Technical University of Moldova

The AI-foodome, the machine-readable integration of food chemistry, nutrition and safety through artificial intelligence, is the declared objective of a fast-growing literature, yet the data infrastructures invoked as its foundation have not, to our knowledge, been tested at the architectural level. A systematic search of the reproducible corpus assembled for this review (3205 records, 2015–April 2026) identified no prior per-indicator FAIR audit of these infrastructures; the audit reported here evaluates ten infrastructures with three standardised instruments allocated by technical applicability (F-UJI, O'FAIRe and the RDA FAIR Data Maturity Model). They are FAIR-by-narrative, not FAIR-by-design: across the seven infrastructures assessed on a common indicator scheme, Accessibility reaches 86.9% compliance while Interoperability falls to 33.3%, a gap that tracks the technical substrate rather than implementation choices. Declared compliance does not correspond to technical exemplification: no typed inter-axis link was traversable among the audited infrastructures, and a single food entity, wheat, could not be reconstructed across axes. The fragmentation is read here as structural: at the observed rate of cross-axis convergence, reaching 50% multi-axis integration would require approximately 73 years (95% CI: 53–198). The audit locates the integration deficit in the data architecture rather than in the algorithms: the constraints documented here are located below the level at which model refinement operates. From this diagnosis the review derives a four-pillar framework (semantic unification, FAIR-by-design infrastructures, cross-axis provenance traceability and inclusive contribution) specifying the conditions under which agencies and infrastructure operators can query composition, intake and hazard data as a single evidence base.

This study investigated the effect of root application of copper, gold and silver nanoparticles at different concentrations (5, 50, 100 mg/L) on Mentha spicata L. during 28-days field experiment. The plants were watered once with the nanoparticle solutions, which were coated with polyvinylpyrrolidone and had the following sizes: 1–2 nm for silver, 1–4 nm for gold, and 15–70 nm for copper. To determine the patterns of metal nanoparticle accumulation, their content in soil and plant segments was quantified using inductively coupled plasma optical emission spectrometry and atomic absorption spectrometry. Silver nanoparticles exhibited the highest bioavailability, while copper and gold nanoparticles showed low mobility in plants. Irrigation with a 50 mg/L solution of copper nanoparticles led to a 25

An experimental cell design for studying the properties of anisotropic materials under the action of a magnetic field during deformation is described. The developed method has been tested by studying the properties of low-dimensional samples of anisotropic materials subjected to deformation. Single glass-coated bismuth wires as more suitable objects characterized by a large elastic strain range were selected for these studies. Dependencies of the resistance of bismuth wires on deformation in a transverse magnetic field have been measured by modifying the design of the bronze console of the insert, in which the wire is fixed. The proposed method has been used to reconstruct the Fermi surface topology in single glass-coated bismuth wires under uniaxial deformation by studying quantum oscillations of the perpendicular magnetoresistance for different orientations of the magnetic field relative to the crystallographic axes.

During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods.

In this review, we summarize the foundations underlying a variety of phenomena in superconductor– ferromagnet hybrid structures, with a focus on recent advances in several key areas. These include: (i) the fundamental understanding of proximity effects in superconductor–ferromagnet based systems; (ii) spin-valve effects in superconductor– ferromagnet and superconductor–ferromagnet–superconductor Josephson junctions; and (iii) the design and realization of superconducting memory elements, particularly in hybrid Josephson junctions. We also discuss the experimental progress in fabricating and characterizing spin-valve structures.