Concurrent and complex aerial missions with multiple targets exceed the capabilities of a single cooperative formation of high-speed flight vehicles (HSFVs). To address this challenge, this article decomposes a fleet of HSFVs (subject to multiple compounding factors, including unknown aerodynamic disturbances, unmodeled or parametric uncertainties, actuator faults, and potential intervehicle collisions) into several subgroups and develops a recurrent neural network (RNN) online learning-based prescribed-time safe and robust cooperative group formation control protocol under dynamic event-triggered communication. A distributed prescribed-time event-triggered estimator (DP-TE-TE) is first developed to drive all HSFVs to acquire the convex hull information (i.e., input, velocity, and position) spanned by multiple virtual leader vehicles (VLVs) before grouping or the input, velocity, and position information of their respective single VLV within the group after grouping. Then, based on the constraint-following theory, the safety distance inequality between any potentially colliding pair of HSFVs, along with the first-order differential equation involving the formation position tracking error, is converted into collision motion constraints and prescribed-time trajectory tracking constraints, respectively. To enhance the flight control performance of the swarm, an RNN is constructed for each HSFV to learn the unknown nonlinear function induced by multiple compounding factors, thereby providing online compensation for the subsequent control design. Finally, by integrating the constraint-following errors derived from collision motion constraints and prescribed-time trajectory tracking constraints, the RNN compensation term, and the estimated information, the prescribed-time safe and robust cooperative group formation control scheme (P-TSRCGFCS) is proposed. In the simulation examples, the effectiveness of the proposed algorithms is verified by dividing 12 HSFVs and three VLVs into three subgroups to perform the desired cooperative group formation task.

Recurrent neural network (RNN) is a neurodynamic method designed to tackle time-varying problems in various technical domains, which are widely derived from scientific research and practical applications. It should be noted that traditional models often lack an effective capability to suppress nonlinear time-varying noise during the design process, and thus may encounter many difficulties in practical applications. This article presents a novel RNN model for solving the continuous time-varying matrix pseudoinverse, which has a significant characteristic of double integral-reinforcing (DIR) term and is termed DIR continuous-time RNN (DIR-CT-RNN) model. Correspondingly, using the discretization formula, a DIR discrete-time RNN (DIR-DT-RNN) is presented for solving the discrete time-varying matrix pseudoinverse. The theoretical results present that the DIR-DT-RNN model converges toward the theoretical solution under the discrete time-unvarying constant (DTU-C) noise or discrete time-varying linear (DTV-L) noise interference. Under the discrete time-varying quadratic (DTV-Q) noise interference, the proposed model converges to a constant that relates to the design parameters. In addition, simulation results, including an application for trajectory tracking of three-link robotic manipulator, which come from practical engineering background, verify the effectiveness and superiority of DIR-DT-RNN model for solving the time-varying matrix pseudoinverse under various types of noise interference.

A recurrent neural network (RNN) system based on a Hopfield neural network (HNN) was developed to extract essential spectral patterns from the time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of peptide samples. Because ToF-SIMS produces various fragment ions from organic molecules, the interpretation of ToF-SIMS spectra is generally complicated. ToF-SIMS is useful for peptide analysis because it detects specific amino acid fragment ions from peptides that indicate peptide information. However, the ToF-SIMS spectra also contain fragment ions that do not preserve the main structures of the original molecules, which makes them difficult to interpret. Therefore, it is crucial to extract essential spectral patterns from the ToF-SIMS spectra of organic materials. Peptides were selected as the target organic materials for this study due to their systematic chemical structures. A modified HNN was trained on the ToF-SIMS spectra of each peptide, and the trained HNNs were then used to recall patterns for various peptide ToF-SIMS spectra. The results show that the modified HNN recall essential spectra containing specific ions, including the protonated molecular ions and amino acid fragment ions of target peptides. Furthermore, the HNN results revealed differences and similarities between peptides with similar and different amino acid sequences. Thus, this study demonstrates the effectiveness of the HNN in interpreting complex spectra and its potential for preprocessing data for further analysis.

Artificial intelligence-assisted technology to reduce turnaround time for rapid diagnosis of infectious diseases.

The transition to sustainable energy positions wind power as a key renewable solution. As demand grows, wind turbines are deployed across diverse terrains. However, wind’s stochastic nature and environmental variability complicate power forecasting, affecting grid stability. The study leverages data-driven techniques to enhance wind power forecasting using high-resolution SCADA system time-series data. Key operational parameters include wind speed, rotor speed, generator speed, nacelle orientation, ambient temperature and power output. A comparative analysis evaluates traditional machine learning models—Linear Regression, Decision Trees, Random Forests, Gradient Boosting and Support Vector Machines—against deep learning models like Long Short-Term Memory (LSTM) networks and a novel Recurrent Neural Network (RNN) architecture. The core contribution is an optimized Bidirectional LSTM-RNN model with permutation layers and attention. These layers capture long-range dependencies and nonlinear interactions in wind data. The structure improves long-range dependency capture and nonlinear interaction modeling. Bidirectionality enables learning from both past and future time steps, while attention mechanisms highlight critical temporal features. Experimental results demonstrate the proposed model’s superior performance, achieving a Mean Absolute Error (MAE) of 0.0994 and Root Mean Square Error (RMSE) of 0.1390, significantly outperforming traditional models (e.g., Random Forest: MAE 86.44, RMSE 220.30) and basic LSTM models (MAE 14.48, RMSE 15.27). Robust cross-validation confirms its ability to generalize across different temporal segments. Feature importance analysis improves interpretability, supporting informed decision-making in wind farm operations. The framework is scalable, modular and well-suited for real-time forecasting applications. The work presents a reliable deep learning model for wind power forecasting, enabling intelligent, data-driven energy management in modern power systems.

Artificial intelligence-based analysis of flow and heat transfer in blood-inspired Casson hybrid nanofluids via recurrent neural networks with Bayesian regularization

Hybrid propulsion systems increase ship energy efficiency by allowing the sharing of power between diesel engines and battery energy storage systems. However, the long-term efficiency of these types of systems depends on accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries to allow effective charge scheduling, maintenance planning, and reliable navigation. This study uses nine data-driven algorithms, including ensemble methods, recurrent neural networks, and linear models, to examine the RUL of a lithium-ion battery pack installed on a hybrid cargo ship. A 5-fold cross-validation structure was used to preprocess, normalize, and analyze actual operational data gathered during the vessel's service life. To improve the accuracy of predictions, hyperparameter optimization was performed out. Long Short-Term Memory (LSTM), which reduced MAE from 2.87 to 1.46 and RMSE from 12.57 to 6.34 after optimization while retaining a high coefficient of determination (R² = 0.9999), performed the best among the models that were evaluated. The results obtained indicate that condition-based maintenance and energy utilization methods on hybrid ships can be effectively supported by data-driven RUL estimation. In order to enhance generalization and assess integration with real-time propulsion control systems, future research will expand the analysis to multi-vessel datasets.

Cancer remains a leading cause of death globally, with nearly 10 million deaths in 2020. Advances in genomic technologies have revolutionized cancer research, shifting focus towards precision medicine based on comprehensive tumour genomic profiling. Concurrently, deep learning (DL) has emerged as a powerful paradigm for complex biological data. This review critically assesses recent advances in DL applications for tumour genomics, emphasizing four key domains: DNA sequencing analysis for mutation detection, gene expression profiling for cancer subtype classification, methylation function prediction for epigenetic characterization and integrative multi-omics approaches for comprehensive tumour profiling. We systematically analyse how different DL architectures-including convolutional neural networks, recurrent neural networks, graph neural networks, autoencoders and transformers-address specific challenges in cancer genomics. Our review highlights how these approaches significantly enhance detection sensitivity for genomic alterations, improve cancer subtype stratification, identify novel biomarkers and optimize therapeutic target selection. We examine technical challenges in DL implementation, including model interpretability, data scarcity, computational requirements and integration issues, alongside emerging solutions such as explainable AI, federated learning, and multi-modal frameworks. By synthesizing methodological innovations and identifying research directions, this review provides bioinformaticians and cancer researchers with a roadmap for leveraging DL to advance precision oncology.

This work presents an optimized Field-Programmable Gate Array (FPGA) implementation of an Echo State Network (ESN), a type of Recurrent Neural Network (RNN), to predict the chaotic signal of the Lorenz system. The focus is on executing AI models efficiently on resource-limited devices like those in the Internet of Things (IoT). In contrast to previous state-of-the-art works that used the tanh activation function (which required complex hardware approximations), this proposal employs the simpler ReLU function. This choice eliminates the need for approximations and, when combined with an optimized network architecture, enables a drastic reduction in resource usage. Specifically, the optimization achieved three key reductions: a) The reservoir size, decreased from 50 to 3; b) The connectivity matrix, transitioned from a dense matrix of 2500 values to a diagonal matrix with only 3 non-zero values; and c) Precision, reducing the operational bit-width from 32 to 23 bits. As a result, the optimized ESN is significantly more efficient on FPGA hardware, successfully reducing utilized resources while simultaneously achieving improved performance compared to prior state-of-the-art implementations. • We apply a Network Search Architecture with Echo State Networks (ESN) to predict the chaotic signal of Lorenz system. • A very small network with a size of 3 neurons in the reservoir is ob- tained. • An implementation of the obtained ESN in FPGA is presented.

Gate recurrent unit neural network inverse model prediction-based dynamic multi-objective evolutionary algorithm and application

An accurate and efficient neuromorphic computing framework with UKF-assisted spiking recurrent neural network for fault detection in UAV

Computer-aided lung cancer classification on computed tomography imaging using optimized heterogeneous bi- directional recurrent neural network

Multiscale analysis of woven composites using hierarchical physically recurrent neural networks

Automated artificial intelligent approach for enhancing bone cancer detection through hybrid feature extraction and adaptive elman recurrent neural network

Soft sensor for nonuniform sampling nonlinear dynamic process using irregular-time-interval latent probabilistic predictability embedding supervised deep network.

Complex refractive indices of materials encode fundamental information on light-matter interactions and are critical for the design of advanced photonic and optoelectronic devices. In many emerging materials, refractive indices change under external stimuli such as temperature, electric fields, or strain. Tracking these changes in-operando is critical for active photonic and optoelectronic device design, but remains challenging. Conventional methods such as ellipsometry rely on labor-intensive model fitting and are often impractical for multilayer stacks or in-operando measurements. Optical reflectometry offers a simpler alternative but suffers from ambiguous extraction of refractive index from reflectance spectra and limited applicability under dynamic modulation. Here, we present ReflectoRNN, an artificial intelligence (AI)-powered reflectometry framework based on recurrent neural networks (RNN), for real-time extraction of complex refractive indices in evolving materials. ReflectoRNN extracts refractive indices from reflectance spectra under thermal, electrical, magnetic, or mechanical stimuli. It achieves a median Pearson's correlation coefficient (PCC) of 0.998 and a relative accuracy score (RAS) of 0.968 on generated datasets. Validation experiments on MoS2 and WS2 across diverse substrates, including single-layer and multilayer dielectric stacks, and distributed Bragg reflectors (DBRs), demonstrate high accuracy and physical consistency, with temperature-dependent exciton resonance energy matching Bose-Einstein predictions. ReflectoRNN enables in-operando optical characterization of materials across complex photonic structures and offers a pathway toward automated, real-time monitoring and accelerated materials discovery.

Dengue fever is a major climate-sensitive mosquito-borne disease shaped by both environmental conditions and human behavior. We quantified the combined effects of climate variability and COVID-19 non-pharmaceutical interventions (NPIs) on dengue transmission in Guangdong, China, using an integrated modeling framework that combines climate-driven mosquito abundance estimated by recurrent neural networks (RNNs) with a temperature- and intervention-sensitive Susceptible-Infected-Recovered (SIR) model. Mosquito surveillance, meteorological data, NPI intensity, and reported dengue cases from 2016 to 2023 were used for model calibration within a partially observed Markov process framework. The calibrated model reproduced observed interannual dengue fluctuations using shared, rather than year-specific, fitted parameters.SHAP (SHapley Additive exPlanations) analysis reveals the nonlinear and day-night thermal effects on mosquito abundance, identifying minimum temperature as the relatively more influential climatic driver, while mechanistic experiments showed that temperature-dependent biting rates critically influenced dengue transmission. COVID-19 NPIs substantially suppressed dengue incidence in Guangdong during the pandemic period, corresponding to an estimated 99.03% (95% CI: 94.54-99.68%) reduction relative to the weak-intervention counterfactual scenario. By integrating explainable climate-driven mosquito abundance prediction with mechanistic transmission modeling and counterfactual NPI experiments, our framework explicitly separates and quantifies the roles of mosquito abundance, temperature-dependent effective biting, and human interventions, providing a practical tool for climate-informed early warning and intervention assessment for dengue and other climate-sensitive mosquito-borne diseases.

Land subsidence, a complex phenomenon with extensive environmental and economic consequences, presents a significant challenge in the Silakhor Plain, Lorestan Province. This study investigates the effective factors on subsidence, including land use, drainage density, soil properties, and groundwater level changes, using machine learning and deep learning models. The results indicate that soil properties, excessive groundwater exploitation, and human infrastructure are the primary drivers of subsidence, with the central and southeastern parts of the plain facing the highest risk due to the high density of water wells and proximity to active faults. Among the four advanced models utilized: Random Forest (RF), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), the RF model demonstrated the best performance in predicting subsidence intensity, exhibiting the highest coefficient of determination (R2 = 0.9880) and the lowest error (MSE = 0.0516). Furthermore, in the classification of subsidence-prone and non-subsidence areas, RF showed significant superiority with a precision of 0.9929 and a recall of 0.9999. The deep learning models also demonstrated strong performance. The LSTM model, with an R-squared of 0.9792, and the CNN model, with an R-squared of 0.9695, showed excellent capability in predicting subsidence intensity and identifying subsidence-prone areas (with a Recall of 1 for both models). However, their specificity was lower compared to the RF model. In contrast, the RNN model provided poorer performance in both prediction and classification compared to other models. SHAP analysis further confirmed these findings, indicating that soil properties, surface water resources, and human infrastructure-related factors play the most significant roles in subsidence occurrence. The findings of this research can serve as a basis for future planning in water and land resource management, as well as for developing effective strategies to mitigate subsidence risks in the Silakhor Plain and similar regions.

Chemometrics, VIS-NIR-SWIR spectroscopy, and deep learning algorithms to classify and predict qualitative attributes of rice in grain processing and storage units.

The ongoing global energy transition has intensified the need for precise modeling of renewable energy systems, especially photovoltaic–thermal (PV/T) systems that have the ability to produce both electrical and thermal energy. Improving the efficiency and reliability of PV/T systems is a key enabler of the transition toward sustainable energy. Accurate forecasting of their thermal performance is therefore essential to maximize renewable energy use and reduce energy losses. A deep learning-based method is proposed in this study for the prediction of the thermal efficiency of an air-based PV/T system. More specifically, temporal deep learning architectures are investigated to exploit the complex nonlinear relationships and temporal dependencies governing the thermal behavior of the PV/T collector. A comprehensive comparative analysis is conducted using four state-of-the-art architectures, namely Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Furthermore, the influence of sequence length is examined through a sensitivity analysis considering forecasting horizons of 1 h, 6 h, 12 h, and 24 h. The models are evaluated using the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results demonstrate that forecasting performance is strongly influenced by the selected temporal horizon. Among the investigated configurations, the 24-h horizon provided the most informative temporal context for thermal efficiency prediction. Under this common forecasting horizon, the LSTM model achieved the highest predictive accuracy, reaching an R2 of 0.9952, an RMSE of 0.5975, and an MAE of 0.2364, outperforming the TCN, GRU, and Transformer architectures. The residual error and convergence analyses further highlighted the effectiveness of recurrent neural networks in capturing the thermal dynamics of the investigated PV/T system. By enabling accurate and reliable thermal efficiency forecasting, the proposed framework supports improved energy management, higher energy efficiency, and a stronger integration of renewable energy systems, thus contributing to more sustainable operation of hybrid solar energy technologies.