Short-term Model Research Articles

Energy consumption analysis of metropolitan logistics vehicles based on an ensemble K-means long short-term memory model

Energy consumption analysis of metropolitan logistics vehicles based on an ensemble K-means long short-term memory model

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking.

The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5-117.1% accuracy surpassed 1.2-14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

A short-term wind power prediction approach based on an improved dung beetle optimizer algorithm, variational modal decomposition, and deep learning

Accurate short-term wind power prediction is crucial for the efficient and safe operation of wind power systems. To enhance the accuracy of short-term wind power prediction, this paper proposes a hybrid short-term wind power prediction model, IDBO-VMD-TCN-GRU-Attention, based on the Improved Dung Beetle Optimizer algorithm (IDBO), Variational Modal Decomposition (VMD), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism. The IDBO is used to optimize the parameters of the VMD. Then the optimized IDBO-VMD is used to decompose the original data into modal components with lower volatility to fully extract the data features. These modal components are inputted into the TCN-GRU-Attention to predict the short-term wind power and obtain the final prediction value. The model is tested and validated using real data from four different months and compared against 11 other models. Results demonstrate that our proposed model significantly reduces the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by an average of at least 22.86 %, 18.82 %, and 19.99 %, respectively, across the four different months. This indicates that the proposed model provides a superior fit to the data, offers higher prediction accuracy, and holds significant practical value.

An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.

A Deep Learning Approach for Real-Time Sign Language Detection and Response through Avatar Model

Abstract: This paper focuses on the evolution of a real-time sign language detection model using computer vision, machine learning, and deep learning. Its goal is to narrow the poor communication for the speaking and hearing-impaired community by using recurrent neural networks and long short-term memory models. The proposed system utilizes a dataset comprising sign language gestures captured in various contexts and by different signers. Preprocessing techniques are applied to draw out fitting features from the video frames, including hand movements, facial expressions, and body postures. The LSTM neural network architecture is chosen to grab temporal dependencies in sequential data, making it suitable for the robust nature of sign language. The training process involves optimizing the LSTM network on the labeled dataset, incorporating techniques such as transfer learning and data augmentation to enhance model generalization. The resulting model is capable of recognizing a divergent set of sign language actions in real-time

Recurrent neural network and long short-term memory models for audio copy-move forgery detection: a comprehensive study

AbstractOne of the most pressing challenges in audio forgery detection—a major topic of signal analysis and digital forensics research—is detecting copy-move forgery in audio data. Because audio data are used in numerous sectors, including security, but increasingly tampered with and manipulated, studies dedicated to detecting forgery and verifying voice data have intensified in recent years. In our study, 2189 fake audio files were produced from 2189 audio recordings on the TIMIT corpus, for a total of 4378 audio files. After the 4378 files were preprocessed to detect silent and unsilent regions in the signals, a Mel-frequency-based hybrid feature data set was obtained from the 4378 files. Next, RNN and LSTM deep learning models were applied to detect audio forgery in the data set in four experimental setups—two with RNN and two with LSTM—using the AdaGrad and AdaDelta optimizer algorithms to identify the optimum solution in the unlinear systems and minimize the loss rate. When the experimental results were compared, the accuracy rate of detecting forgery in the hybrid feature data was 76.03%, and the hybrid model, in which the features are used together, demonstrated high accuracy even with small batch sizes. This article thus reports the first-ever use of RNN and LSTM deep learning models to detect audio copy-move forgery. Moreover, because the proposed method does not require adjusting threshold values, the resulting system is more robust than other systems described in the literature.

Polysorbate 80 coated chitosan nanoparticles for delivery of α-melanocyte stimulating hormone analog (NDP-MSH) to the brain reverse cognitive impairment related to neuroinflammation produced by a high-fat diet (HFD)

This study aimed to develop polysorbate 80-coated chitosan nanoparticles (PS80/CS NPs) as a delivery system for improved brain targeting of α-Melanocyte Stimulating Hormone analog (NDP-MSH). Chitosan nanoparticles loaded with NDP-MSH were surface-modified with polysorbate 80 ([NDP-MSH]-PS80/CS NP), which formed a flattened layer on their surface. Nanoparticle preparation involved ionic gelation, followed by characterization using scanning electron microscopy (SEM) for morphology, dynamic light scattering (DLS) for colloidal properties, and ATR-FTIR spectroscopy for structure. Intraperitoneal injection of FITC-PS80/CS NPs and [NDP-MSH]-PS80/CS NP in rats demonstrated their ability to cross the blood-brain barrier, reach the brain, and accumulate in CA1 neurons of the dorsal hippocampus within 2 h. Two experimental models of neuroinflammation were employed with Male Wistar rats: a short-term model involving high-fat diet (HFD) consumption for 5 days followed by an immune stimulus with LPS, and a long-term model involving HFD consumption for 8 weeks. In both models, [NDP-MSH]-PS80/CS NPs could reverse the decreased expression of contextual fear memory induced by the diets. These findings suggest that [NDP-MSH]-PS80/CS NPs offer a promising strategy to overcome the limitations of NDP-MSH regarding pharmacokinetics and enzymatic stability. By facilitating NDP-MSH delivery to the hippocampus, these nanoparticles can potentially mitigate the cognitive impairments associated with HFD consumption and neuroinflammation.

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.

A short-term rodent model for non-alcoholic steatohepatitis induced by a high-fat diet and carbon tetrachloride.

Several models of mice-fed high-fat diets have been used to trigger non-alcoholic steatohepatitis and some chemical substances, such as carbon tetrachloride. This study aimed to evaluate the joint action of a high-fat diet and CCl4 in developing a short-term non-alcoholic steatohepatitis model. C57BL6/J mice were divided into two groups: standard diet-fed (SD), the high-fat diet-fed (HFD) and HFD + fructose-fed and carbon tetrachloride (HFD+CCl4). Animals fed with HFD+CCl4 presented increased lipid deposition compared with both SD and HFD mice. Plasma cholesterol was increased in animals from the HFD+CCl4 group compared to the SD and HFD groups, without significant differences between the SD and HFD groups. Plasma triglycerides showed no significant difference between the groups. The HFD+CCl4 animals had increased collagen deposition in the liver compared with both SD and HFD groups. Hydroxyproline was also increased in the HFD+CCl4 group. Liver enzymes, alanine aminotransferase and aspartate aminotransferase, were increased in the HFD+CCl4 group, compared with SD and HFD groups. Also, CCl4 was able to trigger an inflammatory process in the liver of HFD-fed animals by promoting an increase of ~2 times in macrophage activity, ~6 times in F4/80 gene expression, and pro-inflammatory cytokines (IL-1b and TNFa), in addition to an increase in inflammatory pathway protein phosphorylation (IKKbp). HFD e HFD+CCl4 animals increased glucose intolerance compared with SD mice, associated with reduced insulin-stimulated AKT activity in the liver. Therefore, our study has shown that short-term HFD feeding associated with fructose and CCl4 can trigger non-alcoholic steatohepatitis and cause damage to glucose metabolism.

Study on multi-timescale operation of hydrogen-containing energy coupled with transportation system

Abstract With the widespread attention on hydrogen energy, the operation optimization of the coupling system of hydrogen energy and transportation has become a critical issue. Therefore, this study develops a hydrogen-containing energy transportation coupling system for the system collaborative operation framework, operation mode and equipment modelling of the system. Furthermore, a medium- to long-term operation optimization model and a short-term operation optimization model were constructed considering the differences in the operation of coupled systems at different timescales and operating costs as the objective function and power balance and system equipment as constraints. In the medium- and long-term operation optimization model, the planning scenario reduction method is used to reduce the wind power generation scenario. In the short-term operation optimization model, a multivariate uncertainty model is constructed to represent the uncertainty in the coupling system. Subsequently, the solution method of the model is proposed. Finally, a coupled system is simulated to verify the effectiveness of the model. (i) When the initial scene set is 600, the typical scene reduction method using Latin hypercube sampling and the Wasserstein distance can reduce operating costs by 7.60% and 9.49% compared with K-means reduction and K-media reduction methods. (ii) The sensitivity coefficients of hydrogen sales price, electricity sales price and maintenance rate to operating costs are –0.031%, –1.009% and 0.0105%, respectively. (iii) Considering multiple uncertainties can help make optimal decisions based on the overall consideration of disadvantage scenarios, thereby reducing system operating costs.

An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India

Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30) meteorological sub-divisions in India. The model was compiled using the Mean Square Error (MSE) loss function and Adam optimizer. The models' performances were evaluated using statistical metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings: This study discovered that the proposed Bidirectional LSTM model achieved an RMSE of 240.79 and outperformed an existing Recurrent Neural Network (RNN), Vanilla LSTM and Stacked LSTM by 8%, 4% and 2% respectively. The study also finds that increasing the input time step and increasing the number of cells in the hidden layer enhanced the prediction performance of the proposed model, and the Bidirectional LSTM converges at a lower epoch compared to RNN and LSTM models. Novelty: This study applied the Bidirectional LSTM for the first time in predicting all-India monthly average rainfall and provides a new benchmark for this dataset. Keywords: Deep Learning, LSTM, Rainfall prediction, Stacked LSTM, Bidirectional LSTM

Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

To improve the prediction accuracy of the performance degradation trend of proton exchange membrane fuel cell (PEMFC), this paper proposes a temporal convolutional network (TCN) model based on genetic algorithm (GA) optimization to predict the performance degradation trend of PEMFC. Firstly, variational mode decomposition and wavelet threshold denoising algorithms are used to denoise the original data. Then the hyperparameters of the TCN model are optimized by GA, and the GA-TCN model for predicting the performance degradation trend of PEMFC is constructed. Finally, this paper uses the PEMFC stack degradation experimental dataset disclosed in the IEEE PHM 2014 Data Challenge to verify, and compares the proposed model with the backpropagation neural networks model, the long short-term memory model and the classical TCN model. The results show that the proposed method has the highest performance degradation trend prediction accuracy. In particular, when the training dataset accounts for 30%, i.e. the training samples are small, the root mean square error, mean absolute error and mean absolute percentage error of the GA-TCN model are 0.004 726, 0.003 119 and 9.62%, respectively, which are 14.48%, 20.05% and 2.42% lower than that of the classical TCN model. Consequently, this methodology can forecast the degradation trend of PEMFC with high accuracy.

Cardiac abnormality detection with a tiny diagonal state space model based on sequential liquid neural processing unit

This manuscript introduces a novel method for cardiac abnormality detection by combining the Diagonal State Space Sequence (S4D) model with the Closed-form Continuous-time neural network (CfC), yielding a highly effective, robust, generalizable, and compact solution. Our proposed S4D-CfC model is evaluated on 12- and single-lead electrocardiogram data from over 20 000 patients. The system exhibits validation results with strong average F1 score and average area under the receiver operating characteristic curve values of 0.88% and 98%, respectively. To demonstrate the tiny machine learning of our 242 KB size model, we deployed the system on relatively resource-constrained hardware to evaluate its training performance on-the-edge. Such on-device fine-tuning can enhance personalized solutions in this context, allowing the system to learn each patient’s data features. A comparison with a structured 2D convolutional long short-term memory CfC model demonstrates the S4D-CfC model’s superior performance. The proposed model’s size can be significantly reduced to 25 KB, maintaining reasonable performance on 2.5 s data, 75% shorter than the original 10 s data, making it suitable for resource-constrained hardware and minimizing latency. In summary, the S4D-CfC model represents a groundbreaking advancement in cardiac abnormality detection, offering robustness, generalization, and practicality with the potential for efficient deployment on limited-resource platforms, revolutionizing healthcare technology.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022

BackgroundInfluenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.MethodsThe multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.ResultsDuring the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2–4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0–1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.ConclusionThe ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Hybrid heuristic-based optimal weighted fused feature for convolutional long short-term memory-based intelligent crop yield prediction model

Hybrid heuristic-based optimal weighted fused feature for convolutional long short-term memory-based intelligent crop yield prediction model

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.

CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Examining the influence of sampling frequency on state-of-charge estimation accuracy using long short-term memory models

Examining the influence of sampling frequency on state-of-charge estimation accuracy using long short-term memory models