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This technical note describes in detail a method for associating individual sheets of blank A4 white paper from the same ream by the physical fit of machine‐cut edges. A large‐scale laboratory trial involving ~700 sheets of paper from 24 different reams (plus one spoiled sample), and more than 20,000 potential physical fits, correctly associated and sequenced 219 pairs of sheets together with a 100% empirical success rate and no false associations. The edge profile of each short machine‐cut end of a sheet of A4 paper allows us to physically fit sheets of paper from the same ream to each other and use this to predict the sequence of sheets in a set of documents. In a real‐life scenario, it may now be possible to detect the substitution or addition of a sheet in a multipage document, link documents from different sources to each other or to a common source of paper (e.g. to paper from a seized printer or from an accused's address) or to date documents. The study provides data for the application of this method in forensic casework and supports the practitioner when forming conclusions in this type of case. [ABSTRACT FROM AUTHOR]

Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems. [ABSTRACT FROM AUTHOR]

The condition assessment of oil–paper insulation is a vital issue in transformer management. To investigate the effect of moisture content on the accuracy of the prediction model, accelerated ageing experiments were performed with different initial moisture content (IMC) in laboratory condition. Two types of multi-parameter lifetime prediction models, e.g. with and without consideration of IMC, were presented for comparison purpose. The authors found that a better accuracy for its prediction results could be achieved by establishing the prediction models with the consideration of IMC. Besides, models considering IMC have better economic feasibility than that without considering IMC(non-IMC) as its total cost is cheaper according to life cycle cost (LCC) calculations. In addition, failure probability based on the proposed prediction models was also calculated as a direct indicator for evaluating the oil–paper insulation status. [ABSTRACT FROM AUTHOR]

We enjoyed reading Professor Efron's (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. Interestingly, Efron puts the pure predictive algorithms to the test in a scenario where the sample size is extremely moderate by today's standards ( I n i = 102). In the knockoff framework, one can use I any i black-box (any predictive model) to identify any kind possible association - linear or not - between a large set of explanatory variables HT ht and an outcome I Y i . This framework rigorously tests whether the conditional distribution HT ht actually depends on any of the variables I X SB j sb i without assuming any model for how I X i and I Y i are connected (Candès et al. 2018). [Extracted from the article]

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]

Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables. [ABSTRACT FROM AUTHOR]

With the diversification of users' energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%. [ABSTRACT FROM AUTHOR]

This research offers a thorough examination of Journal of Forecasting (JoF) from two angles. On the one hand, this research examines a total of 1403 publications indexed by Web of Science (WoS) from 1982 to 2019. Not only some fundamental statistics like the number of publications and citations each year, the most prolific countries/regions/authors, the highly cited countries/regions/authors, and papers are counted, but content analysis is also done to look at things like document co‐citation clusters and hot and emerging subjects. On the other hand, to illustrate the knowledge flow of JoF that underlies citation connections, this research, which is based on the citing papers of JoF publications, exposes the direction of JoF knowledge dissemination from Economics and Management (i.e., the categories where JoF belongs) to other categories. Moreover, a deeper investigation on semantic clustering of five representative categories is conducted to figure out what topics are the citing papers of JoF are most interested in and whether there is any relationship between the knowledge of these categories and the knowledge produced by JoF. The framework for journal evaluation presented in this research is novel and comprehensive. With this comprehensive perspective, we are able to look back on past accomplishments, identify historical patterns of knowledge spread, illustrate the current course of development, and partially predict future dynamics. [ABSTRACT FROM AUTHOR]

Clickjacking is a fast‐growing threat for users online. Here, an attacker program shows a user‐interface (UI) which is entirely out of context, by concealing a very sensitive UI element and rendering it in such a way that it is not visible (transparent) to the end user. The user is then tricked into clicking on the hidden element. By exploiting cameras and publishing unwanted messages, these attacks have the potential to do significant harm. Many websites still lack server‐side minimum security (eg, X‐Frame‐Options header, Content‐Security‐Policy Header, etc) and are hence susceptible to clickjacking. Additionally, client‐side defense methods fare poorly and are ineffective against advanced clickjacking attack types. This paper focuses on dealing with the detection of a possibility of a website being clickjacked. It also predicts the Level of Security of a website against a clickjacking attack and the possible security countermeasures that could be taken to avoid a clickjacking attack on the website. Testing this approach on various websites has proved effective in detecting whether or not a website is vulnerable to clickjacking. [ABSTRACT FROM AUTHOR]

A Ground Level Enhancement (GLE) event can be observed as an increase in the background of ground‐based neutron monitor observations and is often associated with an increase of >500 MeV space‐based proton flux measurements. GLE events begin as very high‐energy SEP events associated with GeV protons. For such events to be detected at sea level, proton energies must exceed about 433 MeV. Since the increased flux of such particles can be a major problem to technology in space and on Earth and pose a threat for human health, developing real time warning systems is of great importance. GLE Alert++ is a product, built by the Athens Cosmic Ray Group of the National and Kapodistrian University of Athens, that issues alerts when a GLE event starts to register and is based on ground‐based neutron monitor observations. From the space‐based approach, the HESPERIA UMASEP‐500 product, jointly developed within the framework of the EU HORIZON2020 HESPERIA project by the Universidad de Malaga, Spain and National Observatory of Athens, Greece, provides forecasts of GLE events and >500 MeV protons relying on GOES satellite Soft X‐Ray and high energy proton observations. These two products are fully integrated as federated products on the ESA SWE Portal and are provided as part of the ESA Space Safety Program Space Weather Service Network. In this paper we present how the products were built, provide examples of their outputs as seen on the ESA SWE Portal, and show how the products complement each other and how using them together can in some instances provide more information for users of these services. Plain Language Summary: High‐energy solar energetic particle events contribute to the characterization of the space environment and in extreme cases their effects can be registered on Earth. A Ground Level Enhancement (GLE) event can be observed as an increase in the background of ground‐based neutron monitor observations and is often associated with an increase of >500 MeV space‐based proton flux measurements. Such particles can have potential impacts on space systems, avionics and human health. Therefore, space‐based and ground‐based monitoring of these particles is essential, and so is having reliable real‐time warning systems. In this paper we present two space weather products that have been built for this purpose: (a) GLE Alert++, built by the Athens Cosmic Ray Group of the National and Kapodistrian University of Athens, issues alerts when a GLE event has been registered and is based on ground‐based neutron monitor observations., (b) EU HORIZON2020 HESPERIA UMASEP‐500 provides forecasts of GLE events and >500 MeV protons relying on GOES satellite Soft X‐Ray and high‐energy proton observations. HESPERIA UMASEP‐500 and GLE Alert++, available on the ESA SWE Portal, complement each other and provide space weather users with valuable added advance warnings minutes before and trustworthy alerts during GLE events. Key Points: Ground Level Enhancement (GLE) Alert++ issues alerts when a GLE event starts to register and is based on ground‐based neutron monitor observationsHESPERIA UMASEP‐500 provides forecasts of GLE events and >500 MeV protons relying on Soft X‐Ray and high‐energy proton observationsHESPERIA UMASEP‐500 and GLE Alert++ are complementary tools that enhance space weather forecasting capabilities [ABSTRACT FROM AUTHOR]

We introduce a new multivariate data set that utilizes multiple spacecraft collecting in‐situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998–2013), we identify 252 solar events (>C‐class flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly‐developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP‐I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP‐II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers). Plain Language Summary: We present a new data set that uses observations from multiple spacecraft observing the Sun and the interplanetary space around it. This data is connected to the processes that create solar energetic particles (SEPs). SEP events pose threats to both astronauts and assets in space. The data set contains 252 solar flare events that caused SEPs and 17,542 that do not. For each event, we gather information about the local space environment around the sun, such as energetic protons and electrons, the conditions of the solar wind, the magnetic field, and remote solar imaging data. We use instruments from NOAA's Geostationary Operational Environmental Satellites (GOES) and the Advanced Composition Explorer spacecraft, as well as data from the Solar Dynamic Observatory, the Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. This data set is designed to be used in machine learning (ML), with a focus on predicting the occurrence and properties of SEP events. We detail each observation obtained from publicly available sources, and the data treatment processes used to validate the reliability and usefulness for ML applications. Key Points: Machine learning oriented data set for predicting the occurance and properties of solar energetic particle eventsMultivariate remote sensing and in‐situ observationsContinuous data set spanning several decades [ABSTRACT FROM AUTHOR]

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]

Peripartum cardiomyopathy is an idiopathic cardiomyopathy presenting with heart failure secondary to left ventricular systolic dysfunction towards the end of pregnancy or in the months following delivery, where no other cause for heart failure is identified. Outcome varies from full recovery to residual left ventricular systolic dysfunction and even death. Many women return to their physician to acquire information on their long-term prognosis, to seek medical advice regarding contraception, or when planning a subsequent pregnancy. This position paper summarizes current evidence for long-term outcome, risk stratification of further pregnancies and overall management. Based on the best available evidence, as well as the clinical experience of the European Society of Cardiology Study Group on Peripartum Cardiomyopathy members, a consensus on pre- and postpartum management algorithms for women undergoing a subsequent pregnancy is presented. [ABSTRACT FROM AUTHOR]

In this work we describe the basics of technology employed to develop high efficiency red OLED panel for automotive lighting. The devices exhibit high luminance, long lifetime even at elevated temperatures and minimal color change with viewing angle. The described method of LT70 estimation can be used for accurate prediction of device operational stability at various driving conditions. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data. [ABSTRACT FROM AUTHOR]

Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output power, various meteorological data, and solar irradiation intensity of photovoltaic modules. This paper establishes the functional relationship between the output power of photovoltaic modules and the irradiation intensity through Pearson correlation analysis. By deducing the distribution relationship of irradiation intensity, the prediction model of irradiation intensity based on peak sunshine hours and sunshine duration is constructed and based on 340 sites across the country 64 years peak sunshine hours and sunshine duration query database. In this work, the theoretical value of the prediction model on sunny days is close to the measured value (R2 = 0.918–0.985). The solar radiation intensity on rainy days is weak, and the prediction accuracy is low (R2 = 0.838–0.930). The relative errors between the sunshine duration and the peak sunshine hours in the database are less than 4.55% and 4.79%, respectively, under sunny conditions in each quarter, indicating that the accuracy of the database meets the actual needs. [ABSTRACT FROM AUTHOR]

Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near‐term forecasts of water quantity and that near‐term water quality forecasts are fewer in number and in the early stages of development (i.e., non‐operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near‐term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end‐user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near‐term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management. [ABSTRACT FROM AUTHOR]

Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches. [ABSTRACT FROM AUTHOR]

Earthquake swarms are most commonly composed of small‐magnitude earthquakes. However, a recent study by Yoshida, Uchida, et al. (2023, https://doi.org/10.1029/2023GL106023) analyzed a swarm beneath the Noto Peninsula in Japan that, after more than two years of moderate‐magnitude seismicity, triggered the moment magnitude (Mw) 6.2 Suzu mainshock in May 2023. Based on high‐precision earthquake locations and a slip inversion of the mainshock, these authors found that the Mw 6.2 Suzu earthquake occurred on the updip extension of a fault that was active during the swarm, likely driven by increased fluid pressure. After publication of that paper, a much larger and more destructive Mw 7.5 event occurred nearby on 1 January 2024. These events underscore the potential for swarms to be precursors to large, damaging earthquakes. Forecasting the eventual evolution of swarms is currently very challenging but could be aided in the future by new observations and models. Plain Language Summary: Although earthquake swarms often only contain small earthquakes, occasionally they can trigger large, damaging earthquakes. Yoshida, Uchida, et al. (2023, https://doi.org/10.1029/2023GL106023) examined the triggering of a magnitude 6.2 earthquake in Japan, which caused extensive damage, following two years of smaller earthquakes. This sequence, like many swarms, may have been driven by elevated fluid pressure migrating upwards within the crust. After publication of that paper, a much larger and more destructive Mw 7.5 event occurred nearby. Forecasting the eventual evolution of ongoing swarms is currently very challenging but could be aided in the future by new observations and models. Key Points: Earthquake swarms can be precursors to damaging earthquakes, including the 1 January 2024, Mw 7.5 Noto Peninsula, Japan eventFluid pressure changes often drive swarm activity, and their degree of confinement may influence swarm evolutionDirect observations of fluids in fault zones combined with numerical modeling may aid swarm forecasting in the future [ABSTRACT FROM AUTHOR]

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine‐learning (ML) and deep‐learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non‐invasive treatment to the patients. [ABSTRACT FROM AUTHOR]

This paper investigates the multi‐dimensional non‐linear exponential smoothing prediction method under different weight coefficients, so as to realize the short‐term prediction of the original wind power output. Based on the current research on wind power fluctuation prediction, this paper firstly proposes a new multi‐dimensional nonlinear exponential smoothing prediction method. Based on different weight coefficients, the corresponding short‐term forecast volatility is obtained. Then, the wavelet packet decomposition method is used to further realize the secondary smoothing of the prediction wind power fluctuations. Hybrid energy storage system (HESS) plays an important role in wind power fluctuation suppression from different frequency bands. However, the traditional fixed frequency power allocation method significantly degrades the performance of the accuracy of the algorithm. Therefore, this paper proposes a frequency conversion entropy strategy, which combines energy value with wavelet packet decomposition. By calculating the energy components of different frequency bands, the fluctuation can be divided effectively. Finally, simulation and experiment discussions show that the proposed algorithm in this paper can realize the power allocation effectively compared to the traditional one. And it further verifies the feasibility and reliability of the strategy proposed in this work. [ABSTRACT FROM AUTHOR]

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi‐evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long‐term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting. [ABSTRACT FROM AUTHOR]

This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. Worked examples are included for both cases. This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. [ABSTRACT FROM AUTHOR]

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods. Plain Language Summary: This paper proposes a functional quantile autoregressive model of order one, which is used to predict the entire distribution of the realizations of river flow time series curve. The proposed model allows modeling the conditional quantiles of the response variable as a function of its past values of it. The proposed method for historical river flow curves is an excellent alternative to existing mean regression methods at the 0.5 quantile level (median regression). Also, as an advantage over existing methods, it offers a more thorough explanation of the connection among previous and future realizations of river flow curves at various quantile levels, providing a more extensive understanding of the relationship. Moreover, this feature of the proposed method allows for the effortless generation of pointwise prediction intervals for future realizations of river flow curves. The numerical results obtained by Monte Carlo experiments and empirical data analyses exhibit that, compared with existing methods, the proposed method produces competitive or even better forecasting results. The results also indicate that the future realizations of the river flow measurements are well covered by the prediction intervals constructed by the proposed method. Key Points: Predicting the mean and extreme values of the river flow curve is important for various applications in water resources managementThe FQAR(1) allows predicting the entire distribution of future realizations of the river flow curve as a function of its past values of itNumerical results based on river flow measurements collected from the Australia Continent confirmed the potential of the FQAR(1) [ABSTRACT FROM AUTHOR]

Various scenario classifications apply in an attempt to make the field of future studies easier to outline. This paper discusses the appropriateness of various creating, coordinating, and consistency procedures for creating specific emission scenarios in environmental assessments. The intuitive logics, story and simulation (SAS), social, technological, economic, environmental and political (STEEP), and La prospective models approaches are the five emission scenarios discussed in the paper analytically with their stages, storylines, steps, and building analysis. Every scenario method was examined and sorted into its separate benefits and limitations. Subsequently, the study selected the appropriate technique of emission scenario in environmental assessment with fulfilled scenario planning to deliver constructive scenarios. The study helps the aspiring designers by providing the fundamental guidance on the extensive change toward a sustainable environment. [ABSTRACT FROM AUTHOR]

Here we will comment on how one can make use of the newest developments in quantum theory, implemented in the ORCA software, to predict excited state lifetimes from scratch. Starting only from the Lewis structure of a given molecule, now it is possible to compute or estimate its photophysical rates and even emission color of OLEDs. [ABSTRACT FROM AUTHOR]

An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is based on spatio‐temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super‐parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread‐F. Plain Language Summary: Since the clear trace indicates the ionospheric electron density background and the Spread‐F indicates disturbances/irregularities, the prediction of both ionograms with/without Spread‐F is important to the research and application. There are currently no well‐established neural networks specifically designed to predict the extensive information encompassed within ionograms, particularly the intricate characteristics of Spread‐F. In this paper, we approach the short‐term prediction (next 30 min) of ionograms with/without Spread‐F. To achieve this, we employ the ConvGRU network to generate ionograms blurred but still captured the primary spatio‐temporal features of various types of Spread‐F (FSF/RSF/MSF/SSF) in ionogram sequences, as well as features without Spread‐F. Subsequently, we refine these rough images by EDSR network to obtain clear and detailed predictions. Our treatment to blurred prediction figures and our focus on the Spread‐F key area are innovative. Through our work, the auto‐prediction for ionosonde observation benefits ionosphere research and monitoring. Key Points: Rather than numerical prediction methods, our prediction is based on spatio‐temporal features onlyPredicting high‐definition ionograms by combining ConvGRU and EDSR on the basis of establishing two ionogram data setsAchieving a high accuracy of 94.28% for Spread‐F prediction [ABSTRACT FROM AUTHOR]

The image‐to‐video (I2V) person re‐identification (Re‐ID) is a cross‐modality pedestrian retrieval task, whose crux is to reduce the large modality discrepancy between images and videos. To this end, this paper proposes to predict the following video frames from a single image. Thus, the I2V person Re‐ID can be transformed to video‐to‐video (V2V) Re‐ID. Considering that predicting video frames from a single image is an ill‐posed problem, this paper proposes two strategies to improve the quality of the predicted videos. First, a pose‐guided video prediction pipeline is proposed. The given single image and pedestrian pose are encoded via image encoder and pose encoder, respectively; then, the image feature and pose feature are concatenated as the input of the video decoder. The authors minimize the difference between the predicted video and true video, and simultaneously minimize the difference between the true pose and predicted pose. Second, the conditional adversarial training strategy is employed to generate high‐quality video frames. Specifically, the discriminator takes the source image as condition and distinguishes whether the input frames are fake or true following frames of the source image. Experimental results demonstrate that the pose‐guided adversarial video prediction can effectively improve accuracy of I2V Re‐ID. [ABSTRACT FROM AUTHOR]

The sharp rise in inflation during the past few years has engendered comparisons with the great inflation of the 1970s and early 1980s. Like that earlier episode, the recent inflation has been underpinned by supply‐side shocks and, in the case of the United States, by highly accommodative fiscal and monetary policies. Like that early episode, the recent inflation has been largely unpredicted; it caught US policy makers by surprise, despite the fact that several prominent economists had warned early on that fiscal and monetary policies were setting the stage for an upsurge in inflation. To motivate this special issue, we provide a brief account of the policy miscues and policy debates that resulted from the large forecast errors in predicting the upsurge in inflation in 2021 and 2022. We then provide an overview of the papers in this issue, which address a range of methodological and empirical issues related to forecasting and highlight the dilemmas faced by agents within the economy and policy makers. [ABSTRACT FROM AUTHOR]

With the rapid development of globalization economy, countries in the world are more and more closely connected in business, and international import and export trade has also achieved rapid development. Based on the analysis of the status quo of the country's import and export trade, this article combines the main national economic indicators to analyze and study the algorithm of import and export trade forecasting. RVM is a sparse probability model and a new supervised learning method. Firstly, this paper introduces the protection system of block chain technology for import and export trade data information. Then, a hybrid RVM model is constructed, which is optimized by PSO algorithm, and the import and export volume is predicted based on this model. Based on the analysis of the data, it is tested that the security protection coefficient of the blockchain security technology for import and export trade data can reach up to 99.9%, and the accuracy of the import and export trade forecast based on the PSO optimized hybrid RVM model can reach up to 85.79%. A series of experiments show that the import and export trade forecast model studied in this paper can accurately predict the volume of import and export trade, thus providing a new method for the country's import and export trade, with high practical significance. [ABSTRACT FROM AUTHOR]

This paper proposes an interpretation of Aristotle's understanding of tyche (τύχη), a Greek term that can be alternatively translated as luck, fortune, or fate. The paper disentangles various threads of argument in the primary sources to argue for a realist understanding of what we moderns call "luck." In short, it contends that Aristotle's account of these issues is mostly correct and merits close attention when canvassing recent philosophical debates about luckology. Aristotle argues that science pertains to the general rule; it is not about the particular. Particular events have contingent content that exceeds the scope of science. Even if we could predict all future events with ultimate accuracy, we would still be left wondering why good or bad things happen to specific people. Although luck is not a scientific category, it has an existential reality that leaves momentous events open to metaphysical and even religious interpretation. [ABSTRACT FROM AUTHOR]

UK top income shares have varied hugely over the past two centuries, ranging from more than 30% to less than 7% of pre‐tax national income allocated to the top 1 percentile. We build a congruent dynamic linear regression model of the top 1% income share allowing for economic, political and social factors. Saturation estimation is used to model outliers and trend breaks, proxying underlying structural changes driving income inequality in the UK. We use the model to forecast the top 1% income share over the last 15 years, and compare to a range of forecast devices. Despite a well‐specified constant parameter model conditioning on significant explanatory variables, the best performing forecasts are obtained from a random walk and a smoothed random walk. These results are explained by the presence of shifts in the income share over the forecast period, resulting in forecasts from equilibrium correction models converging to the wrong equilibrium. Our best prediction for 2026 based on the most recent data from 2021 (a 5‐year ahead projection) is that the pre‐tax top 1% income share will remain at the most recent realized value of 12.7%, but there is a large degree of uncertainty, with a 95% confidence band ranging from 10% to 15.7%. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. David Forbes Hendry received his MSc and PhD from the LSE. [ABSTRACT FROM AUTHOR]

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest. Plain Language Summary: As low earth orbit has become crowded with new satellites and debris, operators have been forced to maneuver satellites to avoid collisions on a regular basis. The drag force on a satellite, which can significantly affect the orbital path, varies depending on solar and geomagnetic activity. Unfortunately, solar activity and resulting impacts at Earth are difficult to predict even just a few days in advance. This paper traces space weather forecasting ability directly to impacts on satellite maneuver decision‐making and finds that forecast uncertainty plays a critical role in making durable maneuver decisions. Key Points: Poor forecasts of space weather drivers inhibit our ability to perform actionable satellite conjunction assessment with advance noticeClimatology‐based probabilistic forecasts are developed for F10.7 and Kp, which are used to assess satellite conjunction scenariosProviding probabilistic forecasts of the space weather drivers makes a meaningful difference in maneuver decision‐making [ABSTRACT FROM AUTHOR]

The rapid expansion of low‐voltage distributed photovoltaic (PV) systems with decentralized layouts poses significant data collection challenges. The scarcity of data amplifies prediction complexities, affecting the operational security and stability of distribution networks. To enhance predictive accuracy for distributed regional PV power generation, including unmonitored low‐voltage systems, this paper proposes a novel prediction approach that combines weight optimization and transfer learning. Firstly, to more accurately extrapolate from neighbouring monitored PV to estimate unmonitored PV outputs, a novel SSA‐MLP‐XGBoost model is proposed for monitored PV power plants, in which the hyperparameters of multilayer perceptron (MLP) and (extreme gradient boosting) XGBoost models are fine‐tuned using the sparrow search algorithm (SSA), and the optimized models are synergistically integrated to heighten prediction precision for monitored plants. Secondly, based on the predicted power curves of monitored PV power plants, a weight optimization model is presented to optimize the combined weights between unmonitored and monitored power stations. Furthermore, the power generation model from monitored facilities is transplanted to unmonitored plants for power generation estimation. Ultimately, the authors combine the weight optimization and transfer learning model to get more accurate and robust model. Validation across three regional distributed photovoltaic clusters demonstrates a noteworthy improvement in prediction accuracy—20%, 7.5%, and 25% for the respective clusters compared to the existing methods. [ABSTRACT FROM AUTHOR]

Refresh rate is an important specification for gaming OLED monitors. With the development of the gaming industry and the graphics processing unit (GPU), the demand for gaming monitors that supporting high refresh rates is increasing. In this paper, simulation methods for perceived motion blur are proposed to predict the degree of blur according to the refresh rate. Experimental results indicate that the proposed simulation methods are quite effective in predicting the degree of motion blur. In addition, this paper presents the predicted results of blur as the refresh rate increases up to 480Hz. [ABSTRACT FROM AUTHOR]

This paper proposes the time‐heterogeneous Student's t autoregressive model as an alternative to the various volatility forecast models documented in the literature. The empirical results indicate that: (i) the proposed model has better forecasting performance than other commonly used models, and (ii) the problem of reliable risk measurement arises primarily from the model risk associated with risk forecast models rather than the particular risk measure for computing risk. Based on the results, the paper makes recommendations to regulators and practitioners. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of two artificial neural networks (ANN) models, utilising time and power‐based architectures, to accurately predict key parameters of a hydrogen hybrid train profile and its optimal trajectory. The research employs a hybrid train simulator (HTS) to authenticate the ANN models, which were trained using simulated trajectories from five unique hybrid trains on a designated route. The models' performance was evaluated by computing the mean square normalisation error and mean absolute performance error, while the output's reliability was confirmed through the HTS. The results indicate that both ANN models proficiently predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade‐forward neural network (CFNN) topology in the time‐based architecture surpasses the feed‐forward neural network (FFNN) topology concerning mean squared error (MSE) and maximum error in the power‐based architecture. Specifically, the CFNN topology within the time‐based structure exhibits a slightly lower MSE and maximum error than its power‐based counterpart. Additionally, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time‐based architecture exhibits lower differences (0.18% and 0.85%) compared to the power‐based architecture (0.46% and 0.92%). [ABSTRACT FROM AUTHOR]

Previous studies suggested that Atlantic Multidecadal Variability (AMV) modulations on pan‐Pacific sea surface temperature (SST) variability and prediction are model‐dependent. These results were mainly based on SST forcing experiments in which AMV‐related Atlantic SST anomalies were prescribed. However, the AMV itself is also model‐dependent, but its influences on the Pacific remain unclear. Here, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with observations, to study the model‐dependent AMV trans‐basin effects. We found that AMV strength is a key factor: Stronger (Weaker) model AMV than observations overestimates (underestimates) SST response and decadal prediction skills, mainly in the North Pacific. The reason is that stronger positive phased AMV, for example, leads to higher sea level pressure anomalies over the North Pacific, which lifts sea surface height and deepens thermocline to warm SST. Our study highlights the necessity to improve simulations of AMV strength. Plain Language Summary: Pacific sea surface temperature (SST) decadal variability and prediction are important for economy and environment. The Atlantic Multidecadal Variability (AMV) was thought to significantly influence Pacific SST variability and prediction. However, the results of previous studies were model‐dependent. In this paper, we use fully coupled experiments of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to study what contributes to the model dependency. We found that the strength of the AMV is a key factor influencing AMV trans‐basin modulations on SST variability and decadal prediction, mainly in the North Pacific Ocean. The related mechanisms are discussed. Our paper sheds light on the way to improve North Pacific SST decadal prediction. Key Points: Climate models show a strong inter‐model spread of Atlantic Multidecadal Variability (AMV) strengthModels with stronger (weaker) AMV than observations overestimate (underestimate) North Pacific sea surface temperature (SST) response and decadal predictionThe North Pacific SST response is primarily forced by AMV‐induced wind stress curl and ocean dynamics [ABSTRACT FROM AUTHOR]

Ensemble climatological forecasts play a critical part in benchmarking the predictive performance of hydroclimatic forecasts. Accounting for the skewness and censoring characteristics of hydroclimatic variables, ensemble climatological forecasts can be generated by the log, Box‐Cox and log‐sinh transformations, by the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and by the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. This paper is concentrated on the reliability of the 12 types of ensemble climatological forecasts. Specifically, mathematical formulations are presented and large‐sample tests are devised to verify the forecast reliability for the Multi‐Source Weighted‐Ensemble Precipitation version 2 across the globe. Climatological forecasts of monthly precipitation over 18,425 grid cells are generated for 30 years under leave‐one‐year‐out cross validation, leading to 6,633,000 (12 × 18425 × 30) sets of ensemble climatological forecasts. The results point out that the reliability of climatological forecasts considerably varies across the 12 methods, particularly in regions with high hydroclimatic variability. One observation is that climatological forecasts tend to deviate from the distributions of observations when there is inadequate flexibility to fit precipitation data. Another observation is that ensemble spreads can be overly wide when there exist overfits of sample‐specific noises in cross validation. Through the tests of global precipitation, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Overall, the investigations can serve as a guidance on the uses of transformations, distributions and non‐parametric methods in generating climatological forecasts. Plain Language Summary: Ensemble climatological forecasts have been extensively used as the benchmark to evaluate forecast skill. That is, forecasts generated by a certain forecasting model are skillful when they outperform climatological forecasts and otherwise they are not. In practice, ensemble climatological forecasts are generated by different methods, including the log, Box‐Cox and log‐sinh transformations, the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. It is important to investigate pros and cons of different types of climatological forecasts. Focusing on the reliability, that is, statistical consistency between forecasts and observations, this paper has devised large‐sample tests of global monthly precipitation. The results show that owing to hydroclimatic variability, different types of climatological forecasts exhibit varying characteristics of reliability. On the one hand, climatological forecasts can deviate from observations when there is inadequate flexibility to fit precipitation data, especially for the Bernoulli‐Gaussian distribution. On the other hand, ensemble spreads can be too wide when there exist overfits of sample‐specific noises in cross validation. Among the 12 methods, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Key Points: Climatological forecasts can be generated by using data transformations, statistical distributions and non‐parametric methodsThe reliability of climatological forecasts generated by different methods is shown to vary considerably in large‐sample testsThe robustness of log‐sinh transformation and Bernoulli‐Gamma distribution is illustrated through the tests of global precipitation [ABSTRACT FROM AUTHOR]

Photovoltaics (PV) output prediction, which is indispensable for power system operation, can affects demand and supply adjustment adversely when large prediction error occurs. Thus, the reduction of large error as well as average error is required in PV power prediction. In 2019, the operation of the Meso‐scale Ensemble Prediction System (MEPS) of numerical weather prediction started from the JapanMeteorological Agency, and the amount of forecasting information would be potentially useful for the improvement of PV power prediction. However, very few studies on inputting multiple meteorological elements of the MEPS have been reported. In this paper, we newly develop the prediction model for an area day‐ahead PV power output composed of Just‐In‐Time Modeling (JIT Modeling) with multiple elements of theMEPS. The developed method achieves precise forecasts with low computational load by both selecting meteorological elements valid for improving prediction accuracy and adequately devising the structure of JIT Modeling. Some numerical examples demonstrating the effectiveness of the developed method are also presented. In particular, the proposed method reduces large error significantly. [ABSTRACT FROM AUTHOR]

The main objective of this review paper is twofold: Recall the most captivating areas of research in predicting the fatigue life of metals with the application of thermal methodology, particularly for the stress‐controlled fatigue tests. The explicit lifetime models reviewed have been developed by scholars over the past two decades owing to the advancements in infrared thermal imaging technology.Introduce, discuss, and conclude a broad range of alternative theoretical frameworks in thermodynamics. Some investigations are made with the previous methods, including the most recent evolutions of lifetime models for structural integrity across various fields. Some future perspectives are provided in final, which could pave the way for a new realistic or potential capability. Highlights: The first paper to review thermo‐based methodologies for predicting fatigue life.Literature results are introduced, and organized to be straightforwardly accessible.Perspectives can offer novel and practical prospects and potential capacities. [ABSTRACT FROM AUTHOR]

In this paper, we propose data‐driven control for the case of that the structure of the ideal controller is unknown. In this case, it is natural to implement FIR typed controller. Here, we utilize FRIT for the case of FIR typed inverse controller. In general, FIR might have unnecessarily large number of its taps. To prevent it, we also utilize LASSO regression so as to realize FIR typed inverse controller in as less numbers of the tap as possible. Moreover, we also utilize data‐driven prediction to determine hyper parameters required in LASSO regression. The validity of our proposed method is verified by using experiments. [ABSTRACT FROM AUTHOR]

It is of great importance to build an accurate model for the multi‐step forecasting of household power consumption. In recent years, more and more researchers have focused on adopting hybrid models to execute forecasting due to the irregularity and nonlinearity of power data. However, existing forecasting models usually make predictions directly on original data. This paper introduces secondary decomposition algorithm used to decompose primal data. We use singular spectrum analysis (SSA) to decompose the original series into several subseries, utilize variational mode decomposition (VMD) optimized by whale optimization algorithm to decompose the subseries with the highest frequency into several intrinsic mode functions. Then all subseries obtained from SSA and VMD are fed into long short‐term memory model to get predictions. In order to confirm the validity of proposed model, this paper performs several experimental analyses. The results of experiments show that the proposed model effectively improves the accuracy of forecasting. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM. [ABSTRACT FROM AUTHOR]

Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The energy demand of the auxiliaries of battery electric vehicles can account for a significant share of the total energy demand of a trip and must be taken into account for the prediction of the vehicle's remaining driving range or the implementation of predictive driving functions. This paper investigates a method that uses system identification and neural networks with bidirectional long short‐term memory layers to predict the power requirements of the auxiliaries depending on information that is known prior to the trip. By using a self‐learning, data‐driven approach as well as data that can be measured without additional instrumentation, a prediction is made possible without the need to design detailed physical models in advance. Additionally, a rule‐based allocation of the training data based on environmental conditions is implemented, which serves to adapt individual models to different climatic modes of the thermal system. The potential of the method is demonstrated for three different systems showing a prediction accuracy of on average 3% to 8% in terms of energy, while the deviation of the predicted power consumption is on average about 500 watts. Due to the complete automation of the process, a further increase in prediction accuracy can be expected. [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]

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]