Technologies

The electric network frequency (ENF) signal embedded in recordings made under grid-powered lighting is widely used to verify recording time and assess authenticity. Its reliability under deliberate anti-forensic manipulation, however, requires further study. This paper demonstrates a signal-processing pipeline that suppresses the original ENF component in a target video and introduces the ENF-bearing luminance component of a donor video recorded at another time. We also examine ENF injection into videos with no usable ENF trace. In eight matched experiments, the ENF re-extracted from the manipulated videos produced normalized cross-correlation values within the range observed for authentic donor videos. Frame-averaged PSNR and SSIM were used to quantify visual fidelity, and two reference-free tests examined artifacts introduced by the pipeline. A batch-boundary phase-discontinuity test was not statistically significant, whereas a notch-shoulder spectral-energy measure separated the original and manipulated videos in this small sample (AUC = 1.00). These results show that ENF substitution can mislead NCC-based timestamp matching but is not necessarily undetectable, supporting the use of complementary forensic checks when ENF evidence is evaluated.

This work systematically examines the effect of zirconium additions on the phase transition behavior and microstructural evolution of TiAl(Nb,Mo)B systems subjected to shear deformation under unconstrained self-propagating high-temperature synthesis (SHS). The study further evaluates the direct correlation between Zr doping levels, microstructural arrangements, and the resulting mechanical behavior of the densified compacts. The experiments focused on a precursor powder blend with the nominal composition of 51.85Ti–43Al–4Nb–1Mo–0.15B (at %) supplemented with 1–3 at % of zirconium. The results demonstrate that introducing Zr during SHS processing promotes the precipitation of a secondary Al₂Zr intermetallic compound, which selectively segregates at the boundaries of the lamellar γ and α₂ colonies. This alloying strategy yields a pronounced enhancement in room-temperature improved crack resistance under contact/indentation conditions and structural damage tolerance. These mechanical enhancements are associated with a synergistic effect, phase transformations, and the precipitation of an Al₂Zr phase, whose volume fraction is governed by the initial Zr concentration. We assumed that the resulting microstructure effectively impedes macrocrack advancement through multi-directional branching and interfacial energy dissipation, thereby effectively preventing brittle failure under indentation loads up to 100 N at room temperature.

The analysis of heterogeneous tabular datasets remains challenging for non-specialist users because data preparation, statistical diagnostics, algorithm selection, model evaluation, and result interpretation are frequently distributed across separate software environments. This manuscript presents a reproducible web-based technology that integrates data ingestion, automated data-quality assessment, statistical validation, transparent rule-based analytical guidance, supervised and unsupervised machine learning, and interactive visual analytics within a unified workflow. The platform supports commonly used tabular formats and employs a modular, containerized architecture to support portability and reproducible analytical execution. Rather than autonomously selecting a supposedly optimal model, the recommendation mechanism identifies the analytical task, screens incompatible methods, detects semantic ambiguity, generates methodological warnings, and prioritizes compatible candidate models using explicit and inspectable rules. Final supervised-model selection is based on empirical cross-validation within the training data. The technology is evaluated through functional tests on heterogeneous health and financial datasets, predictive and clustering experiments, controlled recommendation-engine and schema-robustness experiments, computational benchmarks, and a usability study involving users with limited programming experience. In a COVID-19 dataset containing 95,839 records, the platform supported two-code and three-code patient-classification experiments, descriptive clustering, and data-quality analysis while transparently identifying class imbalance, predictor missingness, repeated predictor profiles, and performance limitations. A grouped-profile sensitivity analysis further showed that the principal classification results did not deteriorate when identical selected predictor configurations were prevented from occurring in both the training and held-out partitions. Controlled schema-robustness experiments correctly identified all prespecified unambiguous analytical tasks, detected all prespecified semantic ambiguities, rejected incompatible supervised configurations, and produced no silent acceptance of ambiguous or invalid cases. The recommender-specific evaluation also showed that the highest-ranked rule-based candidate does not necessarily coincide with the cross-validation-best model, reinforcing its role as a decision-support mechanism rather than an autonomous model-selection procedure. An exploratory laboratory usability evaluation with twelve non-specialist participants produced an average task-success rate of 95.3% and a mean System Usability Scale score of $86.7\pm 5.2$. These values correspond to aggregate results recorded during the original laboratory experiment. Because the participant-level observer logs and individual SUS responses were not retained in the available study materials, the usability results are interpreted descriptively and are not presented as population-level estimates. The results indicate that the proposed architecture can reduce technical barriers while preserving analytical traceability, methodological transparency, and user control. The platform therefore provides an extensible foundation for human-supervised, reproducible analysis of heterogeneous tabular data rather than an autonomous statistical or machine-learning decision-making system.