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Introduction: The "future of work" refers to workplace modifications projected to occur following large scale events impacting workforce health and safety such as pandemics, technological advancements, or economic shifts. This paper examines the influence of the COVID-19 pandemic on the future of work., Methods: The electronic databases PubMed, Google Scholar, and Scopus were explored using search terms such as "post-pandemic," "working force," and "future of work" to retrieve papers published between 2000 and 2023., Results: The main characteristics of the future of work modification revealed are blurring of work-home boundaries, increase in telework and the gig economy, demand for artificial intelligence, smart working, quiet hiring, diversity and inclusion, and algocratic governance. Needed skills include virtual collaboration capability, cross-cultural competency, computational thinking, news media literacy, transdisciplinarity, sense making, and focusing on mental health and well-being., Conclusions: Organizations are remodeling workforces to meet employee, employer, regulatory, and market expectations to adapt to future workplace needs., Competing Interests: Conflict of interest: None declared., (Copyright © 2024 American College of Occupational and Environmental Medicine.)

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making., (© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)

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]

Radiological imaging is a crucial diagnostic tool for the pediatric population. However, it is associated with several unique challenges in this age group compared to adults. These challenges mainly come from the fact that children are not small-sized adults and differ in development, anatomy, physiology, and pathology compared to adults. This paper reviews relevant articles published between January 2015 and October 2023 to analyze challenges associated with imaging technologies currently used in pediatric radiology, emerging technologies, and their role in resolving the challenges and future prospects of pediatric radiology. In recent decades, imaging technologies have advanced rapidly, developing advanced ultrasound, computed tomography, magnetic resonance, nuclear imaging, teleradiology, artificial intelligence, machine learning, three-dimensional printing, radiomics, and radiogenomics, among many others. By prioritizing the unique needs of pediatric patients while developing such technologies, we can significantly alleviate the challenges faced in pediatric radiology., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables., Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R 2  = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82)., Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Paper, a porous-fibrous network, is made up of hydrophilic fibres, which are notably susceptible to deformations due to variations in moisture content. The response of paper sheets to full or partial wetting, based on the water-induced fibre swelling, has mostly been predicted in the past using highly idealised two-dimensional network geometries. The purpose of the present paper is to establish how precisely the two-stage process of determining effective hygro-expansivity under uniform wetting by a two-dimensional model followed by bending analysis using a continuum model predicts the curl of a fully three-dimensional network. In the present multi-scale modelling work, the moisture-induced homogenised sheet scale curl, derived from hygro-elastic deformations using non-uniform moisture profiles, is analysed based on an idealised non-woven three-dimensional fibrous network finite element model. First, the effective paper sheet swelling properties, derived computationally for the uniform wetting condition, are compared with analytically homogenised hygro-elastic properties. Whereas the analytically predicted elastic modulus has an inaccuracy of only ∼ 5%, the hygro-expansivity coefficient is over-predicted by roughly a third for a network with straight fibres. The difference is smaller if waviness of the fibres and wrap-around in the fibre bonds are taken into account. Fibre alignment turns out to have little influence. Subsequently, paper sheet curl predictions made by the analytical model are compared with that computed from the three-dimensional network model. The inaccuracy of the fully analytical model, based on the two-dimensional network description, relative to the fully detailed three-dimensional computational model with straight fibres is on the order of 40%–50%. The fidelity of the simple model is primarily constrained by the inaccuracy of the effective hygro-expansion coefficient. If the expansivity extracted from the three-dimensional simulations under uniform wetting is inserted, the inaccuracy drops to less than 10%. The continuum bending description hence performs adequately, provided it is based on a reliable estimation of the uniform expansion behaviour of the material. • Hygroscopic curl of paper sheets may be predicted using homogenised models. • We assess the accuracy of such predictions. • As a reference simulations of an idealised three-dimensional fibre network are used. • The predictions are accurate provided an accurate hygro-expansivity is used. • Two-dimensional network models may not be sufficient for this. [ABSTRACT FROM AUTHOR]

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6 ∘ C to almost zero, while the daily mean absolute error decreased from 5 ∘ C to 3 ∘ C for the neural network and 3.5 ∘ C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day., (© 2024. The Author(s).)

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)

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]

While the Consumer Price Index (CPI) has its flaws, I chose to analyze it rather than any other data because it is an essential component for both businesses and consumers to make informed economic decisions as it indicates the change in consumers purchasing power. It is a standard used to guide nationwide economic decisions of government, business, labor, and private citizens. This paper assesses how well ARIMA models perform in different economic regimes to give a historical factor to these functions. This is important because if one uses an ARIMA model to make forecasts, one needs to be aware of how it behaves in different market conditions. This is not a prediction, as the cluster year is required beforehand. This paper investigates different economic regimes using autoregressive integrated moving average (ARIMA) models and a K-means clustering model. In this paper, I utilized the K-means model to first identify clusters of similar years of data. Then, I was able to apply that information to the ARIMA model to compare actual and predicted economic data for similar years (determined by the K-means clustering). The reason for applying this model to years with the same cluster label was to avoid massive fluctuations in economic data across an expansive range of years (1922 to 2022) that might have confused the model. [ABSTRACT FROM AUTHOR]

This paper focuses on crime status prediction through an ensemble methodology applied to extensive datasets obtained from catalog.data.gov, specifically targeting Los Angeles crime incidents since 2020. The research methodology comprises meticulous data collection, rigorous preprocessing, exploratory data analysis, model selection, and comprehensive model evaluation. Initial challenges included data inaccuracies and privacy-preserving measures in location data, necessitating thorough cleaning and transformation processes. Exploratory data analysis revealed crucial insights, including the 'Status' attribute's limited correlation, crime code distributions, areawise crime counts, and temporal patterns. To address class imbalance within 'Status', the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. Model evaluation highlighted the superiority of random forest models employing 10 and 20 decision trees, alongside KNN, which demonstrated consistent high accuracy, balanced precision-recall trade-offs, and notable F1 scores in crime status prediction. [ABSTRACT FROM AUTHOR]

In recent years, the global installed capacity of wind power has grown rapidly. Wind power forecasting, as a key technology in wind turbine systems, has received widespread attention and extensive research. However, existing studies typically focus on the power prediction of individual devices. In the context of multi-turbine scenarios, employing individual models for each device may introduce challenges, encompassing data dilution and a substantial number of model parameters in power generation forecasting tasks. In this paper, a single-model method suitable for multi- device wind power forecasting is proposed. Firstly, this method allocates multidimensional random vectors to each device. Then, it utilizes space embedding techniques to iteratively evolve the random vectors into representative vectors corresponding to each device. Finally, the temporal features are concatenated with the corresponding representative vectors and inputted into the model, enabling the single model to accomplish multi-device wind power forecasting task based on device discrimination. Experimental results demonstrate that our method not only solves the data dilution issue and significantly reduces the number of model parameters but also maintains better predictive performance. Future research could focus on using more interpretable space embedding techniques to observe representation vectors of wind turbine equipment and further explore their semantic features. [ABSTRACT FROM AUTHOR]

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [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]

Most of the deep-learning algorithms on stock price volatility prediction in the existing literature use data such as same-frequency market indicators or technical indicators, and less consider mixed-frequency data, such as macro-data. Compared with the traditional model that only inputs the same-frequency data such as technical indicators and market indicators, this study proposes an improved deep-learning model based on mixed-frequency big data. This paper first introduces the reserve restricted mixed-frequency data sampling (RR-MIDAS) model to deal with the mixed-frequency data and, secondly, extracts the temporal and spatial features of volatility series by using the parallel model of CNN-LSTM and LSTM, and finally utilizes the Optuna framework for hyper-parameter optimization to achieve volatility prediction. For the deep-learning model with mixed-frequency data, its RMSE, MAE, MSLE, MAPE, SMAPE, and QLIKE are reduced by 18.25%, 14.91%, 30.00%, 12.85%, 13.74%, and 23.42%, respectively. This paper provides a more accurate and robust method for forecasting the realized volatility of stock prices under mixed-frequency data. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method generally outperformed the other baseline models in all four seasons of the year according to three performance assessment metrics: the normalized root mean square error (RMSE), normalized mean absolute error (MAE) and coefficient of determination (R2). The best RMSE, MAE and R2 reached 0.0195, 0.0128 and 99.72%, respectively, with maximum improvements of 11.32%, 8.57% and 0.38%, respectively, over those of the suboptimal model. Therefore, the model proposed in this paper is effective at improving prediction accuracy. Using the proposed method, this paper concludes with multistep predictions of 3, 6, and 9 steps, which also indicates that the proposed method significantly outperforms the other models. [ABSTRACT FROM AUTHOR]

With the high yield of many wells represented by Well JT1 in the Maokou Formation, has catalyzed a surge in exploration activities along the platform margin facies of the Maokou Formation in central Sichuan and further showed the significant exploration potential of the Maokou Formation in the northern slope. However, the fracture cave body of the Maokou Formation exhibits a high degree of development, strong longitudinal and horizontal heterogeneity, large formation pressure differences, and drilling events such as gas kicks and lost circulation occur frequently, which seriously affects the efficient implementation of drilling. Understanding the spatial distribution of the three-pressure in the formation can help better deal with and solve the above problems. Therefore, in order to help the safe, high-quality and rapid drilling of the Maokou Formation in the study area, and enhance the efficiency of oil and gas development, this paper explores the research on the prediction method of the three-pressure of jointing well-seismic data based on the geomechanical experimental data and the actual drilling data. In the process of prediction of pore pressure, this study found that the pore pressure and formation velocity in the study area have an exponential relationship. In order to enhance the applicability of the Filippone's method in the study area and improve the prediction accuracy of pore pressure, the linear relationship between pore pressure and formation velocity in the Filippone's method is modified to an exponential relationship, and a pore pressure prediction model suitable for the work area was established. Based on the Mohr–Coulomb criterion and Huang's model, the prediction models of collapse pressure and fracture pressure applicable to the study area were established, respectively. Then, the elastic parameters were obtained through pre-stack inversion, and the three-pressure bodies were calculated based on the elastic parameter bodies. The results indicate that: (1) The three-pressure prediction method of the jointing well-seismic data in this paper can predict the formation's longitudinal and transverse pressure anomaly zones in advance. (2) The Maokou Formation in the study area is characterized by abnormally high pressure, to balance the pressure of the high-ground formation, high-density drilling fluid is necessary. (3) The prediction results of three-pressure in this paper are highly consistent with the actual drilling engineering events, which verifies the reliability of the three-pressure prediction results presented in this study. The results of the study can provide a basis for decision-making in drilling geological design, such as the determination of drilling fluid density, the evaluation of borehole stability and other engineering problems that require support from three-pressure data. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) have been widely used in ocean missions. When they fail in the ocean, it is important to predict their trajectory. Existing methods rely heavily on historical trajectory data while overlooking the influence of the ocean environment on an AUV's trajectory. At the same time, these methods fail to use the dependency between variables in the trajectory. To address these challenges, this paper proposes an AUV trajectory prediction model known as the nonlinear Kepler optimization algorithm–bidirectional long short-term memory–time-variable attention (NKOA-BiLSTM-TVA) model. This paper introduces opposition-based learning during the initialization process of the KOA and improves the algorithm by incorporating a nonlinear factor into the planet position update process. We designed an attention mechanism layer that spans both time and variable dimensions, called TVA. TVA can extract features from both the time and variable dimensions of the trajectory and use the dependency between trajectory variables to predict the trajectory. First, the model uses a convolutional neural network (CNN) to extract spatial features from the trajectory. Next, it combines a BiLSTM network with TVA to predict the AUV's trajectory. Finally, the improved NKOA is used to optimize the model's hyperparameters. Experimental results show that the NKOA-BiLSTM-TVA model has an excellent parameter optimization effect and higher prediction accuracy in AUV trajectory prediction tasks. It also achieves excellent results in ship trajectory prediction. [ABSTRACT FROM AUTHOR]

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the "dual-carbon" goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system. [ABSTRACT FROM AUTHOR]

This study investigates dynamic interactive patterns of the dry bulk shipping markets through the lens of structural vector autoregression (VAR) models. There are two distinct hypotheses in the extant literature. One is that asymmetry between spot and forward freight agreement (FFA) markets causes FFA prices to respond more rapidly to new information than spot prices, termed the Physical-Paper Hypothesis in this paper. The other hypothesis is that there are some diverse shocks with different propagation mechanisms, termed the Shock-Diversity Hypothesis. This paper combines these two seemingly separate hypotheses at one time in two illustrative structural VAR models, based on a dataset of weekly spot, FFA, and time charter (TC) rates in the dry bulk shipping markets. The two structural VAR models show that spot, FFA, and TC rates have different, particularly dynamic responses to identified permanent shocks. Furthermore, they show that the reduced-form tri-variate FFA-Spot-TC VAR model produces more accurate forecasts of future spot rates than the bi-variate FFA-Spot and TC-Spot VAR models. Therefore, market participants can use VAR models as an information-processing tool for understanding of (e.g. data descriptions and structural inferences), and forecasting in, dry bulk shipping markets. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. Then, the paper delves into the concept of resilience and the role of microgrids in maintaining power stability. The paper reviews various AI techniques and methods, and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control. [ABSTRACT FROM AUTHOR]

The accumulation of wind power prediction deviations will make it difficult to maintain the long-term stable operation of energy storage. To solve this problem, this paper proposes a hybrid energy storage system configuration method containing second-use batteries. This paper establishes a three-battery hybrid energy storage operation strategy that considers the accumulation of prediction deviation and prevents the accumulation of prediction deviation by changing the energy storage used at the end of the dispatch cycle. It also establishes an optimal allocation model for energy storage capacity, which takes into account the performance parameters and life loss of the second-use batteries and the new power battery. Finally, Gurobi is used to simulate the field data of a wind farm. The simulation results show that this method is effective in preventing the accumulation of prediction deviation while reducing wind power grid deviation and improving the level of energy storage utilization. It can play a certain reference role in the configuration of energy storage for wind farms. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of photovoltaic (PV) power generation has led to an increased focus on accurate forecasting of PV power output. Interval forecasting, which provides uncertainty measurement information for forecasting results, has become a hot research topic in this field. However, the accuracy of single models or traditional multi-model forecasting methods is often insufficient for meeting the forecasting requirements. In addition, interval forecasting places higher demands on the learning and generalization capabilities of forecasting models. To address these issues, this paper proposes an intelligent multi-model forecasting method based on output features clustering and convolutional neural network–long short term memory (CNN–LSTM) for PV power interval forecasting. First, multiple feature indices are constructed to represent the differences in PV output features for different months. The intelligent clustering method is then employed to achieve the multi-model clustering for forecasting model. Finally, CNN–LSTM is utilized to implement the PV power interval forecasting. The combination of CNN and LSTM effectively improves the modeling accuracy of the intelligent forecasting model. Based on actual data from PV power stations, the method described in this paper narrows down the forecasting interval compared with the single model, reducing prediction interval normalized average width by more than 4%. [ABSTRACT FROM AUTHOR]

Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer. [ABSTRACT FROM AUTHOR]

An ultra-short-term multivariate load forecasting method under a microscopic perspective is proposed to address the characteristics of user-level integrated energy systems (UIES), which are small in scale and have large load fluctuations. Firstly, the spatio-temporal correlation of users' energy use behavior within the UIES is analyzed, and a multivariate load input feature set in the form of a class image is constructed based on the various types of load units. Secondly, in order to maintain the feature independence and temporal integrity of each load during the feature extraction process, a deep neural network architecture with spatiotemporal coupling characteristics is designed. Among them, the multi-channel parallel convolutional neural network (MCNN) performs independent spatial feature extraction of the 2D load component pixel images at each moment in time, and feature fusion of various types of load features in high dimensional space. A bidirectional long short-term memory network (BiLSTM) is used as a feature sharing layer to perform temporal feature extraction on the fused load sequences. In addition, a spatial attention layer and a temporal attention layer are designed in this paper for the original input load pixel images and the fused load sequences, respectively, so that the model can better capture the important information. Finally, a multi-task learning approach based on the hard sharing mechanism achieves joint prediction of each load. The measured load data of a UIES is analyzed as an example to verify the superiority of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Objective, accurate and fair development of research and effective performance prediction methodology for the construction of the talent team is the current needs of the new era of innovation and reform and development of university management, as well as the need to improve the quality of scientific research and teaching level of the talent team. OBJCTIVES: To address the problems of irrational principle of indicator selection, incomplete system and imprecise methodology in the current research on performance prediction of talent team building. METHODS: This paper proposes a talent team construction performance prediction method based on intelligent optimization algorithm improving neural network with integrated learning as the framework. First of all, through the analysis of the current talent team construction performance prediction influencing factors selection principles, analyze the talent team construction performance management process, select the talent team construction performance prediction influencing factors, and construct the talent team construction performance analysis system; then, with the integrated learning as a framework, improve the neural network through the internal search optimization algorithm to construct the talent team construction performance prediction model; finally, through the simulation experiments to analyze and verify the effectiveness and superiority of the proposed method. The effective type and superiority of the proposed method are verified. RESULTS: The results show that the proposed method satisfies the real-time requirements while improving the prediction accuracy. CONCLUSION: This paper addresses the lack of precision in forecasting the performance of the talent pipeline and the lack of a sound analytical system. [ABSTRACT FROM AUTHOR]

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer. [ABSTRACT FROM AUTHOR]

The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMDSGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research. [ABSTRACT FROM AUTHOR]

The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and R2, were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems based on sensor signals are crucial in addressing contemporary transportation issues, accomplishing dynamic traffic management, and facilitating route planning. However, the highly dynamic and intricate nature of traffic sensor signals presents difficulties for traffic prediction, with current models for traffic prediction inadequate in meeting the requirements of both long-term and short-term prediction tasks. In this paper, we propose a novel deep-learning framework called dynamic spatial-temporal correlation learning network (DSTLNet) that jointly leverages dynamical spatial and temporal features of traffic sensor signals to further improve the accuracy of long- and short-term traffic modeling and route planning. Specifically, we leverage the temporal convolutional network to capture long-term correlations. In addition, a spatial graph convolutional network is developed to dynamically model spatial features, and long- and short-term fusion layers are used to fuse the extracted long- and short-term temporal features, respectively. Experimental results on real-world datasets show that DSTLNet is competitive with the state-of-the-art, especially for long-term traffic prediction. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a critical role in urban traffic management and planning. Accurate prediction of traffic flow can enhance traffic efficiency, improve traffic safety, conserve traffic resources, and promote sustainable urban development. Graph convolutional networks (GCN) have great potential in traffic flow prediction due to their ability to handle complex data with topological structures while considering spatial relationships between nodes in data. However, it is difficult for the traditional GCN-based models to capture dynamic spatiotemporal dependencies between the stations since these models usually use static relationship graph in GCN. This paper presents an enhanced approach called the coupled generative graph convolution model (CGGCN) for capturing dynamic regional traffic relationships. The CGGCN learns the adjacency matrix of graph convolution in a generative manner, thereby improving the ability of previous graph convolution models to effectively capture the dynamic relationships of regional traffic within multi-layer structures. Additionally, the paper optimizes the fusion method of coupling graph convolution in multi-level matrix feature flow information aggregation, leading to the efficient extraction of spatiotemporal information. To validate the effectiveness of the proposed model, extensive experiments were conducted on two real traffic datasets obtained from New York, namely NYCBike and NYCTaxi. The results demonstrate the superior performance of the CGGCN model when compared to other eight baseline models. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]