Introduction: Resting-state EEG (rsEEG) is a scalable window onto trait-like “executive readiness,” but findings have been fragmented by task impurity on the executive-function (EF) side and heterogeneous EEG pipelines. This review synthesizes rsEEG features that reliably track EF in healthy samples across development and aging and evaluates moderators such as cognitive reserve. Materials and methods: Following PRISMA 2020, we defined PECOS-based eligibility (human participants; eyes-closed/eyes-open rsEEG; spectral, aperiodic, connectivity, topology, microstate, and LRTC features; behavioral EF outcomes) and searched MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, and IEEE Xplore from inception to 30 August 2025. Two reviewers were screened/double-extracted; the risk of bias in non-randomized studies was assessed using the ROBINS-I tool. Sixty-three studies met criteria (plus citation tracking), spanning from childhood to old age. Results: Across domains, tempo, noise, and wiring jointly explained EF differences. Faster individual/peak alpha frequency (IAF/PAF) related most consistently to manipulation-heavy working may and interference control/vigilance in aging; alpha power was less informative once periodic and aperiodic components were separated. Aperiodic 1/f parameters (slope/offset) indexed domain-general efficiency (processing speed, executive composites) with education-dependent sign flips in later life. Connectivity/topology outperformed local power: efficient, small-world-like alpha networks predicted faster, more consistent decisions and higher WM accuracy, whereas globally heightened alpha/gamma synchrony—and rigid high-beta organization—were behaviorally sluggish. Within-frontal beta/gamma coherence supported span maintenance/sequencing, but excessive fronto-posterior theta coherence selectively undermined WM manipulation/updating. A higher frontal theta/beta ratio forecasts riskier, less adaptive choices and poorer reversal learning for decision policy. Age and reserve consistently moderated effects (e.g., child frontal theta supportive for WM; older-adult slow power often detrimental; stronger EO ↔ EC connectivity modulation and faster alpha with higher reserve). Boundary conditions were common: low-load tasks and homogeneous young samples usually yielded nulls. Conclusions: RsEEG does not diagnose EF independently; single-band metrics or simple ratios lack specificity and can be confounded by age/reserve. Instead, a multi-feature signature—faster alpha pace, steeper 1/f slope with appropriate offset, efficient/flexible alpha-band topology with limited global over-synchrony (especially avoiding long-range theta lock), and supportive within-frontal fast-band coherence—best captures individual differences in executive speed, interference control, stability, and WM manipulation. For reproducible applications, recordings should include ≥5–6 min eyes-closed (plus eyes-open), ≥32 channels, vigilant artifact/drowsiness control, periodic–aperiodic decomposition, lag-insensitive connectivity, and graph metrics; analyses must separate speed from accuracy and distinguish WM maintenance vs. manipulation. Clinical translation should prioritize stratification and monitoring (not diagnosis), interpreted through the lenses of development, aging, and cognitive reserve.

Accurate building footprint is a fundamental element of large-scale base maps, which serve as critical inputs for urban planning, infrastructure development, environmental monitoring, and disaster management. While building footprint extraction and geometric regularization have been widely studied, their combined application for automated, large-scale basemap generation using very-high-resolution satellite imagery has received limited attention. To address this gap, this study proposes an integrated framework that leverages deep learning and geometric regularization to efficiently extract and refine building footprints for large-scale base maps. The framework first enhances spectral, spatial, and textural features of very-high-resolution satellite imagery through pan-sharpening, NDVI computation, GLCM-based texture analysis, and PCA. A Mask R-CNN model is then trained on multi-band imagery to segment building footprints, followed by geometric regularization to simplify and align polygons along dominant structural orientations. Object-based evaluation on ground-truth buildings demonstrates high performance, with 97.6% precision, 91.6% recall, and a 94.5% F1-score. The proposed systematic framework substantially reduces production time compared to manual stereo-plotting, requiring less than an hour per 5.29 km2 map sheet in operational production, representing a more than 35-fold efficiency gain. While minor geometric inaccuracies and merged adjacent buildings persist, the methodology offers a robust, scalable, and efficient approach to support large-scale base map production.

Abstract With tens to hundreds of spectra of white dwarfs being taken each night from multi-object spectroscopic surveys, automated spectral classification is essential as part of efficient data processing. In this study, we design a neural network to classify the spectral type of white dwarfs using a combination of spectra from the Dark Energy Spectroscopic Instrument (DESI) data release 1 and imaging from Pan-STARRS photometry. The trained network has a near 100 % accuracy at identifying DA and DB white dwarf spectral types, while having an 85–95 % accuracy for identifying all other primary types, including metal pollution. Distinct spectral or photometric features map into separate structures when performing a Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction. Investigating further and looking at multiple epoch spectra, we performed a separate search for objects that have strongly changing spectral signatures using UMAP, discovering 3 new inhomogeneous surface composition (‘double-faced’) white dwarfs in the process. We lastly show how machine learning has the potential to separate single white dwarfs from double white dwarf binary star systems in a large dataset, ideal for isolating a single star population. The results from all of these techniques show a compelling use of machine learning to boost efficiency in analysing white dwarfs observed in multi-object spectroscopy surveys, at times replacing the need for human-driven spectral classifications. This demonstrates our techniques as powerful tools for batch population analyses, finding outliers as a form of rare subclass detection, and in conducting multi-epoch spectral analyses.

Timely monitoring of corn growth at early stages is essential for food security and agricultural management, yet most existing mapping approaches depend on mature-stage spectral features, delaying operational applications. This study introduces TCBA-ViT, a hybrid framework that integrates convolutional neural networks and Vision Transformers, enhanced with dual-path Convolutional Block Attention Module (CBAM) and temporal attention, to jointly capture local spectral details and global temporal dynamics from multi-temporal Sentinel-2 imagery. Using six years (2019-2024) of data from the U.S. Corn Belt, TCBA-ViT reliably identified corn as early as June (V7 stage, four weeks after seeding) and achieved stable accuracies above 90% by late July, nearly two months before physiological maturity. Cross-year experiments demonstrated robustness to interannual variability and crop rotation, while cross-regional tests confirmed strong spatial generalization, maintaining F1-scores above 0.85 within 250 km and above 0.80 within 450 km. Compared with existing baseline models, TCBA-ViT consistently delivered earlier and more accurate classification across years and regions. Ablation analyses further highlighted the indispensable contributions of CBAM and temporal attention to performance gains. By addressing the questions of how early corn can be classified and how far models can generalize, this study provides a validated framework for early-season dynamic crop classification and large-scale agricultural monitoring, supporting sustainable decision-making.

Raman spectroscopy has shown great promise in classifying biomedical samples. While conventional spectral analysis techniques have been employed to extract meaningful patterns from Raman data, emerging developments in machine learning (ML) offer new opportunities to advance the field. We employed Raman spectroscopy (RS) to analyze hippocampal tissue sections from rats subjected to chronic constriction injury (CCI) exposed to Cannabidiol (CBD) vapor versus Sham controls. Through examination of spectral features in the Raman data, we detected distinct molecular bond signatures indicative of changes in key biomolecules, including proteins, lipids, and nucleic acids. These signatures offer insights into CBD vapor-induced biochemical alterations in the hippocampus and have potential relevance to neuropathic pain mechanisms linked to CCI. Our Raman-based approach enabled us to capture information about biochemical modifications in the hippocampal region exposed to CBD vapes. We applied supervised machine learning (ML) models (Random Forest, SVM) to classify Raman spectra obtained from the brain tissues. The machine learning algorithms effectively captured patterns in the spectral data, enabling us to accurately differentiate between treated and control groups. Our models achieved high classification performance, showing that CBD vapor exposure induces distinct biochemical alterations in brain tissue detectable via Raman spectroscopy. Our results support the use of ML as a powerful tool for spectral analysis in complex biological systems.

Abstract The James Webb Space Telescope (JWST) has unveiled a population of enigmatic, compact sources at high redshift known as “little red dots” (LRDs), whose physical nature remains a subject of intense debate. Concurrently, the rapid assembly of the first supermassive black holes (SMBHs) requires the formation of heavy seeds, for which supermassive stars (SMSs) are leading theoretical progenitors. In this work, we perform the first quantitative test of the hypothesis that LRDs are the direct observational manifestation of these primordial SMSs. We present a novel, first-principles pipeline generating synthetic spectra for a nonrotating, metal-free SMS up to 10 6 M ⊙ . We establish that its luminosity ( L λ ≈ 1.7 × 10 44 erg s −1 μ m −1 at 4050 Å) provides a decisive constraint, matching prominent LRDs. Our model self-consistently reproduces their defining spectral features: the V-shaped Balmer break morphology is shown to be an intrinsic photospheric effect, while the complex line phenomenology, strong H β in emission with other Balmer lines in absorption arises from non-LTE effects in a single stellar atmosphere. With wind and macroturbulent broadening, we match LRD spectra at z = 7.76 and z = 3.55, including the H β width of MoM-BH*-1 to within 4%. We predict a luminosity-dependent observability window, ∼10 4 yr for the most luminous systems and 10 5 –10 6 yr if L λ (4050 Å) is lower by 1–2 dex. These results provide a self-consistent alternative to multicomponent obscured active galactic nucleus scenarios and suggest JWST may be witnessing luminous stages of SMBH progenitors before collapse.

An enhanced convolutional neural network architecture for nondestructive detection of microbial contamination on eggshells through hyperspectral imaging.

We present that the broad feature usually observed in X-ray spectra at around 6.4 keV can be explained by ray-traced emission from the two-slab system containing a dissipative, warm corona on the top of an accretion disk in an active galactic nucleus (AGN). Such an accretion flow is externally illuminated by X-ray radiation from a lamp located above a central supermassive black hole (SMBH). Thermal lines from highly ionized iron ions (FeXXV and FeXXVI) caused by both internal heating and reflection from the warm corona, can be integrated into the observed broad line profile due to the close vicinity to the SMBH. We investigate the dependence of the total broad line profile on the variations in black hole spin parameter, viewing angle, lamp height, and dissipation factor. Our results introduce a new method to probe properties of warm corona using high-resolution spectroscopic measurements with current and future X-ray missions. XRISM NewATHENA We use photoionization code to compute local ion population and emission line profiles, and ray-tracing code to include relativistic effects on the outgoing X-ray spectrum. TITAN GYOTO In our models, the temperature of the inner atmosphere covering the disk can reach values of 10^7 - 10^8 K due to warm corona dissipation and external illumination, which is adequate for generating highly ionized iron lines. These lines can undergo significant gravitational redshift near the black hole, leading to a prominent spectral feature centered around 6.4 keV. For all computed models, relativistic corrections shift highly ionized iron lines to the 6.4 keV region, usually attributed to fluorescent emission from the illuminated skin of an accretion disk. Hence, for a warm corona that covers the inner disk regions, the resulting theoretical line profile under strong gravity is a sum of different iron line transitions, with highly ionized iron contributing the most to the total line profile observed in an AGN.

Advancing quinoa(Chenopodium quinoa Willd.) quality assessment using hyperspectral imaging.

Unveiling clouded consciousness: broad-band EEG slowing tracks recovery from Post-Traumatic Confusional State.

Fractionalization of the electron charge e is one of the most striking phenomena arising from strong electron-electron interactions. A celebrated example is the emergence of anyons with fractional charges in fractional quantum Hall effect (FQHE) states1-13. Recently, zero-field fractional Chern insulators (FCIs)14-19, lattice analogues of the FQHE states that form without Landau levels, have been realized20,21. FCIs provide a unique platform to investigate anyons, yet their detection remains a challenge. Here we report the observation of anyon-trions, a new type of excitonic complex formed by binding a trion with a fractional charge in twisted MoTe2 bilayers. Photoluminescence spectroscopy of quantum-confined excitons reveals emergent peaks that appear only within slightly doped FCI states. The new spectral features are red-shifted relative to the trions in undoped FCIs, but share the same electric field, temperature and magnetic field dependence. These observations suggest their origin as trions binding with elementary quasi-particles, that is, anyon-trions. Crucially, the ratio of binding energies between the anyon-trions in the -2/3 and -3/5 FCI states matches the expected fractional charge ratio of e/3 to e/5. This provides strong evidence for fractional charges in FCI-an essential property of anyons. Our results address a fundamental question in FCI physics and establish trion spectroscopy as a powerful probe of fractionally charged excitations, complementary to transport- and tunnelling-based approaches.

Abstract The complex phase diagram of 1T-TiSe 2 consists of a charge density wave (CDW) below 200 K, and CDW fluctuations of still unknown origin at higher temperatures. Here, we use time-resolved extreme ultraviolet momentum microscopy and density functional perturbation theory to uncover the formation mechanism of CDW fluctuations and their spectral features at 295 K. We investigated the transient dynamics of fluctuations upon nonresonant ultrafast photoexcitation, and directly correlate it with the CDW soft-phonon hardening. Surprisingly, our results show that the coherent amplitude mode modulating ultrafast CDW recovery persists above T CDW , and reveal that CDW fluctuations are dominated by the electron-phonon interaction rather than excitonic correlations as commonly believed. Our findings on these microscopic CDW fluctuations clarify the complex interplay between electronic and lattice degrees of freedom at elevated temperatures and, therefore, could be useful in understanding the nature of the CDW phase transition in 1T-TiSe 2 and similar quantum materials.

Optimized Hybrid Texture and Spectral Feature Selection Using Hippopotamus Optimization Algorithm in Hyperspectral Imaging

Few-site implementations of the Kitaev chain offer a minimal platform to study the emergence and stability of Majorana bound states. Here, we realize two- and three-site chains in semiconducting quantum dots coupled via superconductors, and tune them to the sweet spot where zero-energy Majorana modes appear at the chain ends. We demonstrate control of the superconducting phase through both magnetic field and sweet-spot selection, and fully characterize the excitation spectrum under local and global perturbations. All spectral features are identified using the ideal Kitaev chain model. To assess Majorana localization, we couple the system to an additional quantum dot. The absence of energy splitting at the sweet spot is compatible with high-quality Majorana modes, despite the modest chain size.

We analyze the degradation of the C60 fullerene under the action of hypochlorite (ClO-). The ClO- anion, produced by the human myeloperoxidase (hMPO) enzyme, is highly reactive and known for destroying bacteria and degrading nanostructured materials. In particular, previous studies show that hMPO can biodegrade nC60 nanoparticles in a short time, with hypochlorite playing a key role, though the exact mechanism is still unknown. In this work, we use density functional theory (DFT) calculations to investigate ClO- adsorption on water-covered fullerenes. We find that there is a strong tendency of hypochlorite to dissociate rather than remain molecularly adsorbed near the hydrated C60 surface. As a consequence of this reaction, the fullerene cage can be oxidized through the adsorption of carbonyl, epoxy, molecular O2, and ClO groups preferentially located in close proximity on the carbon network, while individual chloride ions remain hydrated and stabilized in the aqueous environment. The formation of domains of chemisorbed oxygen species, as reported here, reduces the number of C═C double bonds in the cage, thereby decreasing the structural stability of C60. Chlorination of the carbon surface is not energetically favored following ClO- bond cleavage. Most interestingly, our calculations reveal that the oxidation of the fullerene surface is frequently accompanied by the breaking of C-C bonds beneath the oxidized regions, resulting in hole formation in the carbon cage. We performed simulations of NMR, UV-vis, and ECD spectroscopies, which reveal well-defined spectral features that could be very helpful in identifying the structural transformations and chemical composition reported here for these nanosized carbon materials. According to our proposed atomistic mechanism, the dissociative adsorption of hypochlorite at various regions on the carbon network, along with the formation of molecular islands composed of oxidizing species, may lead to a generalized porous morphology of the cage consistent with experimental observations of significant structural transformations in hMPO exposed C60 solutions.

Rapid and accurate mapping of brain tissue pH is crucial for early diagnosis and management of ischemic stroke. Amide proton transfer (APT) imaging has been used for this purpose but suffers from hypointense contrast and low signal intensity in lesions. Guanidine chemical exchange saturation transfer (CEST) imaging provides hyperintense contrast and higher signal intensity in lesions at appropriate saturation power, making it a promising complementary approach. However, quantifying the guanidine CEST effect remains challenging due to its proximity to water resonance and the influence of multiple confounding effects. This study presents a machine learning (ML) framework to improve the accuracy and robustness of guanidine CEST quantification with reduced scan time. The model was trained on partially synthetic data, where measured line-shape information from experiments were incorporated into a simulation framework along with other CEST pools whose solute fraction (fs), exchange rate (ksw), and relaxation parameters were systematically varied. Gradient-based feature selection was used to identify the most informative frequency offsets to reduce the number of acquisition points. The proposed model achieved significantly higher accuracy than polynomial fitting, multi-pool Lorentzian fitting, and ML models trained solely on synthetic or in vivo data. Gradient-based feature selection identified the most informative frequency offsets, reducing acquisition points from 69 to 19, a 72% reduction in CEST scan time without loss of accuracy. In vivo, conventional fitting methods produced unclear lesion contrast, whereas our model predicted clear hyperintense lesion maps. The strong negative correlation between guanidine and APT effects supports its physiological relevance to tissue acidosis. The use of partially synthetic training data combines realistic spectral features with known ground-truth values, overcoming limitations of purely synthetic or limited in vivo datasets. Leveraging this data with ML, enables robust quantification of guanidine CEST effects, showing potential for rapid pH-sensitive imaging.

Analysis of EEG univariate features for epileptic seizures.

Nocturnal non-speech sounds encapsulate critical physiological and behavioral information, making them a valuable modality for non-invasive assessment of sleep quality. Despite this potential, existing approaches predominantly rely on single-view spectral features or shallow learning architectures, limiting their ability to generalize across diverse acoustic patterns. To overcome these limitations, this study proposes a hybrid deep learning architecture tailored for the classification of seven distinct nocturnal sound categories. The system employs a tri-branch design that independently processes Mel-frequency cepstral coefficients (MFCC), Mel-spectrogram, and constant-Q transform (CQT)-spectrogram representations. Each branch passes through a dedicated pipeline comprising convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) layers, and attention-equipped vision transformers (ViT). This configuration facilitates hierarchical learning of local, temporal, and global contextual features. The softmax outputs of each branch are fused using a stacking ensemble strategy, with an XGBoost-based meta-classifier performing the final decision integration. A complementary weighted ensemble is also implemented for comparative evaluation. Experimental results on a publicly available seven-class non-speech sound dataset demonstrate the proposed model’s outstanding performance, achieving 99.71% accuracy under 10-fold cross-validation, along with consistently high precision, recall, and F1-scores across all classes. Comparative benchmarks show substantial improvements over existing state-of-the-art models, including CNNs, long short-term memory (LSTM) variants, classical machine learning approaches, and metaheuristic-based ensembles. Supporting analyses such as confidence score distributions and dimensionality reduction visualizations (principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE)) further validate the model’s robustness and discriminative power. These findings highlight the effectiveness of integrating multi-spectral representations, deep hierarchical modeling, and ensemble strategies for high-fidelity nocturnal non-speech sound classification.

Odor detection underpins food safety, environmental monitoring, medical diagnostics, and many more fields. Current artificial sensors developed for odor detection struggle with complex mixtures, while non-invasive recordings lack reliable single-trial fidelity. To develop a general system for odor detection, in this study we present preliminary work where we test two hypotheses: (i) that spectral features of local field potentials (LFPs) are sufficient for robust single-trial odor detection and (ii) that signals from the olfactory bulb alone are adequate. To test these hypotheses, we propose an ensemble of complementary one-dimensional convolutional networks (ResCNN and AttentionCNN) that decodes the presence of odor from multichannel olfactory bulb LFPs. Tested on 2349 trials from seven awake mice, our final ensemble model supports both hypotheses, achieving a mean accuracy of 86.2%, an F1-score of 85.3%, and an AUC of 0.942, substantially outperforming previous benchmarks. The t-SNE visualization confirms that our framework captures biologically significant signatures. These findings establish the feasibility of robust single-trial detection of odor presence from extracellular LFPs and demonstrate the potential of deep learning models to provide deeper understanding of olfactory representations.

Complex networks for modeling texture and spectral features of hyperspectral images for environmental analysis.