Complex Spatiotemporal Patterns Research Articles

Mining struggles in north-central Mexico: Between mining tradition, poverty, and environmentalism

The present study is centered on characterizing the conflicts associated with mining in Sonora and Zacatecas, two states in north-central Mexico. We consider the municipal surroundings where they emerge, via cartographical analysis of some variables including mining frontiers and poverty. The predominant conflict types show that communities are not inclined to accept mining as presented in government or corporate discourses; rather, communities struggle to improve their living conditions and preserve their natural surroundings. We can observe that in long-time mining locations, there is greater acceptance of the mining industry, so that the conflicts which arise tend to be concerned with labor or with eco-territorial matters of coexistence and pecuniary assets, although there are also alternatives; meanwhile, in places with recent mining penetration and deep-rooted agro-fisheries activities, conflicts tend to be eco-territorial regarding alternatives and territorial defense. However, the advance of the mining frontier configures the coexistence of different temporal regimes, based on complex spatio-temporal patterns where different types of environmentalism overlap with demands for opposition, negotiation, or collaboration in the same place.

WiSigPro: Transformer for elevating CSI-based human activity recognition through attention mechanisms

The utilization of Channel State Information (CSI) in Wi-Fi-based passive sensing has become popular due to its cost-effectiveness and broad applicability. This technology is highly valued for its ability to gather data without requiring active user interaction, making it versatile in various contexts. However, existing methods face significant challenges, including low sensing accuracy, high processing requirements, and system instability. To address these issues, we introduce the WiSigPro Transformer, an advanced CSI-based Wi-Fi passive sensing model specifically designed for Human Activity Recognition. This model employs multi-head attention mechanisms and positional encoding to effectively capture complex spatiotemporal patterns, thereby enhancing both robustness and accuracy. Our approach integrates advanced signal processing techniques to improve signal quality and feature extraction. These techniques include wavelet denoising to reduce noise, median filtering to smooth the signal, and Power Spectral Density analysis using Welch’s method to capture frequency domain features. Additionally, we use normalization to standardize amplitude and phase data, and feature engineering methods to extract comprehensive signal characteristics, such as skewness and kurtosis. To address data imbalance, we apply the Synthetic Minority Over-sampling Technique and data augmentation strategies, ensuring balanced representation and improved model generalization. Through comprehensive simulations, the WiSigPro Transformer demonstrates superior performance across key metrics, including recognition accuracy, precision, recall, and F1-score. Achieving an impressive 98% accuracy, it outperforms conventional neural networks such as CNN, RNN, BiLSTM, LSTM, and ABLSTM. These results underscore the transformative potential of the WiSigPro Transformer in Wi-Fi-based passive sensing and activity recognition applications, making it a powerful tool for accurately capturing and analyzing spatiotemporal data.

Roles of cytochrome P450 monooxygenases in triterpene biosynthesis and their potential impact on growth and development.

Pentacyclic triterpenoids, recognized for their natural bioactivity, display complex spatiotemporal accumulation patterns within the ecological model plant Nicotiana attenuata. Despite their ecological importance, the underlying biosynthetic enzymes and functional attributes of triterpenoid synthesis in N. attenuata remain unexplored. Here, we show that three cytochrome P450 monooxygenases (NaCYP716A419, NaCYP716C87, and NaCYP716E107) from N. attenuata oxidize the pentacyclic triterpene skeleton, as evidenced by heterologous expression in Nicotiana benthamiana. NaCYP716A419 catalyzed a consecutive three-step oxidation reaction at the C28 position of β-amyrin/lupeol/lupanediol, yielding the corresponding alcohol, aldehyde, and carboxylic acid. NaCYP716C87 hydroxylated the C2α position of β-amyrin/lupeol/lupanediol/erythrodiol/oleanolic acid/betulinic acid, while NaCYP716E107 hydroxylated the C6β position of β-amyrin/oleanolic acid. The genes encoding these three CYP716 enzymes are highly expressed in flowers and respond to induction by ABA, MeJA, SA, GA3, and abiotic stress treatments. Using VIGS technology, we revealed that silencing of NaCYP716A419 affects the growth and reproduction of N. attenuata, suggesting the ecological significance of these specialized metabolite biosynthetic steps.

Assessing interpreter’s disagreements in land cover reference data collection from historical Landsat time series in Amazon

ABSTRACT Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field data can be very rare for multi-temporal analysis, many studies use samples visually interpreted from Remote Sensing images. This subjective process is prone to errors in interpretation, leading to uncertainties not fully quantified in the reference sample collection of historical time series. We evaluated the agreement rate among independent interpreters in the identification of land cover classes assigned to pixels for yearly Landsat observations from 1984 to 2020, in the Lower Tapajós Basin region (within the Brazilian Amazon). Considering 10 classes, we found that sets of three interpreters completely disagreed on the label of 14.8% of the pixels for each year, and completely agreed in less than half (39.6%). These values change to 2.7% and 68.8% when we consider only five classes in the analysis. We observed time-dependent variations in agreement for specific land cover classes: whereas forest areas tend to present an increase in the disagreement rate over time, land cover types that happen only in previously deforested areas tend to present a higher proportion of pixels with better agreement rates. Disagreements tend to be higher in pixels identified as borders or near borders between targets. However, there is a non-negligible amount of disagreements in pixels identified as not borders caused by limitations derived from the unit of analysis, image composition techniques, and intrinsic similarities among classes. We highlight problems with the identification of classes that happen in complex spatiotemporal patterns (Secondary Vegetation, Small-scale Agriculture, and Degraded Forest). These results alert to possible inconsistencies in reference samples collected for the accuracy assessment of land cover time series and the correspondent values of the calculated accuracy indexes.

DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.

A Graph Attention Recurrent Neural Network Model for PM2.5 Prediction: A Case Study in China from 2015 to 2022

Accurately predicting PM2.5 is a crucial task for protecting public health and making policy decisions. In the meanwhile, it is also a challenging task, given the complex spatio-temporal patterns of PM2.5 concentrations. Recently, the utilization of graph neural network (GNN) models has emerged as a promising approach, demonstrating significant advantages in capturing the spatial and temporal dependencies associated with PM2.5 concentrations. In this work, we collected a comprehensive dataset spanning 308 cities in China, encompassing data on seven pollutants as well as meteorological variables from January 2015 to September 2022. To effectively predict the PM2.5 concentrations, we propose a graph attention recurrent neural network (GARNN) model by taking into account both meteorological and geographical information. Extensive experiments validated the efficiency of the proposed GARNN model, revealing its superior performance compared to other existing methods in terms of predictive capabilities. This study contributes to advancing the understanding and prediction of PM2.5 concentrations, providing a valuable tool for addressing environmental challenges.

Multi-station collaborative wave height prediction based on multi-feature identification and interpretable analysis

This study proposes a novel deep learning model, the graph convolutional gated recurrent unit (GC-GRU), to address the critical challenge of accurate forecasting of ocean wave heights due to the complex nonlinear spatiotemporal variability of wave dynamics. The proposed model, which integrates the strengths of graph convolutional networks (GCNs) for spatial feature extraction and gated recurrent units (GRUs) for temporal feature extraction, allows for effective capture of complex spatiotemporal patterns in wave height data and is evaluated on a dataset of 666 observation stations in the Gulf of Mexico, forecasting wave heights up to 36 h in advance. Comparative experiments with traditional CNN and GRU models demonstrate the superior predictive performance of the GC-GRU approach. Additionally, we introduce the shapley additive explanation (SHAP) values to provide physical insights into the key physical variables and historical patterns driving the model's predictions. The results show that wind speed and mean wave period are the most influential factors related to wave height variations. It is expected that this work presents a significant advancement in wave height forecasting by introducing the innovative GC-GRU architecture and leveraging SHAP analysis to interpret the model's inner workings. The findings are expected to have important implications for enhancing coastal and maritime operations as well as informing our understanding of complex ocean wave dynamics.

Wing deformation improves aerodynamic performance of forward flight of bluebottle flies flying in a flight mill.

Insect wings are flexible structures that exhibit deformations of complex spatiotemporal patterns. Existing studies on wing deformation underscore the indispensable role of wing deformation in enhancing aerodynamic performance. Here, we investigated forward flight in bluebottle flies, flying semi-freely in a magnetic flight mill; we quantified wing surface deformation using high-speed videography and marker-less surface reconstruction and studied the effects on aerodynamic forces, power and efficiency using computational fluid dynamics. The results showed that flies' wings exhibited substantial camber near the wing root and twisted along the wingspan, as they were coupled effects of deflection primarily about the claval flexion line. Such deflection was more substantial for supination during the upstroke when most thrust was produced. Compared with deformed wings, the undeformed wings generated 59-98% of thrust and 54-87% of thrust efficiency (i.e. ratio of thrust and power). Wing twist moved the aerodynamic centre of pressure proximally and posteriorly, likely improving aerodynamic efficiency.

Characterizing Complex Spatiotemporal Patterns from Entropy Measures.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy (SHp) and a new approach based on Tsallis Spectral Permutation Entropy (Sqs). Notably, our observations reveal the segregation of reaction terms in this SHp×Sqs space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns.

Precipitation-induced filament pattern of injected fluid controlled by a structured cell.

Mixing of two fluids can lead to the formation of a precipitate. If one of the fluids is injected into a confined space filled with the other, then a created precipitate disrupts the flow locally and forms complex spatiotemporal patterns. The relevance of controlling these patterns has been highlighted in the engineering and geological contexts. Here, we show that such injection patterns can be controlled consistently by injection rate and obstacles. Our experimental results revealed filament patterns for high-injection and low-reaction rates, and the injection rate can control the number of active filaments. Furthermore, appropriately spaced obstacles in the cells can straighten the motion of the advancing tip of the filament. A mathematical model based on a moving boundary adopting the effect of precipitation reproduced the phase diagram and the straight motion of filaments in structured cells. Our study clarifies the impact of the nonlinear permeability response on the precipitate density and that of the obstacles in the surrounding medium on the motion of the injected fluid with precipitation.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings.

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Characterizing turbulence structures in convective and neutral atmospheric boundary layers via Koopman mode decomposition and unsupervised clustering

The atmospheric boundary layer (ABL) is a highly turbulent geophysical flow, which has chaotic and often too complex dynamics to unravel from limited data. Characterizing coherent turbulence structures in complex ABL flows under various atmospheric regimes is not systematically well established yet. This study aims to bridge this gap using large eddy simulations (LESs), Koopman theory, and unsupervised classification techniques. To this end, eight LESs of different convective, neutral, and unsteady ABLs are conducted. As the ratio of buoyancy to shear production increases, the turbulence structures change from roll vortices to convective cells. The quadrant analysis indicated that as this ratio increases, the sweep and ejection events decrease, and inward/outward interactions increase. The Koopman mode decomposition (KMD) is then used to characterize their turbulence structures. Our results showed that KMD can reveal non-trivial modes of highly turbulent ABL flows (e.g., transverse to the mean flow direction) and can reconstruct the primary dynamics of ABLs even under unsteady conditions with only ∼5% of the modes. We attributed the detected modes to the imposed pressure gradient (shear), Coriolis (inertial oscillations), and buoyancy (convection) forces by conducting novel timescale and quadrant analyses. We then applied the convolutional neural network combined with the K-means clustering to group the Koopman modes. This approach is displacement and rotation invariant, which allows efficiently reducing the number of modes that describe the overall ABL dynamics. Our results provide new insights into the dynamics of ABLs and present a systematic data-driven method to characterize their complex spatiotemporal patterns.

Pattern selection and the route to turbulence in incompressible polar active fluids

Active fluids, such as suspensions of microswimmers, are well known to self-organize into complex spatio-temporal flow patterns. An intriguing example is mesoscale turbulence, a state of dynamic vortex structures exhibiting a characteristic length scale. Here, we employ a minimal model for the effective microswimmer velocity field to explore how the turbulent state develops from regular, stationary vortex patterns when the strength of activity resp. related parameters such as nonlinear advection or polar alignment strength—is increased. First, we demonstrate analytically that the system, without any spatial constraints, develops a stationary square vortex lattice in the absence of nonlinear advection. Subsequently, we perform an extended stability analysis of this nonuniform ‘ground state’ and uncover a linear instability, which follows from the mutual excitement and simultaneous growth of multiple perturbative modes. This extended analysis is based on linearization around an approximation of the analytical vortex lattice solution and allows us to calculate a critical advection or alignment strength, above which the square vortex lattice becomes unstable. Above these critical values, the vortex lattice develops into mesoscale turbulence in numerical simulations. Utilizing the numerical approach, we uncover an extended region of hysteresis where both patterns are possible depending on the initial condition. Here, we find that turbulence persists below the instability of the vortex lattice. We further determine the stability of square vortex patterns as a function of their wavenumber and represent the results analogous to the well-known Busse balloons known from classical pattern-forming systems such as Rayleigh–Bénard convection experiments and corresponding models such as the Swift–Hohenberg equation. Here, the region of stable periodic patterns shrinks and eventually disappears with increasing activity parameters. Our results show that the strength of activity plays a similar role for active turbulence as the Reynolds number does in driven flow exhibiting inertial turbulence.

Violence Detection in Jails and Mental Asylums

This research investigates the use of Long- term Recurrent Convolutional Networks (LRCNs) for violence detection in video surveillance systems. LRCNs combine the strengths of Convolutional Neural Networks (CNNs) for capturing spatial information and Long Short-Term Memory (LSTM) networks for modeling temporal sequences. This combination allows the system to learn complex spatiotemporal patterns in video data, improving violence detection accuracy in environments like jails and mental health facilities. The project focuses on the integration of the LRCN model with a Telegram bot for real-time alerting and response. Upon detecting violent incidents in the video streams, the LRCN model triggers alerts through the Telegram bot, providing instant notifications to relevant authorities. The Telegram bot facilitates seamless communication and coordination among stakeholders, enabling swift action to mitigate potential risks and ensure the safety of occupants within these facilities. Through rigorous experimentation and evaluation, the effectiveness and reliability of the LRCN-based violence detection system integrated with the Telegram bot are demonstrated. The research contributes to advancing technology-driven solutions for proactive security measures in high-risk environments, fostering safer and more secure institutional settings

Optical soliton solutions in a distinctive class of nonlinear Schrödinger’s equation with cubic, quintic, septic, and nonic nonlinearities

The Biswas–Mollivic equation is a special type of nonlinear Schrödinger equation, which explains the spatio-temporal behaviour of excitable media. In this paper, we investigate the optical soliton solutions of the Biswas–Mollivic equation with cubic–quintic–septic–nonic nonlinearities using the generalized Riccati equation mapping method. This method is efficient and provides new perspectives. It also provides novel insights into the dynamics of excitable media. Our findings add to a better understanding of the complex spatio-temporal patterns that develop in excitable media and have potential applications in the design of new technologies for controlling and manipulating pattern formation. To depict optical soliton solutions graphically, we use the MATLAB software.

Symmetry Breaking of Three Self-Organization Rules: A General Theory for the Origin of Complexity

Complex spatiotemporal patterns in nature significantly challenge reductionism-based modern science. The lack of a paradigm beyond reductionism hinders our understanding of the emergence of complexity. The diversity of countless patterns undermines any notion of universal mechanisms. Here, however, we show that breaking the symmetry of three simple and self-organization rules gives rise to nearly all patterns in nature, such as a wide variety of Turing patterns, fractals, spiral, target and plane waves, as well as chaotic patterns. The symmetry breaking is rooted in the basic physical quantities, such as positive and negative forces, space, time and bounds. Besides reproducing the hallmarks of complexity, we discover some novel phenomena, such as abrupt percolation of Turing patterns, phase transition between fractals and chaos, chaotic edge in traveling waves, etc. Our asymmetric self-organization theory established a simple and unified framework for the origin of complexity in all fields, and unveiled a deep relationship between the first principles of physics and the complex world.

Behind the waterfall - Interdisciplinary results from Holley Shelter and their implications for understanding human behavioral patterns at the end of the Middle Stone Age in southern Africa

Holley Shelter is a Middle (MSA) and Later Stone Age (LSA) site in KwaZulu-Natal, South Africa. The rock shelter is located at the intersection of three ecosystems, in a strip of the Savanna Biome between the Grassland and Indian Ocean Coastal Belt Biomes. Initial excavations in the 1950s by Gordon Cramb yielded large amounts of unifacial points and splintered pieces, as well as organic remains associated with MSA occupations. Our comparative techno-typological evaluation of this material in 2015 suggested the occupations may date to early MIS 3. We then initiated new excavations at the site using modern field methods and controlled sampling for lithic and organic material. Following the initial round of interdisciplinary analysis, we herein report the first absolute ages for Holley Shelter, a detailed technological analysis of new lithic material from the uppermost MSA layers at the site, and a preliminary study of the associated faunal remains. A set of 12 14C dates place these occupations within a constricted chronological period dated to ∼36,000–34,000 cal. BP that overlaps with the so-called ‘final MSA’. The hornfels-dominated lithic assemblages are characterized by diverse core technologies oriented toward the production of laminar products, which include bladelets and abundant splintered pieces. The zooarchaeological analysis documents an emphasis on open grasslands that provided the bulk of the animal prey exploited by the inhabitants of the shelter, with a preference for medium-sized antelopes. Three bone retouchers, a rare occurrence in southern African MSA contexts, were identified in the faunal assemblage. The archaeological deposits at Holley Shelter reflect short-term, potentially seasonal, and specialized occupations that could be influenced by its ecological position. The archaeological material exhibits marked discrepancies with other well-dated final MSA assemblages such as those from Sibhudu, Umbeli Belli, Sibebe and Border Cave. This illustrates considerable variability and a complex spatio-temporal patterning of behavioral adaptations and cultural traditions at the end of the MSA in southern Africa.

Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method

This study presents a Bayesian-driven ensemble learning framework to minimize errors in evapotranspiration (ET) data assimilation, thereby generating a continuous-scale ET estimation with improved accuracy for the data-scarce arid Central Asia (CA). Five state-of-the-art gridded ET products were extensively tested and utilized to reconstruct new long-term ET data at grid scale, including two Global Land Data Assimilation System (GLDAS)-Land Surface Models (LSM) (Catchment and NOAH), two reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications – ver. 2 (MERRA2)), along with the Global Land Evaporation Amsterdam (GLEAM) satellite ET product ver. 3.6b. Taylor Skill Score was employed to evaluate the input gridded ET datasets, using in situ measurements across four arid biomes — cropland, shrubland, farmland, and grassland —as benchmarks. Bayes-Markov Chain Monte Carlo (Bayes-MCMC)-derived logarithmically transformed predictors from the posterior ensemble anomalies were standardized to resolve the mismatches in the cumulative distribution functions of ET data relating to the in situ observations. We found that the constrained residual errors were more profound between 45th and 95th percentiles for the rescaled total ET anomalies across all the input ET datasets at 95 % confidence interval. Moreover, the weighted ensemble estimates derived from the Bayes-MCMC were instrumental in correcting ET anomalies, leading to the robust reconstruction of a more reliable ensemble mean ET data for Central Asia (CAETens) at monthly temporal and 0.25° spatial resolutions, spanning from 2003 to 2020. Overall, CAETens exhibited superior performance, achieving the highest averaged Kling-Gupta Efficiency of 0.75 and registering the lowest Root Mean Square Error of 6.85 mm/m compared to the standalone gridded ET products. Comparisons of spatial distributions of fine-scale features, and evaluations using standard metrics revealed that all ET products varied significantly (p < 0.05) by biome types. Remarkably, only CAETens exhibited a reasonable low positive bias and pronounced ability to resolve the complex spatiotemporal patterns associated with the input gridded ET products. The study bridges the gap in continuous ET monitoring for sustainable water resources management in CA.

A voltage-based Event-Timing-Dependent Plasticity rule accounts for LTP subthreshold and suprathreshold for dendritic spikes in CA1 pyramidal neurons.

Long-term potentiation (LTP) is a synaptic mechanism involved in learning and memory. Experiments have shown that dendritic sodium spikes (Na-dSpikes) are required for LTP in the distal apical dendrites of CA1 pyramidal cells. On the other hand, LTP in perisomatic dendrites can be induced by synaptic input patterns that can be both subthreshold and suprathreshold for Na-dSpikes. It is unclear whether these results can be explained by one unifying plasticity mechanism. Here, we show in biophysically and morphologically realistic compartmental models of the CA1 pyramidal cell that these forms of LTP can be fully accounted for by a simple plasticity rule. We call it the voltage-based Event-Timing-Dependent Plasticity (ETDP) rule. The presynaptic event is the presynaptic spike or release of glutamate. The postsynaptic event is the local depolarization that exceeds a certain plasticity threshold. Our model reproduced the experimentally observed LTP in a variety of protocols, including local pharmacological inhibition of dendritic spikes by tetrodotoxin (TTX). In summary, we have provided a validation of the voltage-based ETDP, suggesting that this simple plasticity rule can be used to model even complex spatiotemporal patterns of long-term synaptic plasticity in neuronal dendrites.

Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming.