National University of Engineering

The significant rise in childhood anemia prevalence in Peru coincided with the onset of the COVID-19 pandemic. This impact was not homogeneous across the country, with some areas experiencing significant disruption while others demonstrating only a mild effect. In this research, we present a classification approach for the pandemic’s impact in small geographic areas, based on Small Area Estimation approach, employing data from 2016, 2019 and 2022. This approach enables the identification of unaffected areas and the determination of distinct impact patterns for the remaining areas. The proposed disaggregated analysis provides further insight into the significant increase of anemia among children under five during the COVID-19 pandemic in Peru. Notably, it facilitates evaluation and comprehension of the risk factors that contribute to the susceptibility of certain areas and regions to natural hazards, such as the COVID-19 pandemic. Given the notable discrepancies in the pandemic’s impact on childhood anemia at the area level, as revealed by our analysis, the findings presented in this article provide a basis for policymakers to elaborate strategies for targeted interventions and resource allocation, aimed at mitigating the potential consequences of future hazards within Peru.

We prove that a Malcev algebra [Formula: see text] that belongs to a certain variety of Malcev algebras [Formula: see text] such that [Formula: see text] contains the [Formula: see text]-dimensional exceptional Malcev algebra [Formula: see text] is isomorphic to a direct sum [Formula: see text], where [Formula: see text] is a commutative associative algebra. Also, we prove that a Malcev superalgebra [Formula: see text] in [Formula: see text] whose even part [Formula: see text] contains [Formula: see text] is isomorphic to a direct sum [Formula: see text], where [Formula: see text] is a supercommutative associative superalgebra.

Artificial intelligence (AI) offers objective, adaptive tools for skill enhancement in microsurgical training, but evidence is fragmented. This systematic review evaluates AI-enhanced training efficacy compared to traditional methods, focusing on technical performance, learning efficiency, and skill retention. Following PRISMA guidelines, databases (MEDLINE, Embase, Cochrane, IEEE Xplore, Web of Science) were searched from January 2010. Data on study characteristics, AI models, outcomes (time, errors, skill metrics), risk of bias, evidence certainty (GRADE), methodological quality, and reporting quality were extracted and synthesized narratively. From 2,056 records, 13 studies were included, involving 3-50 participants, mostly single-centre with varied designs. AI/ML models, such as Mask R-CNN, YOLOv2, ResNet-50, and other convolutional neural networks, were primarily used for assessment or guidance/coaching, focusing on instrument tracking (30.8%), motion analysis (23.1%), tutoring/guidance (15.4% each). Median accuracy 83.8% (IQR 78.4-88.2%). AI improved technical skills (reduced errors) and learning curves via real-time feedback, with promising retention outcomes. RoB high; evidence certainty very low. Reporting quality high/moderate, external validation poor. AI enhances microsurgical training with objective metrics and personalised feedback, showing promising technical advantages in simulations. However, heterogeneous, low-quality evidence limits generalisability. Future research needs multi-centre RCTs, standardised outcomes, external validation, and ethical considerations for clinical translation.

Moray (Cusco, Peru) represents one of the most sophisticated examples of Inca agricultural engineering, where architecture, environmental management, and constructive systems converge to generate controlled microclimates for agricultural experimentation. Recognized as an important archaeological heritage site, Moray provides valuable insight into ancestral Andean strategies for adapting agriculture to complex high-altitude environments. However, the site is increasingly exposed to environmental pressures associated with climatic variability, soil erosion, structural collapses, and tourism intensity. This study aims to analyze the relationship between microclimates, geometric design, and constructive sustainability of the Moray archaeological complex through integrated spatial, functional, and constructive analyses, supported by digital tools such as Google Earth Pro, AutoCAD 2023, SketchUp 2023, and environmental simulations developed by Andrew Marsh. The research examines the geometric configuration of the circular terraces, which present radii between 45 and 65 m, heights ranging from 3 to 5 m, and slope variations between 14% and 48%, generating temperature gradients of 12–15 °C between upper and lower levels. These conditions enabled the Incas to experiment with and adapt diverse ecological species across different thermal zones. The study also evaluates the irrigation and infiltration systems composed of gravel, sand, and stone layers that ensured soil stability and moisture regulation. Climate data from SENAMHI (2019–2024) indicate that Moray is located in a semi-arid meso-Andean environment, reinforcing its interpretation as an ancestral environmental laboratory. The results demonstrate Inca mastery in integrating environmental design, hydrological engineering, and agricultural experimentation while also identifying current conservation challenges related to erosion processes, structural deterioration, and tourism pressure. This research contributes to understanding Moray as a climate-sensitive heritage system, offering insights relevant to contemporary strategies for sustainable agriculture, climate adaptation, and heritage conservation in Andean regions.

ABSTRACT Laboratories are essential to engineering education, yet traditional setups often face challenges such as limited accessibility for students with disabilities and constraints imposed by events like pandemics. To address these issues, this paper introduces an innovative remote laboratory platform to overcome these barriers. The platform integrates specialized control boards and hardware with advanced laboratory management software, offering a versatile and accessible educational tool. Its fault detection and control system ensures reliable operation while safeguarding both equipment and users from potential risks. The platform supports both in‐person and remote access to laboratory resources, providing a cost‐effective and high‐precision alternative to conventional labs. Furthermore, it can be easily and swiftly implemented in existing traditional labs at a reasonable cost. This remote laboratory system enhances educational equity and operational flexibility, with practical applications in teaching, research, and industrial settings.

This study explores the influence of precursor nature on the structural and mechanical characteristics of hydroxyapatite-yttria partially stabilized zirconia (HAp-YSZ) nanocomposites designed for biomedical applications. Precursor powders for obtaining these ceramic composites were synthesized via wet coprecipitation, using different calcium phosphate precursors: dibasic and monobasic ammonium phosphates for hydroxyapatite, and zirconyl chloride with yttrium acetate for YSZ. The dried precipitated powders were thermally treated at 600 °C and 800 °C and characterized by X-ray diffraction (XRD), thermal analysis (DTA-TG), transmission electron microscopy (TEM), and BET surface area measurements. The nanocomposites containing 70-90 wt.% HAp and 10-30 wt.% YSZ were sintered between 1000 °C and 1400 °C. Microstructural and physical properties were evaluated using scanning electron microscopy (SEM), open porosity, and compressive strength testing. Results revealed that precursor type and calcination temperature strongly affected crystallinity, particle size, and phase composition, influencing both porosity and mechanical strength of the final materials. An optimal sintering temperature of approximately 1200 °C was identified, balancing densification and phase stability. The findings demonstrate that controlling precursor chemistry and heat treatment enables fine-tuning of nanocomposite structure and performance, supporting their potential as bioactive, mechanically enhanced ceramics for orthopedic implant applications.

Solar-driven conversion of CO2 and biomass-derived alcohols offers a promising strategy for mitigating CO2 emissions and providing value-added chemicals. However, due to complex reaction pathways and sluggish C-H bond activation, it remains challenging to attain efficient photocatalytic CO2 reduction to CH4 alongside the valorization of biomass-derived alcohols. Herein, we report a judicious design to construct asymmetric dual-metal catalytic centers via anchoring semiconductor nanoclusters in vacancy-rich MOFs, achieving tandem CO2-to-CH4 photoreduction with C-H oxidation. Through loading Fe2O3 clusters in O-vacancy-rich Mil-125(Ti)-NH2, interfacial O-vacancy renders a newly formed Fe-O bond as an atomic-level electron transfer pathway toward Z-scheme construction. Furthermore, the interfacial vacancies trigger intimate interactions and modulate the d-band center to form asymmetric Ti-Fe dual-metal sites. Remarkably, a superior selectivity of 87.0% toward CH4 production coupled with 100% selectivity for benzyl alcohol-to-benzaldehyde oxidation is realized. Mechanistic investigations reveal that the heteronuclear Ti-Fe units synergistically facilitate the hydrogenation of *CO to *CHO through d-p hybridization, thereby enabling CH4 synthesis to be thermodynamically favorable, while photogenerated holes from Fe2O3 preferentially oxidize the α-C-H bond of benzyl alcohol. Further supported via another MOFs-based catalyst, this work enlightens a general design of building asymmetric dual-metal interfaces to concurrently realize selective CO2 photomethanation coupled with C-H activation.

Precise surface engineering of gold nanoclusters (Au NCs) is critical for bioimaging. However, conventional methods often compromise the structural integrity of Au NCs or result in uncontrolled ligand distribution due to rapid reaction kinetics and etching side reactions. Herein, we develop an interphase-assisted ligand exchange method, which leverages mass transfer resistance between immiscible phases to precisely control the mass transfer of ligands. This approach significantly suppresses side etching while maintaining exchange kinetics, enabling high-yield, structure-preserving substitution of diverse thiol ligands on Au NCs. By systematically introducing p-aminothiophenol (p-ATP) ligands, we reveal that increasing p-ATP density on Au25 NCs shifts their biodistribution in female BALB/c nude mice from liver and spleen to kidneys, highlighting the tunability of organ targeting through ligand engineering. The methodology developed here not only boosts the surface engineering to molecular resolution, but also provides a platform for studying ligand effects of metal nanomaterials at the same resolution.