ML) technique to identify and quantify the MP distribution. The developed SERS‐ML‐based MP detection method is applied for mixture MPs and for real matrix samples, demonstrating that the method provides improved accuracy. This system is expected to be used for various MPs detection and for environmentally hazardous substances, such as bacteria, viruses, and fungi. [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]

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]

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]