This was one of those times

Imagine a digital ecosystem where autonomous software agents, driven by vast neural networks, are tasked with optimizing energy grids, managing supply chains, and even trading commodities.

Attention forms a fundamental component of visual processing. The locus and spread of covert attention reveals how specific locations or features in the environment are preferentially processed, howev...

In a new study published in the journal Virtual Reality, University of Maryland scientists used electroencephalography (EEG) to identify changes in brain wave activity while participants viewed virtua...

Being rejected by peers is especially painful for teens with depression. We measured brain responses to peer acceptance and rejection in 179 adolescents, then tracked their mood through their smartpho...

Experiments on an underactuated two-degree-of-freedom control moment gyroscope found that online adaptive neural control improved tracking-error performance by 89.7% under induced disturbances.

Rhythmic auditory stimulation in early life rarely occurs in isolation. Caregivers naturally combine singing and speech with rhythmic touch, rocking, and visual cues. Newborns are sensitive to auditor...

Scientific Reports, Published online: 18 October 2026; doi:10.1038/s41598-026-75162-3Comparative performance analysis of adaptive neuro-fuzzy inference system, fuzzy logic, and neural network-based maximum power point tracking controllers for permanent magnet synchronous generator-based variable-speed wind energy conversion systems

PV systems require efficient maximum power tracking to operate under dynamic environmental conditions. Importance of PV systems The photovoltaic (PV) system stands out as one of the most critical renewable energy technologies because it directly converts sunlight to electric power with a high level of reliability and no negative environmental impact. The drive for clean and sustainable sources of energy globally and the dwindling supply of conventional fossil fuels has led to increased use of PV systems in residential, commercial, industrial and smart grid applications. Benefits of PV systems include little maintenance, scalability, silent operation and lower emissions of greenhouse gases. Improvements in power electronic converters, energy storage systems and intelligent maximum power point tracking (MPPT) techniques have greatly enhanced the efficiency and stabilization of PV generation under fluctuating environmental conditions such as partial shading, dust accumulation and temperature changes. Therefore, PV systems are critical facilitators of providing sustainable energy, reducing carbon footprints and moving towards the development of modern intelligent energy management systems. Need for MPPT Maximum Power Point Tracking (MPPT) is an important control method in photovoltaic (PV) systems to ensure that maximum available power is obtained during fluctuations in environmental conditions. The output characteristics of PV panels...

Concurrent advances in artificial intelligence (AI), large-scale neuronal recording, and behavioral tracking have enabled increasingly powerful predictive models of brain-behavior relationships. Yet predictive accuracy does not automatically yield interpretable principles of neural organization, particularly during natural behavior, where the relevant variables computed are often unknown in advance. Here, we developed a functional digital twin of the hippocampus, trained jointly on large-scale neural activity and self-directed three-dimensional flight in freely flying bats. A multimodal, multitask transformer accurately predicted both neural activity and behavior, outperformed standard approaches, and generalized across recording sessions. Interrogating the model's representations suggested a form of hippocampal generalization across self-selected trajectories that was not apparent in physical coordinates, but was plainly visible in the model's low-dimensional latent space. We confirmed this organization directly in the recorded neural activity: neurons fired at corresponding stages of spatially distinct paths connecting the same origin and destination, preserving their relative firing order even as their firing fields shifted with the path. Analysis of neuron-specific readouts further identified functional relationships among neurons that generalized from flight to waking rest and sleep, brain states never used for training, and differed systematically between the hippocampal subregions CA1 and CA3. Together, these results show how AI-based functional digital twins can complement conventional analysis by generating testable hypotheses about neural organization during natural behavior.

Scientific Reports, Published online: 12 October 2026; doi:10.1038/s41598-026-72808-0Dynamic periodic event-triggered adaptive PD-like composite neural control with iterative disturbance observer for 3D tracking of UUVs

Concurrent advances in artificial intelligence (AI), large-scale neuronal recording, and behavioral tracking have enabled increasingly powerful predictive models of brain-behavior relationships. Yet predictive accuracy does not automatically yield interpretable principles of neural organization, particularly during natural behavior, where the relevant variables computed are often unknown in advance. Here, we developed a functional digital twin of the hippocampus, trained jointly on large-scale neural activity and self-directed three-dimensional flight in freely flying bats. A multimodal, multitask transformer accurately predicted both neural activity and behavior, outperformed standard approaches, and generalized across recording sessions. Interrogating the model's representations suggested a form of hippocampal generalization across self-selected trajectories that was not apparent in physical coordinates, but was plainly visible in the model's low-dimensional latent space. We confirmed this organization directly in the recorded neural activity: neurons fired at corresponding stages of spatially distinct paths connecting the same origin and destination, preserving their relative firing order even as their firing fields shifted with the path. Analysis of neuron-specific readouts further identified functional relationships among neurons that generalized from flight to waking rest and sleep, brain states never used for training, and differed systematically between the hippocampal subregions CA1 and CA3. Together, these results show how AI-based functional digital twins can complement conventional analysis by generating testable hypotheses about neural organization during natural behavior.