Reduction In Mean Square Error Research Articles

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

Explainable predictions of multi-component oxides enabled by attention-based neural networks

Multicomponent oxides (MCOs) have attracted considerable attention due to their wide range of applications. However, the extensive search space of MCO components and the scarcity of MCO crystal structures in existing literature have promoted the use of deep machine learning methods for predicting MCO properties. Despite these advances, accurately predicting the thermal expansion of MCOs across wide temperature and composition ranges remains a complex task. An innovative attention-based deep learning model was introduced in this study. The two proposed self-attention modules greatly improved the model's performance, achieving an 86.88% reduction in root mean square error for predicting thermal expansion coefficients of multicomponent oxides. Additionally, the model demonstrates impressive adaptability and interpretability. Its training results can further aid in comprehending the thermal expansion coefficient variations of multicomponent oxide materials. In summary, judiciously crafted self-attention models overcome tradeoffs between performance and interpretability for materials discovery.

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs.

A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones

AbstractWater transport in the unsaturated zone is an important part of the hydrological cycle and is the link between the atmosphere‒soil‐groundwater for material and energy transport. The accurate prediction of soil moisture (SM) is essential for the rational exploitation of water resources. Data‐driven deep learning methods are widely used in many fields; however, the lack of physical mechanisms limits their application in hydrological fields, especially for SM prediction in unsaturated zones. To solve this problem, this study proposes a new deep learning method that introduces the water balance principle, Richard's equation, and SM boundary conditions as constraints to construct the new loss function that guides the training process of deep learning, called physics‐informed deep learning (PIDL). In tests consisting of a large number of data sets acquired from in situ observation sites in the field, PIDL exhibits higher accuracy than ordinary deep learning (long short‐term memory) and physical models, with 51.03% and 53.46% reduction in root mean square error of SM prediction, respectively. PIDL performance significantly improved in predicting scenarios that are difficult for ordinary deep learning to handle, such as sparse data sets, extreme values, and mutated values. In addition, PIDL maintains high accuracy over a longer prediction period. The addition of physical mechanisms allows deep learning to mine patterns not only from the data itself but also from a priori physical theoretical knowledge for guidance, and this hybrid modeling approach can also be generalized to prediction problems in other hydrological domains.

Explainable Approaches for Forecasting Building Electricity Consumption

Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes.

An observing system experiment framework for the tropical Indian Ocean salinity: A case study using a constellation of three satellites

In this study impact of assimilating Sea Surface Salinity (SSS) from multi-satellites (SMOS, Aquarius and SMAP) on numerical ocean model simulations in the north Indian Ocean has been analysed under the observing system experiment (OSE) framework. Daily data sets of Aquarius, SMAP and SMOS, which were available for a common period of April–May 2015, are used to constrain the ocean model using ensemble optimal interpolation technique. Apart from the control simulation in which satellite data were not assimilated, a total of seven assimilation experiments using different combinations of satellite SSS were conducted. The impact of assimilation experiments is analysed by comparing the model-simulated variables with in situ observations. Assimilating satellite SSS results in a reduction in Root Mean Square Error (RMSE) in SSS (∼ 54%) and also in subsurface salinity (∼ 21%) over the control run. The impact of assimilating SMAP observations is maximum on model simulations with the errors reducing by ∼ 54%. Subsurface salinity improvement is better with three satellites with ∼31% improvement in RMSE in the halocline region, which was ∼11% more than single satellite assimilation. Assimilation of SSS also resulted in improved simulations of the model surface, subsurface temperature and mixed layer depth. Model results show the ability of SSS observations to complement other ocean observation networks. One important observation from this study is that while the impact of assimilating SSS observations from a single satellite was on par with the impact of assimilating SSS observations from two or three satellites in correcting simulated surface salinity, assimilation from more than one satellite had a larger impact in the salinity of deeper layers of the ocean.

Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin

The Normalized Difference Vegetation Index (NDVI) is an essential metric in vegetation monitoring for remote sensing applications. While there are numerous long-term low-resolution NDVI datasets available there remains an unmet need for high-resolution NDVI reconstructions over extended time frames. Existing research has not comprehensively assessed the efficacy of various temporal fusion techniques for NDVI reconstruction at large regional scales. Traditional Spatiotemporal Image Fusion (TSTIF) methods often suffer from limited fusion accuracy due to input data quality constraints. To address these limitations this study introduces an innovative Deep Learning-Enhanced Spatiotemporal Fusion Method. Deep learning algorithms are employed to refine the spatial resolution of input data thereby facilitating the separation of complex image elements located at feature boundaries. This improved approach significantly enhances fusion accuracy as validated against five established TSTIF techniques through empirical analysis. As a case study we generate long-term Fractional Vegetation Cover (FVC) datasets to investigate the ecological dynamics of the Danjiang River Basin. Our findings reveal substantial gains in NDVI reconstruction accuracy through the incorporation of deep learning into traditional fusion techniques. Among the methods tested the STRUM algorithm showed the greatest improvement with its R2 value increasing from 0.872 to 0.894 and a consistent reduction in Root Mean Square Error (RMSE). The FSDAF technique emerged as the most effective boasting an R2 value of 0.953 and an RMSE of 0.012. These enhanced NDVI datasets provide more accurate representations of the basin's vegetation cover thus enriching long-term observational archives and facilitating further quantitative remote sensing analyses. Moreover we report a 21-year mean FVC of 0.702 for the Danjiang River Basin characterized by a “north-high south-low” distribution that varies seasonally. Improvements in vegetation cover were observed in Xichuan and Neixiang counties while environmental degradation is evident in the broader Danjiang River watershed. Key factors influencing these spatial variations include elevation and soil composition in addition to climate variables such as rainfall and temperature. Infrastructure projects like the South-to-North Water Diversion Project and targeted ecological conservation policies have also positively impacted vegetation within the basin.

Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) method to improve user and propagation graph representations. Furthermore, we employ two multi-task training strategies to mitigate negative transfer effects among tasks in different settings, enhancing the overall performance of our approach. We also construct two datasets with ground-truth annotations on information virality and user vulnerability in rumor and non-rumor events, which are automatically derived from existing rumor detection datasets. Extensive evaluation results of our joint learning model confirm its superiority over strong baselines in all three tasks: rumor detection, virality prediction, and user vulnerability scoring. For instance, compared to the best baselines based on the Weibo dataset, our model makes 3.8% and 3.0% improvements on Accuracy and MacF1 for rumor detection, and reduces mean squared error (MSE) by 23.9% and 16.5% for virality prediction and user vulnerability scoring, respectively. Our findings suggest that our approach effectively captures the correlation between rumor virality and user vulnerability, leveraging this information to improve prediction performance and provide a valuable tool for infodemic surveillance.

An improvement in accuracy and spatial resolution of the pattern-matching sea ice drift from SAR imagery

ABSTRACTSea ice drift is a crucial parameter for sea ice flux, atmospheric and ocean circulation, and ship navigation. Pattern matching is widely used to retrieve sea ice drift from Synthetic Aperture Radar (SAR) data, but it often yields mismatched vectors and coarse spatial resolution. This study presents a framework to enhance the spatial resolution and accuracy of pattern-matching sea ice drift derived from SAR images. The framework employs the Accelerated-KAZE feature extraction method and Brute-Force feature matcher to extract feature-tracking sea ice drift vectors from SAR data, with mismatched vectors subsequently removed. The pattern-matching vectors are then refined by fusing with these feature-tracking vectors, using a Co-Kriging algorithm. Using the sea ice drift product from the Technical University of Denmark space as the pattern-matching vector field for refinement, the framework's effectiveness is evaluated by comparing the refined vectors with buoy displacements and pattern-matching vectors across five selected regions. Results show a reduction in velocity and direction root mean square error (RMSE) by 0.47 km/d (22%) and 4.97° (28%), respectively, and an enhanced spatial resolution from 10 km to 1 km. The findings demonstrate the framework's success in improving the accuracy and resolution of pattern-matching sea ice drift from SAR imagery.

Design of Backstepping Sliding Mode Control for a Polishing Robot Pneumatic System Based on the Extended State Observer

Due to advantages such as a high power-to-weight ratio, a simple structure, and low cost, pneumatic systems are widely applied in automation. However, precise position control of pneumatic actuators is challenging because of factors such as friction, compressibility, and external disturbances. This paper presents a backstepping sliding mode control (BSMC) strategy based on the extended state observer (ESO) for pneumatic cylinder position tracking. A nonlinear model of the pneumatic system is first established, then system states and disturbances are estimated by an ESO, next the BSMC approach is developed using backstepping method and sliding mode control theory, and the stability of the ESO and controller is analyzed using Lyapunov theory. Finally, simulations and experiments on a pneumatic testbed are performed to compare the effectiveness of the proposed approach with PID control. The results show that the proposed strategy improves tracking accuracy and robustness against disturbances, with a 77.04% reduction in root mean square error (RMSE). This research provides a promising control solution for automated pneumatic polishing robots.

Boosting CO2 Uptake from Waste Concrete Powder Using Artificial Intelligence and the Marine Predators Algorithm

Waste concrete powder (WCP) is emerging as a potential method of adoption for CO2 sequestration due to its ability to chemically react with carbon dioxide and trap it within its structure. This study explores the application of artificial intelligence (AI) and the Marine Predators Algorithm (MPA) to maximize the absorption of CO2 from waste concrete powder generated by recycling plants for building and demolition debris. Initially, a model is developed to assess CO2 uptake according to carbonation time (CT) and water-to-solid ratio (WSR), utilizing the adaptive neuro-fuzzy inference system (ANFIS) modeling approach. Subsequently, the MPA is employed to estimate the optimal values for CT and WSR, thereby maximizing CO2 uptake. A significant improvement in modeling accuracy is evident when the ANOVA method is replaced with ANFIS, leading to a substantial increase of approximately 19% in the coefficient of determination (R-squared) from 0.84, obtained through ANOVA, to an impressive 0.9999 obtained through the implementation of ANFIS; furthermore, the utilization of ANFIS yields a substantial reduction in the root mean square error (RMSE) from 1.96, as indicated by ANOVA, to an impressively low value of 0.0102 with ANFIS. The integration of ANFIS and MPA demonstrates impressive results, with a nearly 30% increase in the percentage value of CO2 uptake. The highest CO2 uptake of 3.86% was achieved when the carbonation time was 54.3 h, and the water-to-solid ratio was 0.27. This study highlights the potential of AI and the MPA as effective tools for optimizing CO2 absorption from waste concrete powder, contributing to sustainable waste management practices in the construction industry.

An energy consumption prediction method for HVAC systems using energy storage based on time series shifting and deep learning

The prediction of building energy consumption plays a crucial role in responding to energy demands and achieving low-carbon control through energy saving. In this study, we focused on peak shaving and valley filling in the energy consumption of office building energy-storage HVAC systems. A time series shifting method based on the Pearson correlation coefficient was combined with a gate recurrent unit (GRU) deep learning model to predict HVAC energy consumption. Additionally, we explored the adaptability of this method to non-deep learning support vector regression (SVR) and random forest (RF) models. The results demonstrated that the Kaiser-Meyer-Olkin test values of HVAC energy consumption, meteorological parameters, and indoor environmental parameters improved by 10.8%. The GRU model exhibited a 12% reduction in the root mean square error (RMSE), and the coefficient of determination (R2) was 0.850. The RMSE of the RF model decreased to 12.24 kW·h, with a 12% increase in R2 to 0.760. The SVR model achieved an RMSE of 12.59 kW·h and an R2 of 0.793. Therefore, this method can effectively improve the prediction accuracy of models by enhancing the correlation between their input and output variables.

NEURAL NETWORKS FOR OPTIMIZATION OF AN EARLY WARNING SYSTEM FOR MOORED SHIPS IN HARBOURS

Within the BlueSafePort project, an early warning system (EWS) for moored ships in the port of Sines was developed. In order to improve the reliability and accuracy of the system, two neural networks (NN) were trained, using wave buoys measured datasets. Numerical models results for the wave propagation are adjusted and forecasts are improved. The trained neural networks were able to produce more accurate estimates for the significant wave height and mean wave period, at the buoy location, deployed in front of the Sines Port. The use of the new NN led to an overall reduction of the root mean square error of around 80percent compared with SWAN numerical model simulations, thus reducing potential errors in subsequential calculations and alert levels issued by the system for the moored ships.

MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST

Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error (RMSE) by 1.95%, 1.39% and 1.28% in NPO, SAO and NAO, respectively, using the STGCN model, and the reduction of RMSE by 4.94%, 0.74% and 1.43% by using the AGCRN model. Such results indicate that the integration of regionality information could notably improve the prediction accuracy of SST.

A novel wind power prediction model improved with feature enhancement and autoregressive error compensation

Wind energy is a widely utilized form of clean energy with significant implications for maximizing its utilization and ensuring the stability of power systems. However, existing methods for short-term wind power prediction face challenges in effectively capturing hidden features in historical data characterized by local random fluctuations. Furthermore, these methods lack appropriate compensation mechanisms to account for deviations between predicted and actual wind farm results. This paper proposes a novel wind power prediction model that addresses these limitations through feature enhancement and autoregressive error compensation. The model emphasizes the relationship between six wind power variables collected in the field and utilizes bidirectional long short-term memory to capture bidirectional time series characteristics. The attention mechanism is employed to assign different weights to these features, enhancing their representation and reducing computational complexity. To overcome frequent local fluctuations in wind power, an autoregressive error compensation model is integrated to further improve prediction accuracy. The proposed method is validated through practical application in a wind power plant, and experimental results demonstrate significant reduction in root mean square error compared to other models. This approach effectively extracts change trends in non-stationary wind power data, enhancing short-term power prediction performance. The proposed method has potential for application in other large-scale wind farms within the hybrid prediction framework.

Halfway to Automated Feeding of Chinese Hamster Ovary Cells.

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.

Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM

Accurate prediction of runoff is an important foundation for optimizing water resource allocation and reservoir scheduling operations. However, due to its complex characteristics such as time-varying and non-stationary, accurate prediction of the runoff process is very difficult. This paper proposes a novel approach for runoff forecasting by combining the physical mechanism models with the Long Short-Term Memory network (LSTM) method. Utilizing the simulation and description capabilities of physically based models, as well as the powerful nonlinear analysis provided by big data methods, a type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. Additionally, to accommodate the comprehensive analysis and evaluation of multi-model forecasting performance, this paper also proposed a comprehensive evaluation metric for runoff forecasting considering the characteristics of group models. The results of the case study demonstrate that this strategy can obtain model combinations suitable for different watershed characteristics and effectively improve the forecast accuracy of multiple models. Model combinations sorted by validation period RMSE and R2 should be a superior choice. When evaluating the runoff forecasting accuracy of obtained optimal model combination during the calibration period, the average reduction of Root Mean Square Error (RMSE) is 39.62%, and the average increase of Nash-Sutcliffe coefficient R2 is 7.49%. During the validation period, the average reduction of RMSE is 62.68% and the average increase of R2 is 24.24%. When the model combinations sorted by validation period F score, the obtained model combination increased RMSE by 22.7% and decreased R2 by 7.3% comparing to the model combination selected by RMSE and R2. It is indicated that the F score may not be suitable for evaluating model selection. The method proposed in this article can effectively improve the overall forecasting performance of a single forecasting model and has good practical value for promotion and application.

Research on the Effect of Data Assimilation for Three‐Dimensional MHD Simulation of Solar Wind

AbstractAs an important part of space weather forecasting, the prediction of solar wind parameters in the near‐Earth space is particularly significant. The introduction of data assimilation (DA) method can improve the reliability of numerical prediction. In this study, we use a three‐dimensional (3D) magnetohydrodynamics (MHD) numerical model with Kalman filter to infer the impact of the DA on solar wind modeling. We use the 3D MHD numerical model with near‐Earth in situ observations from the OMNI database to reconstruct solar wind parameters between 21.5 solar radii and 1 AU. The period from 2018 to 2021 is simulated, when the solar activity in the decay of the 24th solar cycle to the rising of 25th solar cycle. The numerical model generates two separate results, one without DA and one with DA directly performed on the model‐only results. Statistical analysis of observed, modeled and assimilated solar wind parameters at 1 AU reveals that assimilating simulations provide a more accurate forecast than the model‐only results with a sharp reduction in the root mean square error and an increase of correlation coefficient.

Transcutaneous vagus nerve stimulation at nonspecific timings during training can compromise motor adaptation in healthy humans.

Adding afferent vagus nerve stimulation to motor training via implanted electrodes can modify neuromotor adaptation depending on the stimulation timing. This study aimed to understand neuromotor adaptations when transcutaneous vagus nerve stimulation (tVNS) is applied at nonspecific timings during motor skill training in healthy humans. Twenty-four healthy young adults performed visuomotor training to match a complex force trajectory pattern with the index and little finger abduction forces concurrently. Participants were assigned to the tVNS group receiving tVNS at the tragus or the sham group receiving sham stimulation to the earlobe. The corresponding stimulations were applied at nonspecific timings throughout the training trials. Visuomotor tests were performed without tVNS or sham stimulation before and after training sessions across days. The reduction in the root mean square error (RMSE) against the trained force trajectory was attenuated in the tVNS group compared with the sham group, while its in-session reduction was not different between groups. The reduction of RMSE against an untrained trajectory pattern was not different between groups. No training effect was observed in corticospinal excitability or GABA-mediated intracortical inhibition. These findings suggest that adding tVNS at nonspecific timings during motor skill training can compromise motor adaptation but not transfer in healthy humans.NEW & NOTEWORTHY Adding vagus nerve stimulation via implanted electrodes during motor training can facilitate motor recovery in disabled animals and humans. No study examined the effect of transcutaneous vagus nerve stimulation (tVNS) during training on neuromotor adaptation in healthy humans. We have found that adding tVNS at nonspecific timings during motor skill training can compromise motor adaptation but not transfer in healthy humans.

Simulated annealing algorithm optimized GRU neural network for urban rainfall-inundation prediction

Abstract In the context of global climate change and the continuous development of urban areas, rainfall-inundation modeling is a common approach that provides critical support for the protection and early warning of urban waterlogging protection. The present study conducts a data-driven model for hourly urban rainfall-inundation depth prediction, which is based on a gated recurrent unit (GRU) neural network and uses the simulated annealing (SA) algorithm for the hyperparameter optimization of GRU, namely the SA-GRU model. To verify the performance of the proposed model, backpropagation, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) neural networks are set as benchmarks. Results show that the SA-GRU has high accuracy in the case of short-term inundation prediction, with the Nash–Sutcliffe efficiency from 0.999 to 0.596 for the 1-h-ahead to 8-h-ahead predictions. And further research reveals that the SA-GRU integrates the significant optimization of SA, with an average 20% reduction of the root mean square error within the first eight prediction periods, and the efficient training speed of GRU, with 23.7% faster than LSTM and 44.2% faster than BiLSTM. In conclusion, the SA-GRU excels in urban inundation prediction, demonstrating its value in flood management and decision-making.