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Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.

Accurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability. The graphical depicts the detailed steps of the proposed integrated method of RUL prediction based on CEEMD‐Transformer‐LSTM Model, including data processing, model training, and performance analysis. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries.

Obfuscation is rampant in both benign and malicious JavaScript (JS) codes. It generates an obscure and undetectable code that hinders comprehension and analysis. Therefore, accurate detection of JS codes that masquerade as innocuous scripts is vital. The existing deobfuscation methods assume that a specific tool can recover an original JS code entirely. For a multi-layer obfuscation, general tools realize a formatted JS code, but some sections remain encoded. For the detection of such codes, this study performs Deobfuscation, Unpacking, and Decoding (DUD-preprocessing) by function redefinition using a Virtual Machine (VM), a JS code editor, and a python int_to_str() function to facilitate feature learning by the FastText model. The learned feature vectors are passed to a classifier model that judges the maliciousness of a JS code. In performance evaluation, the authors use the Hynek Petrak's dataset for obfuscated malicious JS codes and the SRILAB dataset and the Majestic Million service top 10,000 websites for obfuscated benign JS codes. They then compare the performance to other models on the detection of DUD-preprocessed obfuscated malicious JS codes. Their experimental results show that the proposed approach enhances feature learning and provides improved accuracy in the detection of obfuscated malicious JS codes.

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

In this study, the changes in centrifugal loss, TVB-N, K-value, whiteness and sensory evaluation of glazed large yellow croaker were analyzed at −10, −20, −30 and −40 °C storage. The Arrhenius prediction model and long-short-term memory neural networks (LSTM-NN) prediction model were developed to predict the shelf-life of the glazed large yellow croaker. The results showed that the quality of glazed large yellow croaker gradually decreased with the extension of frozen storage time, and the decrease in quality slowed down at lower temperatures. Both the Arrhenius model and the LSTM-NN prediction model were good tools for predicting the shelf-life of glazed large yellow croaker. However, for the relative error, the prediction accuracy of LSTM-NN (with a mean value of 7.78%) was higher than that of Arrhenius model (with a mean value of 11.90%). Moreover, the LSTM-NN model had a more intelligent, convenient and fast data processing capability, so the new LSTM-NN model provided a better choice for predicting the shelf-life of glazed large yellow croaker.

Depression has become one of the most widespread mental health disorders across the globe. Depression is a state of mind which affects how we think, feel, and act. The number of suicides caused by depression has been on the rise for the last several years. This issue needs to be addressed. Considering the rapid growth of various social media platforms and their effect on society and the psychological context of a being, it’s becoming a platform for depressed people to convey feelings and emotions, and to study their behavior by mining their social activity through social media posts. The key objective of our study is to explore the possibility of predicting a user’s mental condition by classifying the depressive from non-depressive ones using Twitter data. Using textual content of the user’s tweet, semantic context in the textual narratives is analyzed by utilizing deep learning models. The proposed model, however, is a hybrid of two deep learning architectures, Convolutional Neural Network (CNN) and bi-directional Long Short-Term Memory (biLSTM) that after optimization obtains an accuracy of 94.28% on benchmark depression dataset containing tweets. CNN-biLSTM model is compared with Recurrent Neural Network (RNN) and CNN model and also with the baseline approaches. Experimental results based on various performance metrics indicate that our model helps to improve predictive performance. To examine the problem more deeply, statistical techniques and visualization approaches were used to show the profound difference between the linguistic representation of depressive and non-depressive content.

Rationalizing site‐specific crop types is an effective strategy for ensuring food security under climate change. This study employed environmental covariates representing climate, soil, and vegetation, combined with a hybrid convolutional neural network ‐ Long Short‐term Memory‐self‐attention (CNN‐LSTM‐SA) model to predict net primary productivity (NPP) of the Northeast China (NEC) and the Mississippi River Basin (MRB) Mollisol regions. The analysis covered the periods from 2001 to 2020, and 2021 to 2040 under two Shared Socioeconomic Pathways (SSPs): SSP245 and SSP585. Subsequently, areas requiring crop type adjustments were identified, and appropriate crops were assigned to each growth site. Our results elucidate that: (a) During 2021–2040, a general increase in temperature and minor fluctuations in precipitation were observed across the study area. In the NEC, crop NPP initially increases before decreasing, whereas in the MRB, it consistently decreases. (b) Both vegetation and soil covariates explained 75.6% of NPP variability in the NEC, while in the MRB, climate factors, particularly precipitation, accounted for 18.4% of the variability. (c) The proportion of area requiring adjustment in the NEC ranged from 4.45% to 5.13% (SSP245) to 5.05%–5.77% (SSP585), while in the MRB, it varied from 4.92% to 7.54% (SSP245) to 6.49%–9.10% (SSP585), suggesting a necessity for more substantial cropping type adjustments under the SSP585 climate scenario. (d) In the NEC, the area cultivated with corn, soybean, and other crops will decrease, while rice cultivation will increase. Conversely, a decrease in wheat and pasture, and an increase in corn and soybean cultivation are suggested in the MRB. (e) Following crop type adjustments, the average NPP enhancements for corn, soybean, rice, and other crops in unsuitable areas of the NEC were 22.85%, 22.2%, 17.35%, and 20.5%, respectively, In the MRB, the average NPP enhancements for corn, soybean, wheat, and pasture were 28.5%, 26.9%, 32.4%, and 21.1%, respectively. Our research provides valuable insights into predicting future NPP changes, and develops effective crop adjustment strategies to address global food security challenges. Plain Language Summary It is estimated that approximately 30% of the global population faces moderate or severe food insecurity. Changes in crop net primary productivity (NPP) under current and future climate change remain highly uncertain. This raises the question of how to adjust spatial crop cultivation strategies to address food crises due to growing global population under climate change. In this study, we predicted future changes of NPP in the Northeast China (NEC) and the Mississippi River Basin (MRB) Mollisol regions. We then developed adjustment strategies for the spatial distribution of crops. The results show that NPP in the NEC is expected to increase initially and then decrease, whereas a continuous decline is predicted for the MRB, especially in highly productive areas. Soil conditions exert the greatest influence on NPP in the NEC, while meteorological factors, particularly precipitation, dominate crop growth in the MRB. Under both SSP245 and SSP585 climate scenarios, the area requiring adjustment ranged from 4.45% to 5.77% for the NEC and from 4.92% to 9.10% for the MRB. We recommend increasing rice acreage in the NEC and promoting more corn and soybean planting in the MRB. This strategy is expected to be most effective in sustaining and improving NPP. These results emphasize the negative impact of future climate change on crop growth, highlighting the urgent need for crop adjustment strategies. Key Points During 2021–2040, Net primary productivity exhibits an initial rise and fall in Northeast China (NEC), while the Mississippi River Basin (MRB) experiences a consistent decline Proportions of areas requiring crop adjustments substantial changes under SSP585 climate scenario In Mollisols, a potential decrease in corn, soybean was observed in NEC, while a potential increase in corn and soybean cultivation in MRB

In this study, we analyze water-temperature time series was measured over 34 years, between 1986 and 2020, at the water surface at seven stations across Lake Villarrica (Southern Chile). The spring and summer seasons show an increment in the superficial temperature during the study period. The annual maximum temperature, ranging between 17.35 and 21.65 °C were observed in 1997 and 2009, respectively, while the annual minimum, ranging between 16.8 and 21.5 °C were observed in 2001 and 2009, respectively. In addition, we employ a machine learning based estimation model to predict surface temperatures in a South American lake spanning the period 1989 to 2021. Our model uses data in situ of physical, chemical, and biological parameters of lake quality water, along with meteorological data and spectral bands, including combinations of images from the Landsat 8 satellite, as input variables. The 7 lake monitoring stations were classified into 4 regions according to their geographical location: north, south, east, and west. Our findings demonstrate the exceptional performance of the long short-term memory (LSTM) model in accurately estimating temperatures across Lake Villarrica. The best results were obtained for the west region of the lake with good statistical metrics from the estimation model of RMSE = 2.79, Bias =−0.06, max error = 5.93, MSE = 7.83 and median absolute error (MedAE) = 2.13. This approach represents a significant advance in the integration of remote sensing and machine learning techniques to monitor and manage inland water systems.

Accurate prediction of monthly runoff is critical for optimal water resource allocation. However, previous studies mainly focused on the direct prediction of the decomposition sequence, ignoring the error accumulation and the increase in calculation time. In addition, the influence of each sequence on the prediction results was not clarified. Therefore, this study proposes a hybrid prediction method combining time varying filtering-based empirical mode decomposition (TVF-EMD), permutation entropy (PE), a long short-term memory model (LSTM) and a particle swarm algorithm (PSO). Firstly, TVF-EMD is applied for decomposing the original runoff sequences to obtain different components; secondly, PE is applied for characterizing the complexity of different components and reconstructing similar components to obtain new components; then, the decomposed-reconstructed runoff data are predicted by using the LSTM model with PSO based on the analytical studies of different watersheds. The outcomes indicate that the performance index of the proposed model is better than that of the comparison model, improving the prediction accuracy effectively. In addition, the impact of each subseries on prediction performance was also investigated in this study. These findings indicate that the developed model has potential application prospects in runoff prediction and can provide scientific support for water conservancy project operations.

Aim To investigate the optimal training dataset size (TDS) for respiration prediction accuracy using a long short-term memory (LSTM) model. Methods The respiratory signals of 151 patients acquired with the real-time position management system were retrospectively included in this study. Among the dataset, 101 respiratory signals were utilized to evaluate the impact of the TDS on prediction accuracy, while the remaining 50 signals were employed for setting the default hyperparameters. The prediction accuracy of the LSTM model using eight different TDSs (10 s, 20 s, 30 s, 60 s, 90 s, 110 s, 130 s, and 150 s) was examined and evaluated by the root mean square error (RMSE) between the real and predicted respiratory signals. The interplay effects of the main hyperparameters, the ahead time and the different testing data lengths using different TDSs were also measured. Results For the 520 ms ahead time, the root mean square error values of the LSTM model using the eight different training data sizes listed above were 0.146 cm, 0.137 cm, 0.134 cm, 0.125 cm, 0.120 cm, 0.121 cm, 0.121 cm, and 0.119 cm, respectively. The LSTM model achieved the highest prediction accuracy when the TDS was 150 s. The prediction accuracy was stable when the TDS exceeded 90 s. Conclusions TDS selection could influence the respiration signal prediction accuracy of the LSTM model. The relationship between TDS and the prediction accuracy of the LSTM model was not linear. The 90 s seemed to be an optimal TDS for the respiration signal prediction tasks using the LSTM model, as it was the shortest time at which a favorable prediction accuracy was maintained in this study.