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Recent Submissions

Articulated and dislocated infaunal echinoids as unique markers of hypoxic environments from the Miocene of Central Paratethys
(Scandinavian University Press, 2024) Tomašových, Adam; Galović, Ines; Hudáčková, Natália; Hyžný, Matúš; Ruman, Andrej; Rybár, Samuel; Šimo, Vladimír; Schlögl, Ján
Exceptional preservation of macrobenthic invertebrates with articulated remains is typically explained by episodic rapid burial events or by onset of anoxia, both aborting mixing and disintegration processes. However, these scenarios do not explain the preservation of articulated remains of infaunal organisms in the Lower Miocene diatom-rich mudstones (Schlier-type deposits) in the Central Paratethys epicontinental sea. We show that bathyal macrobenthic assemblages and dominated by the shallow-burrowing echinoid Lovenia are best preserved in background mudstones with burrow-disrupted diatomaceous lamination, conforming to intermediate ichnofabric typical of hypoxic environments. Lovenia occurs in three types of assemblages that differ in preservation, size structure, and species diversity: (1) dispersed or clustered, frequently complete echinoid tests with spines occur in homogeneous or partially-laminated silty claystones; (2) sandy pavements with densely-packed, almost monospecific echinoid concentrations exhibit intermediate frequency of intact tests with spines; and (3) well-sorted echinoid fragments co-occur with plant remains in species-rich sandy lags. Alternation of laminae formed by Thalassionema or Coscinodiscus-dominated diatom assemblages with terrigenous laminae indicates that: (1) postmortem burial of echinoids below the taphonomically-active zone was induced by rapid export of ungrazed diatoms to the seafloor and by seasonal fallout of terrigenous muds from hypopycnal plumes or low-density hyperpycnal flows (rather than by sudden burial by thicker event beds); and that, (2) sediment mixing and irrigation rates were slow and patchy because the diatomaceous mats were not eliminated by echinoid and crustacean burrows and the laminae-forming diatom frustules remained intact. Although winnowing and test exhumation to sediment-water interface contributed to the formation of pavements with echinoid concentrations, their dense packing and low evenness can rather reflect population outbreaks of echinoids exploiting seasonal diatom fluxes to the sediment surface. The echinoid fragments with spines, dislocated remains with cross-plate fractures, and molluscs with sharp-edged margins in silty claystones suggest that some mortality events were induced by predation rather than by anoxia. Seasonal hypoxia was a key factor that limited test disintegration and displacement and thus preserved not only unique ichnofabric but also intact or dislocated, weakly time-averaged remains of benthic fauna adapted to hypoxic conditions.
Entropy-weighted medoid shift: An automated clustering algorithm for high-dimensional data
(Elsevier, 2025) Kumar, Abhishek; Ajani, Oladayo S.; Das, Swagatam; Mallipeddi, Rammohan
Unveiling the intrinsic structure within high-dimensional data presents a significant challenge, particularly when clusters manifest themselves in lower-dimensional subspaces rather than in the full feature space. This complexity is prevalent in real-world datasets, such as text documents and images, which often contain numerous noisy or sparse features. Traditional clustering methods often overlook these latent subspace structures. This paper introduces a novel subspace-based clustering algorithm designed explicitly to address this challenge. Building upon the robust medoid shift framework, we integrate a dimensionality reduction scheme that dynamically projects data onto evolving subspaces determined through entropy-constrained optimization. This approach effectively filters irrelevant information and identifies underlying clusters, optimizing subspace representation while avoiding trivial solutions. Unlike existing methods, our algorithm ensures convergence without necessitating stopping criteria, thereby enabling efficient processing of large datasets. We validate the efficacy of our approach through extensive experiments on synthetic and real-world datasets, demonstrating substantial performance enhancements over state-of-the-art techniques. By explicitly uncovering the underlying subspace structures, our method opens new avenues for effective high-dimensional data clustering and offers valuable insights into complex data environments.
Densely carboxylated graphene for synthesis of high-performing NASICON cathodes for Na-ion batteries
(American Chemical Society, 2026) Obraztsov, Ievgen; Cymann-Sachajdak, Anita; Bruniecka, Kamila; Madajski, Piotr; Šedajová, Veronika; Trykowski, Grzegorz; Bakandritsos, Aristides; Wilamowska-Zawłocka, Monika
Sodium-ion batteries are emerging as a promising alternative to lithium-ion technology due to the abundance and low cost of sodium. Among the cathode candidates, Na3V2(PO4)3 (NVP) with a NASICON framework and its analogues offer a high operating voltage and excellent structural stability. However, their practical use is limited by poor electronic conductivity, a low active material fraction, and trade-offs in terms of morphology and tap density. Here, we report a simple synthesis strategy that employs densely carboxylated graphene, graphene acid (GA), as a multifunctional additive. GA acts simultaneously as a chelating agent, pH regulator, and in situ-formed carbon shell prior to calcination. GA allows the efficient reduction of V5+ to electrochemically active V3+, phase-pure NVP formation, and the growth of a thin, conformal carbon shell strongly anchored to NVP particles. The resulting electrodes contain 85 wt % active material while maintaining outstanding charge-transfer kinetics. The optimized NVP@GA cathode delivers an excellent rate performance up to 15 A gEM -1 (151 C), retaining 65.4% of the theoretical capacity of NVP, and stable cycling. This approach provides a versatile route for tailoring NASICON cathodes and can be extended to other phosphate-based systems for high-power sodium-ion batteries.
Development and enhancement of metamaterial-inspired Ag-GaAs THz MIMO antenna with optimized diversity metrics using data-driven machine learning algorithms for future 6G networks
(Springer Nature, 2025) Armghan, Ammar; Mandaliya, Vishalkumar; Alsharari, Meshari; Aliqab, Khaled; Ben Chaabane, Slim; Flah, Aymen
The MIMO antenna design is specifically engineered to support optimized performance in emerging 6G networks. Utilizing advanced techniques such as metamaterials and machine learning algorithms, the antenna system achieves high data rates, improved diversity, and robust signal reliability, making it ideal for next-generation ultra-fast and intelligent wireless communication technologies. Our advanced metamaterial configuration demonstrates high gain and bandwidth. A low ECC value 0.0004 shows minimal correlation, ensuring better signal diversity and improved system performance. Similarly, a high diversity gain confirms the antenna's efficiency in maintaining robust signal reception under varying conditions. The CCL values of 0.0916 bits/Hz bits/Hz provide insight into the information-carrying capacity of the MIMO configuration. The MIMO antenna design achieves a maximum gain of 8.9 dBi and a wide bandwidth of 30 THz. This performance is attained through a combination of parametric optimization and machine learning techniques, enhancing both efficiency and operational range. The machine learning algorithms used for optimization yield a high R-2 value of 0.99, indicating excellent prediction accuracy. The proposed antenna, featuring metamaterial characteristics, demonstrates strong potential for next-generation 6G networks, offering enhanced performance, efficiency, and compact design integration.
A Multimodal Perceived Stress Classification Framework Using Wearable Physiological Sensors
(IEEE, 2026) Majid, Muhammad; Arsalan, Aamir; Frnda, Jaroslav; Anwar, Syed Muhammad
Mental stress is a common condition that poses serious health risks, but proper management can greatly improve quality of life. We propose a robust multimodal framework for perceived stress classification using data from forty subjects collected via three physiological modalities: electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). Unlike most existing studies that focus on single modalities and binary classification, our framework addresses both two- and three-class perceived stress problems through multimodal fusion. Data was acquired over three minutes in an open-eye condition, and stress levels were assessed using the Perceived Stress Scale to assign labels. Time-domain features were extracted from GSR and PPG signals, while frequency-domain features were extracted from EEG. A frequency band selection algorithm identified the theta band as optimal for stress classification, and a wrapper-based feature selection method was applied to derive an effective multimodal feature set. Stress classification was performed with three classifiers utilizing features from all modalities. Among these classifiers, a significant accuracy (95% for two classes and 77.5% for three classes) was achieved using multilayer perceptron. The fusion of features from multiple modalities improves perceived stress classification, and our method, based on wearable sensors, is feasible for out-of-lab applications.