Search Results (28)

Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning methods are difficult to apply due to the small amount of data available, the knowledge-based approach of Case-Based Reasoning (CBR) offers a way to reuse previously gained experience. We introduce the first end-to-end Case-Based Reasoning (CBR) framework that both detects and remedies DQIs in near real time, even when only a handful of annotated fault instances are available. Our solution encodes expert experience in the four CBR knowledge containers: (i) a vocabulary that represents sensor streams and their context in the DataStream format; (ii) a case base populated with fault-annotated event logs; (iii) tailored similarity measures—including a weighted Dynamic Time Warping variant and structure-aware list mapping—that isolate the signatures of missing-value, missing-sensor, and time-shift errors; and (iv) lightweight adaptation rules that recommend concrete repair actions or, where appropriate, invoke automated imputation and alignment routines. A case study is used to examine and present the suitability of the approach for a specific application domain. Although the case study demonstrates only limited capabilities in identifying Data Quality Issues (DQIs), we aim to support transparent evaluation and future research by publishing (1) a prototype of the Case-Based Reasoning (CBR) system and (2) a publicly accessible, meticulously annotated sensor-log benchmark. Together, these resources provide a reproducible baseline and a modular foundation for advancing similarity metrics, expanding the DQI taxonomy, and enabling knowledge-intensive reasoning in IoT data quality management. Full article

Extracellular Vesicle-associated microRNAs (EV-miRNAs) are emerging as pivotal regulators of corneal health and disease, holding exceptional promise for transforming both diagnostics and therapeutics. These vesicles carry distinct miRNA signatures in biofluids such as tears, offering a powerful, non-invasive approach for early detection, risk stratification, and dynamic monitoring of corneal disorders. In addition, EV-miRNAs act as key mediators of critical biological processes, including inflammation, fibrosis, and tissue repair. Consequently, they represent attractive therapeutic targets; for example, engineered EVs loaded with miRNA mimics or inhibitors can precisely modulate these pathways to promote regeneration and suppress disease progression. Yet, despite this considerable promise, the translation of EV-miRNA research into clinical practice remains constrained by several challenges. Topmost among these are the lack of standardized EV isolation methods, variability in miRNA quantification, and the pressing need for regulatory frameworks tailored to the complexity of these biological therapeutics. Addressing these barriers is essential to ensure reproducibility, scalability, and safety in clinical applications. Accordingly, this review synthesizes current knowledge on EV-miRNA profiles in corneal diseases, critically evaluates their diagnostic and therapeutic potential, and highlights strategies to overcome existing technical and regulatory limitations. Ultimately, the successful integration of EV-miRNA-based approaches into personalized medicine frameworks could revolutionize the management of corneal diseases and substantially improve patient outcomes. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

Be stars are characterized by the presence of a circumstellar Keplerian disk formed from material ejected from the rapidly rotating stellar surface. This article presents recent observational and theoretical progress on two central aspects of this phenomenon: the mechanisms driving mass loss, and the fate of the ejected material. Using simultaneous TESS photometry and ground-based spectroscopy, we examine the short-term variability associated with discrete mass ejection events, or “flickers”, and review strong evidence linking them to pulsational activity near the stellar surface. Complementary 3D hydrodynamic simulations reproduce key observational signatures and establish that disk formation requires compact and asymmetric ejection sites with sufficient angular momentum to overcome re-accretion. In systems with binary companions, new high-resolution simulations resolve the outer disk for the first time and identify five dynamically distinct regions, including a circumsecondary disk and a circumbinary spiral outflow. Together, these results provide a coherent framework that traces the full life cycle of disk material from pulsation-driven ejection near the stellar surface to its final destination, whether re-accreted by the companion or lost from the system entirely. Full article

Financial time series in volatile markets often exhibit non-stationary behavior and signatures of stochastic chaos, challenging traditional forecasting methods based on stationarity assumptions. In this paper, we introduce a novel multi-expert forecasting system (MES) that leverages ensemble machine learning techniques—including bagging, boosting, and stacking—to enhance prediction accuracy and support robust risk management decisions. The proposed framework integrates diverse “weak learner” models, ranging from linear extrapolation and multidimensional regression to sentiment-based text analytics, into a unified decision-making architecture. Each expert is designed to capture distinct aspects of the underlying market dynamics, while the supervisory module aggregates their outputs using adaptive weighting schemes that account for evolving error characteristics. Empirical evaluations using high-frequency currency data, notably for the EUR/USD pair, demonstrate that the ensemble approach significantly improves forecast reliability, as evidenced by higher winning probabilities and better net trading results compared to individual forecasting models. These findings contribute both to the theoretical understanding of ensemble forecasting under chaotic market conditions and to its practical application in financial risk management, offering a reproducible methodology for managing uncertainty in highly dynamic environments. Full article

A comprehensive theoretical investigation of shape coexistence and transition phenomena in the neutron-deficient nucleus ${}^{66}\mathrm{Se}$, using complementary microscopic and phenomenological approaches, is presented. The analysis employs the Covariant Density Functional Theory with the Density-Dependent Meson Exchange Model interaction to map the potential energy surface. This microscopic foundation is complemented by calculations using the Bohr–Mottelson Hamiltonian with a sextic oscillator potential, specifically adapted to explore shape coexistence between spherical and $\gamma$-unstable configurations. The latter model successfully reproduces the experimental energy spectrum, including the critical low-lying $0_2^{+}$ state at 1226 keV—a key signature of shape coexistence. An analysis of probability density distributions indicates a distinctive manifestation of shape coexistence wherein different shapes exist without significant mixing in the states. These findings provide crucial insights into the structural dynamics of ${}^{66}\mathrm{Se}$ and establish it as an important case study for understanding shape evolution in neutron-deficient nuclei beyond the $N=Z$ line. Full article

Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink^® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The model includes descriptions of the six degrees of freedoms of each subcomponent, and was validated by comparison with experimental measurements acquired on a test rig capable of applying heavy radial loads. The results show a good fit between experimental and simulated signals in terms of identifying characteristic fault frequencies, which highlights the model’s ability to reproduce vibrations induced by localized defects on the inner and outer races. Amplitude differences can be attributed to simplifications such as neglected housing compliancies and lubrication effects, and do not alter the model’s effectiveness in detecting fault signatures. In conclusion, the developed model represents a promising tool for generating useful datasets for training diagnostic and prognostic algorithms, thereby contributing to the improvement of predictive maintenance strategies in industrial settings. Despite some amplitude discrepancies, the model proves useful for generating fault data and supporting condition monitoring strategies for industrial machinery. Full article

Memristors have emerged as prospective two-terminal elements, having applications in memory, neuromorphic systems, and analog circuits. Biological materials such as egg albumin exhibit memristive behavior, displaying a distinctive pinched hysteresis signature in their current-voltage characteristics. However, such memristive behavior must be mathematically modeled to gain insights into the material’s operation and utilize it in various circuit applications. This article proposes a novel SPICE-level framework for fabricated egg albumin memristors using Joglekar’s memristor model. Experimental current-voltage characteristics are used to calibrate the SPICE model, ensuring accurate reproducibility of the experimental results. Additionally, the impact of variations in model-specific parameters on dynamic resistance and device performance is explored. Full article

Glioblastoma stem cells (GSCs) are key drivers of relapse, metastasis, and therapy resistance in glioblastoma due to their adaptability and diversity, which make them challenging to target effectively. This study explores the O-glycosylation in differentiating two key GSC subtypes, CD133 and CD44. We utilized the TCGA dataset of GBM and presented the reproducible bioinformatics analysis for our results. Our profiling showed enriched O-glycosylation signatures in CD44-expressing GBM cells over CD133, with Cosmc, the chaperone for core mucin-type O-glycosylation, significantly upregulated in the CD44-positive group. Moreover, Cosmc was associated with shorter progression-free intervals, suggesting its potential as an indicator of aggressive disease. High Cosmc expression also enriched immune-related pathways, including inflammatory response and antigen presentation, and was associated with presence of myeloid cells, T cells, and NK cells. Additionally, elevated Cosmc correlated with extracellular matrix (ECM) pathways and stromal cell populations, such as perivascular fibroblasts. These findings position O-glycosylation, specially, Cosmc as a promising biomarker for distinguishing GSC subclones, with relevance to immune modulation, and ECM dynamics, identifying it as a potential target for novel GBM therapies. Full article

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine. Full article

Water samples from two naturally hypersaline lakes, renowned for their balneotherapeutic properties, were investigated through a pilot SERS monitoring program. Nanotechnology-based techniques were employed to periodically measure the ultra-sensitive SERS molecular characteristics of the raw water-bearing microbial community and the inorganic content. Employing the Pearson correlation coefficient revealed a robust linear relationship between electrical conductivity and pH and Raman and SERS spectral data of water samples, highlighting the interplay complexity of Raman/SERS signals and physicochemical parameters within each lake. The SERS data obtained from raw waters with AgNPs exhibited a dominant, reproducible SERS feature resembling adsorbed β-carotene at submicromole concentration, which could be related to the cyanobacteria-AgNPs interface and supported by TEM analyses. Notably, spurious SERS sampling cases showed molecular traces attributed to additional metabolites, suggesting multiplexed SERS signatures. The conducted PCA demonstrated observable differences in the β-carotene SERS band intensities between the two lakes, signifying potential variations in picoplankton abundance and composition or environmental influences. Moreover, the study examined variations in the SERS intensity ratio I₂₄₅/I₁₅₁₂, related to the balance between inorganic (Cl⁻-induced AgNPs aggregation) and organic (cyanobacteria population) balance, in correlation with the electrical conductivity. These findings signify the potential of SERS data for monitoring variations in microorganism concentration, clearly dependent on ion concentration and nutrient dynamics in raw, hypersaline water bodies. Full article

Stratospheric dynamics are strongly affected by the absorption/emission of radiation in the Earth’s atmosphere and Rossby waves that propagate upward from the troposphere, perturbing the zonal flow. Reduced order models of stratospheric wave–zonal interactions, which parameterize these effects, have been used to study interannual variability in stratospheric zonal winds and sudden stratospheric warming (SSW) events. These models are most sensitive to two main parameters: $\Lambda$, forcing the mean radiative zonal wind gradient, and h, a perturbation parameter representing the effect of Rossby waves. We take one such reduced order model with 20 years of ECMWF atmospheric reanalysis data and estimate $\Lambda$ and h using both a particle filter and an ensemble smoother to investigate if the highly-simplified model can accurately reproduce the averaged reanalysis data and which parameter properties may be required to do so. We find that by allowing additional complexity via an unparameterized $\Lambda \left(t\right)$, the model output can closely match the reanalysis data while maintaining behavior consistent with the dynamical properties of the reduced-order model. Furthermore, our analysis shows physical signatures in the parameter estimates around known SSW events. This work provides a data-driven examination of these important parameters representing fundamental stratospheric processes through the lens and tractability of a reduced order model, shown to be physically representative of the relevant atmospheric dynamics. Full article

We discuss the emulation of non-Hermitian dynamics during a given time window using a low-dimensional quantum system coupled to a finite set of equidistant discrete states acting as an effective continuum. We first emulate the decay of an unstable state and map the quasi-continuum parameters, enabling the precise approximation of non-Hermitian dynamics. The limitations of this model, including in particular short- and long-time deviations, are extensively discussed. We then consider a driven two-level system and establish criteria for non-Hermitian dynamics emulation with a finite quasi-continuum. We quantitatively analyze the signatures of the finiteness of the effective continuum, addressing the possible emergence of non-Markovian behavior during the time interval considered. Finally, we investigate the emulation of dissipative dynamics using a finite quasi-continuum with a tailored density of states. We show through the example of a two-level system that such a continuum can reproduce non-Hermitian dynamics more efficiently than the usual equidistant quasi-continuum model. Full article

Reactive oxygen species (ROS) are highly reactive molecules generated in living organisms and an excessive production of ROS culminates in oxidative stress and cellular damage. Notably, oxidative stress plays a critical role in the pathogenesis of a number of oral mucosal diseases, including oral mucositis, which remains one of cancer treatments’ most common side effects. We have shown previously that oral keratinocytes are remarkably sensitive to oxidative stress, and this may hinder the development and reproducibility of epithelial cell-based models of oral disease. Here, we examined the oxidative stress signatures that parallel oral toxicity by reproducing the initial events taking place during cancer treatment-induced oral mucositis. We used three oral epithelial cell lines (an immortalized normal human oral keratinocyte cell line, OKF6, and malignant oral keratinocytes, H357 and H400), as well as a mouse model of mucositis. The cells were subjected to increasing oxidative stress by incubation with hydrogen peroxide (H₂O₂) at concentrations of 100 μM up to 1200 μM, for up to 24 h, and ROS production and real-time kinetics of oxidative stress were investigated using fluorescent dye-based probes. Cell viability was assessed using a trypan blue exclusion assay, a fluorescence-based live–dead assay, and a fluorometric cytotoxicity assay (FCA), while morphological changes were analyzed by means of a phase-contrast inverted microscope. Static and dynamic real-time detection of the redox changes in keratinocytes showed a time-dependent increase of ROS production during oxidative stress-induced epithelial injury. The survival rates of oral epithelial cells were significantly affected after exposure to oxidative stress in a dose- and cell line-dependent manner. Values of TC50 of 800 μM, 800 μM, and 400 μM were reported for H400 cells (54.21 ± 9.04, p < 0.01), H357 cells (53.48 ± 4.01, p < 0.01), and OKF6 cells (48.64 ± 3.09, p < 0.01), respectively. Oxidative stress markers (MPO and MDA) were also significantly increased in oral tissues in our dual mouse model of chemotherapy-induced mucositis. In summary, we characterized and validated an oxidative stress model in human oral keratinocytes and identified optimal experimental conditions for the study of oxidative stress-induced oral epithelial toxicity. Full article