Search Results (40)

Nanotechnology continues to advance rapidly, revealing previously unexplored directions in nanoscale communications. Biological and electromagnetic nanonetworks—established communication paradigms at the nanoscale—have shifted interest toward the middle and higher levels of the nanonetworking protocol stack. Motivated by the discovery of Cable Bacteria (CB) and their unique properties, we propose a theoretical model and framework for a new category of nanonetworks: bioelectrical nanonetworks (BioEN). This proposed framework combines the biocompatibility, sustainability and inherent nanodimensions of biological organisms with the networking performance of electromagnetic systems. Large-scale formations (e.g., 10,000 cells spanning nearly 2 cm), together with the electrical characteristics of CB, suggest the feasibility of guided electron-based transport that could complement diffusion-dominated nanonetworks, subject to resistive-capacitive (RC) constraints that remain to be quantified. Furthermore, we present a set of basic network architectures—such as star, ring, and tree—introducing a conceptual bio-multiplexer component, which utilizes CB to form a bioelectrical nanonetwork and illustrate core functionalities primarily at the network layer. Within this theoretical framework, BioEN is positioned as a potential enabler for diverse scientific, environmental, and technological applications, including health and ecosystem biosensing and bioremediation-oriented bioengineering. This work is conceptual and does not experimentally validate a deployed nanonetwork; instead, it establishes the design principles, abstractions, and architectural foundations intended to guide future implementation and experimental verification of bioelectrical nanonetworks. Full article

In visible-light optical observations for Space Surveillance and Tracking (SST) from ground-based dynamic platforms, attitude disturbances and field-of-view discontinuities frequently undermine interframe geometric consistency, leading to energy diffusion and unstable gain in multi-frame stacking for faint space objects. We propose orbit-prior- guided target-centered stacking (OPG-TCS), a tracking-oriented post-processing method designed to stabilize target energy accumulation and improve enhancement reliability under dynamic observation conditions. OPG-TCS performs frame-wise astrometric calibration using star fields (WCS) and leverages projected orbit priors to predict target pixel locations, enabling local cropping and target-centered alignment/stacking without relying on full-frame geometric consistency. We evaluate OPG-TCS on multiple real-world dynamic-platform sequences and compare it with direct stacking and representative robust baselines. Signal-to-noise ratio (SNR) is used as the primary metric, while auxiliary indicators of peak prominence, energy concentration, and shape consistency are employed to assess robustness across varying stacking depths. The results show that OPG-TCS provides stable enhancement over different frame counts; in representative 50-frame fusions, its relative SNR surpasses direct stacking by 33.7–97.8%. These findings suggest that OPG-TCS offers a practical and robust enhancement strategy for SST-oriented observation of faint space objects, supporting more reliable detection and subsequent tracking analysis. Full article

Precise object tracking of space objects is an image acquisition method that uses the mount of the telescope to orient the instrument in real time towards the target to be tracked, compensating for the target’s motion. Using this method, the object of interest will appear as a circular or point-like shape in the acquired image, while the background stars will appear as streaks. Using precise object tracking, the light from a faint object accumulates in the same region of the image, increasing the chance of observation, but longer exposures also increase the length of the background star streaks and makes the astrometric calibration difficult. This paper presents a method for the automatic processing of image sequences acquired in precise object tracking mode. Our proposed method includes a filtering mechanism that will ensure local maxima in the center of star streaks in order to allow for a publicly available astrometric calibration software to work even if the stars are not point-like, a weighted stacking mechanism to increase the signal-to-noise ratio for faint targets while excluding the stars, an automatic object detection and astrometric reduction mechanism and a constraint-based filtering of outliers for the final generation of the tracklet. The method was tested on multiple observation sessions for surveying the CLUSTER II highly eccentric orbit satellites, including the CLUSTER II FM5 satellite (Rumba) on its final passes before reentry, and the accuracy of the measurements was estimated based on ground truth from ESA’s reentry team. The method was also tested on lower orbit objects and found to be accurate for objects with ranges of more than 1300 km from the observer. Full article

Star polymers with dense shell structures exhibit unique advantages in molecule encapsulation. The incorporation of dendronized polymers as arms into star polymers enables the formation of spherical core–shell structures with high-density chain stacking, which is of great significance for enhancing their encapsulation capabilities. Here, we report on the synthesis of a new type of star dendronized polymer consisting of oligoethylene glycol (OEG)-based dendronized polymers as the arms and gold nanoparticles (AuNPs) as the core. Due to the thickness of individual dendronized polymer arms, the morphology of star dendronized polymers was directly visualized by an atomic force microscope (AFM). These star polymers inherit characteristic thermoresponsiveness from the OEG-based dendronized linear polymers, and their thermoresponsive behavior depends mainly on the grafting density of polymer chains on the AuNP cores and the molecular weights of the polymer arms. More importantly, these star dendronized polymers exhibit a tunable encapsulation capacity to guest molecules, which can be modulated through thermally induced aggregation. By virtue of these peculiarities, these thermoresponsive star dendronized polymers with tailorable release properties hold promise as smart nanoboxes for bio-applications, including drug delivery and biosensing. Full article

Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei, such as lymphomas, due to the loss of spatial depth information. Here, we tested if AI-based 3D nuclear segmentation can improve the accuracy, reproducibility, and diagnostic reliability of FISH reading in critical situations. Materials and Methods: Formalin-fixed follicular lymphoma sections were FISH-labeled for BCL2 gene rearrangements and digitally scanned in multilayer Z-stacks. The analytic performance in nuclear segmentation of the adaptive thresholding-based FISHQuant, and the freely accessible AI-based NucleAIzer, StarDist, and Cellpose algorithms, were compared to the eye control-based traditional FISH testing, primarily focusing on nuclear segmentation. Results: We revealed that the Cellpose algorithm showed limited sensitivity to low-intensity signals and the adaptive thresholding 2D segmentation, and FISHQuant struggled to resolve densely packed nuclei, occasionally underestimating their counts. In contrast, 3D segmentation across focal planes significantly improved the nuclear separation and signal localization. AI-driven 3D models, especially NucleAIzer and StarDist, showed improved precision, lower variance (VP/VS ≈ 0.96), and improved gene spot correlation (r > 0.82) across multiple focal planes. The similar average number of gene spots per cell nuclei in the AI-based analyses as the eye control counting, despite the elevated number of cell nuclei found with AI, validated the AI nuclear segmentation results. Conclusions: Inaccurate segmentation limits automated diagnostic FISH signal evaluation. Deep learning 3D approaches, particularly NucleAIzer and StarDist, may overcome thresholding and 2D constraints and improve the consistency of nuclear detection, resulting in better classification of pathogenetic gene aberrations with automated workflows in digital pathology. Full article

We present a study of the linear polarization properties of radio sources within the 10 deg². Wide Chandra Deep Field South (W-CDFS) in S-band (2–4 GHz). Our W-CDFS image has an angular resolution of 15 arcsec and a 1$\sigma$ RMS in Stokes I of ≈50 $\mathsf{\mu }$Jy/beam. We detect 1920 distinct source components in Stokes I and 175 in linear polarization. We examine the polarized source counts, Faraday Rotation measures, and fractional polarization of the sources in the survey. We show that sources with a total intensity above ≈10 mJy have a mean fractional polarization value of ≈3% from modeling the polarized counts. We also calculate an estimate for the limit on the fractional polarization level of sources with a total intensity below 1 mJy (mostly star-forming galaxies) of ≲3% using stacking. The mean Faraday Rotation we measure is consistent with that due to the Milky Way. We also show that fractional polarization is correlated with in-band spectral index, consistent with a lower mean fractional polarization for the flat-spectrum population. In addition to characterizing the S-band polarization properties of sources in the W-CDFS, this study will be used to validate the shallower, but higher angular resolution S-band polarimetric information that the VLA Sky Survey will provide for the whole sky above Declination −40 degrees over the next few years. Full article

Accurately predicting the ultimate strain of fiber-reinforced polymer (FRP)-confined concrete columns is essential for the widespread application of FRP in strengthening reinforced concrete (RC) columns. This study comprehensively investigates the performance of ensemble machine learning (ML) models in estimating the ultimate strain of FRP-confined concrete (FRP-CC) columns. A dataset of 547 test results of the ultimate strain of FRP-CC columns was collected from the literature for training and testing ML models. The four best single ML models were used to develop ensemble models employing voting, stacking and bagging techniques. The performance of the ensemble models was compared with 10 single ML and 11 empirical strain models. The study results revealed that the single ML models yielded good agreement between the estimated ultimate strain and the test results, with the best single ML models being the K-Star, k-Nearest Neighbor (k-NN) and Decision Table (DT) models. The three best ensemble models, a stacking-based ensemble model comprising K-Star, k-NN and DT models; a stacking-based ensemble model comprising K-Star and k-NN models and a voting-based ensemble model comprising K-Star and k-NN models, achieved higher estimation accuracy than the best single ML model in estimating the strain capacity of FRP-CC columns. Full article

The growing density of Earth-orbiting objects demands improved Space Situational Awareness (SSA) to mitigate collision risks and sustain space operations. This study demonstrates a dual-purpose star tracker (ST) for SSA using data from the Resident Space Object Near-space Astrometric Reconnaissance (RSONAR) stratospheric balloon campaign under the 2022 Canadian Space Agency–Centre National d’Études Spatiales (CSA–CNES) STRATOS program. The low-cost optical payload—a wide-field monochromatic imager flown at 36 km altitude—acquired imagery subsequently used for post-processed attitude determination and Resident Space Object (RSO) detection. During stabilized pointing, over 27,000 images yielded sub-pixel astrometry and stable image quality (mean full-width-Half-maximum ≈ 388 arcsec). Photometric calibration to the Tycho-2 catalog achieved 0.37 mag root mean square (RMS) scatter, confirming radiometric uniformity. Apparent angular velocities of $7\times {10}^2$ to $8\times {10}^3$ arcsec ${\mathrm{s}}^{-1}$ corresponded to sunlit low-Earth-orbit (LEO) objects observed at 25°–35° phase angles. Covariance-weighted Mahalanobis correlation with two-line elements (TLEs) achieved sub-arcminute positional agreement. The Proximity Filtering and Tracking (PFT) algorithm identified 22,036 total RSO and 387 total streaks via image stacking. Results confirm that commercial off-the-shelf STs can serve as dual-use SSA payloads, and that stratospheric ballooning offers a viable alternative for optical SSA research. Full article

Oxygen-enhanced magnetic resonance imaging (OE-MRI) enables non-invasive assessment of lung function by measuring longitudinal relaxation time ($T_1$) changes induced by alternating inhalation of room air and pure oxygen. In this study, the pulmonary $T_1$ and its reduction after breathing pure oxygen were quantified by using the free-breathing three-dimensional (3D) Look-Locker technique based on a stack-of-stars acquisition scheme. This method applied a continuous acquisition model to collect signals during both room-air and pure oxygen conditions without the need for breath-holding or respiratory gating. Comparative evaluations were conducted between the proposed 3D Look-Locker method and the conventional two-dimensional (2D) Look-Locker approach, using both phantom and in vivo experiments. The results demonstrate that the 3D technique yields more pronounced and reproducible $T_1$ reductions between air and oxygen conditions compared to the 2D method. Additionally, the $T_1$ of the average respiratory phase obtained by the 3D approach was compared with the $T_1$ at end-expiration and end-inspiration measured by the 2D approach. A consistent decline in $T_1$ across respiratory phases was demonstrated, from end-expiration to end-inspiration, as well as the average respiratory phase under free-breathing. These findings suggest that the proposed OE-MRI $T_1$ measurement based on the 3D Look-Locker method provides a robust and clinically feasible approach for quantitative lung imaging. Full article

We propose a nested ensemble learning framework that utilizes Topological Data Analysis (TDA) to extract and integrate topological features from graph data, with the goal of improving performance on classification and regression tasks. Our approach computes persistence diagrams (PDs) using lower-star filtrations induced by three filter functions: closeness, betweenness, and degree 2 centrality. To overcome the limitation of relying on a single filter, these PDs are integrated through a data-driven, three-level architecture. At Level-0, diverse base models are independently trained on the topological features extracted for each filter function. At Level-1, a meta-learner combines the predictions of these base models for each filter to form filter-specific ensembles. Finally, at Level-2, a meta-learner integrates the outputs of these filter-specific ensembles to produce the final prediction. We evaluate our method on both simulated and real-world graph datasets. Experimental results demonstrate that our framework consistently outperforms base models and standard stacking methods, achieving higher classification accuracy and lower regression error. It also surpasses existing state-of-the-art approaches, ranking among the top three models across all benchmarks. Full article

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts. Full article

Objectives: To compare the feasibility and image quality of a post-contrast free-breathing radial stack-of-stars 3D T1w turbo-field echo Dixon sequence (3D T1w VANE mDIXON) with a conventional cartesian breath-hold 3D T1w fast-field echo mDIXON sequence in pediatric oncology patients undergoing chest MRI. Methods: A total of 48 children (34 females; mean age 5.3 ± 3.7 years) underwent contrast-enhanced chest MRI, with 24 examined using the 3D T1w VANE mDIXON sequence and 24 with a conventional breath-hold 3D T1w mDIXON sequence. Image quality was independently assessed by three radiologists using a 5-point scale. Signal-to-noise ratio (SNR) was measured at two anatomical sites, a homogeneous paraspinal muscle region (SNR_muscle) and the liver apex (SNR_liver), while avoiding vessels and signal inhomogeneities. The presence of respiratory artifacts, total imaging time, and the need for general anesthesia or sedation were recorded. Interobserver agreement was determined using Fleiss’s kappa (ϰ), and mean SNR values were compared between groups using an independent samples t-test. Results: The 3D T1w VANE mDIXON sequence yielded significantly higher SNR_muscle and SNR_liver (530 ± 120; 570 ± 110 vs. 370 ± 110; 400 ± 90; p < 0.001), improved diagnostic image quality by approximately 25%, and reduced respiratory artifacts by about 23%. Interobserver agreement was almost perfect. Importantly, the need for general anesthesia was significantly reduced using the 3D T1w VANE mDIXON (p < 0.001). Conclusions: Free-breathing 3D T1w VANE mDIXON chest MRI is a feasible and effective imaging approach for pediatric oncology patients, offering superior image quality and reducing the need for general anesthesia compared to conventional methods. Full article

The geostationary orbit (GEO), about 35,786 km above the Earth’s equator, hosts high-value satellites like communication, meteorological, and navigation ones. Real-time detection of geostationary orbit targets is crucial for orbital resource safety and satellite operation. Large field-of-view (FOV) telescopes can observe many such targets but face technical bottlenecks due to their optical systems, such as weak light-gathering capability, stellar interference, and complex stray light. This paper analyzes the apparent motion differences between stars and geostationary orbit targets based on the telescope’s staring mode. Stars move overall in images while GEO targets are relatively stationary. A minimum value stacking (Min-Stacking) method is proposed to suppress stars, improving GEO targets’ signal-to-noise ratio. With the global threshold segmentation algorithm, fast and accurate target extraction is achieved. Experiments show the method has high detection rates, overcomes interference, and features simplicity and real-time performance, with important application value. Full article

The real nature of little red dots (LRDs), a class of very compact galaxies in the early Universe recently discovered by the James Webb Space Telescope, is still poorly understood. The most popular theories competing to interpret the phenomena include active galactic nuclei and enhanced star formation in dusty galaxies. To date, however, neither model gives a completely satisfactory explanation to the population as a whole; thus, alternative theories have arisen, including tidal disruption events (TDEs). By considering observational constraints on the radio emission of LRDs, we discuss whether TDEs are adequate alternatives solving these high-redshift enigmas. We utilise radio flux density upper limits from LRD stacking analyses, TDE peak radio luminosities, and volumetric density estimates. We find that the characteristic values of flux densities and luminosities allow radio-quiet TDEs as the underlying process of LRDs in any case, while the less common radio-loud TDEs are compatible with the model under special constraints only. Considering other factors, such as volumetric density estimates, delayed and long-term radio flares of TDEs, and cosmological time dilation, TDEs appear to be a plausible explanation for LRDs from the radio point of view. Full article

Polarization information is essential for material identification, stress mapping, biological imaging, and robust vision under strong illumination, yet conventional approaches rely on external polarization optics and active biasing, which are bulky, alignment-sensitive, and power-hungry. A more desirable route is to encode polarization at the pixel level and read it out at zero bias, enabling compact, low-noise, and polarization imaging. Low-symmetry layered semiconductors provide persistent in-plane anisotropy as a materials basis for polarization selectivity. Here, we construct an eight-terminal radial ‘star-shaped’ Au/ReS₂ metal-semiconductor junction array pixel that operates in a genuine photovoltaic mode under zero external bias based on the photothermoelectric effect. Based on this, electrical summation of phase-matched multi-junction channels increases the signal amplitude approximately linearly without sacrificing the two-lobed modulation depth, achieving ‘gain by stacking’ without external amplification. The device exhibits millisecond-scale transient response and robust cycling stability and, as a minimal pixel unit, realizes polarization-resolved imaging and pattern recognition. Treating linear combinations of channels as operators in the polarization domain, these results provide a general pixel-level foundation for compact, zero-bias, and scalable polarization cameras and on-pixel computational sensing. Full article